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Abstract Objectives This study aimed to conduct a comprehensive systematic literature review and meta-analysis to assess the risk and outcomes of coronavirus disease 2019 (COVID-19) in patients with rheumatoid arthritis. Methods A systematic search was performed across four electronic databases. The quality of the studies was assessed using the Newcastle‒Ottawa quality assessment scale and the Joanna Briggs Institute critical appraisal checklist. Statistical analyses were conducted using STATA 14 software. Results A total of 62 studies were included in the analysis. First, the meta-analysis revealed the following prevalence rates among rheumatoid arthritis patients: COVID-19, 11%; severe COVID-19, 18%; COVID-19-related hospitalization, 29%; admission to the intensive care unit (ICU) due to COVID-19, 10%; and death from COVID-19, 8%. Second, rheumatoid arthritis was associated with an increased risk of COVID-19 infection (OR 1.045(0.969–1.122), p = 0.006), COVID-19-related hospitalization (OR 1.319(1.055–1.584), p = 0.006), admission to the ICU due to COVID-19 (OR 1.498(1.145–1.850), p = 0.002), and death from COVID-19 (OR 1.377(1.168–1.587), p = 0.001). Third, no statistically significant association was found between rheumatoid arthritis and severe COVID-19 (OR 1.354(1.002–1.706), p = 0.135). Conclusions Rheumatoid arthritis patients have a significantly greater risk of COVID-19 infection, hospitalization, ICU admission, and death than individuals without rheumatoid arthritis. However, rheumatoid arthritis did not show a significant association with the risk of severe COVID-19. These findings underscore the need for tailored management strategies and vigilant monitoring of COVID-19 outcomes in rheumatoid arthritis patients. Systematic Review Registration The study has been registered on PROSPERO [ https://www.crd.york.ac.uk/PROSPERO/ ], and the registration number is CRD42024528119.

Abstract Traffic sign detection is critical for autonomous driving technology. However, accurately detecting traffic signs in complex traffic environments remains challenge despite the widespread use of one‐stage detection algorithms known for their real‐time processing capabilities. In this paper, the authors propose a traffic sign detection method based on YOLO v8. Specifically, this study introduces the Space‐to‐Depth (SPD) module to address missed detections caused by multi‐scale variations of traffic signs in traffic scenes. The SPD module compresses spatial information into depth channels, expanding the receptive field and enhancing the detection capabilities for objects of varying sizes. Furthermore, to address missed detections caused by complex backgrounds such as trees, this paper employs the Select Kernel attention mechanism. This mechanism enables the model to dynamically adjust its focus and more effectively concentrate on key features. Additionally, considering the uneven distribution of training data, the authors adopted the WIoUv3 loss function, which optimizes loss calculation through a weighted approach, thereby improving the model's detection performance across various sizes and frequencies of instances. The proposed methods were validated on the CCTSDB and TT100K datasets. Experimental results demonstrate that the authors’ method achieves substantial improvements of 3.2% and 5.1% on the mAP50 metric compared to YOLOv8s, while maintaining high detection speed, significantly enhancing the overall performance of the detection system. The code for this paper is located at https://github.com/dusongjie/TSD‐YOLO‐Small‐Traffic‐Sign‐Detection‐Based‐on‐Improved‐YOLO‐v8

BackgroundThis study aims to investigate the connection between serum lipids, per- and polyfluoroalkyl substances (PFAS), and age-related macular degeneration (AMD) risk and assess whether serum lipids mediate the association between PFAS and AMD.Methods1605 participants were enrolled from NHANES 2005-2008. Four serum PFAS levels with high detective rates, including perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorohexane sulfonic acid (PFHxS) were examined. Restricted cubic spline analysis (RCS) and weighted quantile sum (WQS) analysis were employed to detect nonlinear and mixed exposure effects.ResultsPFOS level was associated with any AMD (OR, 1.54; 95% CI, 1.12 to 2.11; P = 0.011), early AMD (OR, 1.43; 95% CI, 1.06 to 1.95; P = 0.024), and late AMD (OR, 3.35; 95% CI, 1.55 to 7.23; P = 0.004) risk. PFHxS (OR, 1.72; 95% CI, 1.01 to 2.93; P = 0.045) and PFOA (OR, 2.10; 95% CI, 1.21 to 3.63; P = 0.011) levels were associated with late AMD risk. The RCS showed a nonlinear connection between PFOS exposure and AMD risk (P nonlinear = 0.006). WQS analysis indicated a positive relationship between mixed PFAS exposure and AMD risk (OR, 1.34; 95% CI, 1.03 to 1.75; P = 0.030). Serum total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol were associated with AMD risk (ORTC, 1.005; 95% CI, 1.001 to 1.009; P = 0.026. ORHDL, 1.028; 95% CI, 1.014 to 1.042; PConclusionsPFOS exposure was connected with AMD risk and serum TC and HDL mediated this relationship.

BackgroundLipids are vital biomolecules involved in the formation of various biofilms. Seizures can cause changes in lipid metabolism in the brain. In-depth studies at multiple levels are urgently needed to elucidate lipid composition, distribution, and metabolic pathways in the brain after seizure.MethodsIn this research, a cutting-edge targeted quantitative lipidomics study was conducted on the hippocampal tissues of six rats with temporal lobe epilepsy and six normal rats. Accurate lipid quantification based on linear equations was calculated using an internal standard. The lipids were quantitatively and qualitatively analyzed by ultra-high performance liquid chromatography (UPLC) and mass spectrometry (MS).ResultsA total of 21 lipid classes were identified. Among them, the most abundant were triacylglycerol (TG), phosphatidyl ethanolamine (PE-P), and fatty acids (FA). Cholesteryl ester (ChE) exhibits the most considerable difference between the normal and epileptic samples. ChE was found to be the most significantly upregulated lipid, while FA was observed to be the most significantly downregulated lipid.ConclusionBased on the absolute quantitative analysis of lipids in rat hippocampal specimens, the contents and change trends of different lipids were observed. Upregulation of ChE and dihydroceramide (DHCer) was observed, and an analysis of the distribution changes elucidated the causes and possible molecular mechanisms of lipid accumulation in temporal lobe epilepsy. The results and methods described provide a comprehensive analysis of lipid metabolism in temporal lobe epilepsy and a new therapeutic target for the treatment of epilepsy.

Objectives To examine the ocular biometric parameters and predict the annual growth rate of the physiological axial length (AL) in Chinese preschool children aged 4–6 years old.Methods This retrospective cross-sectional study included 1090 kindergarten students (1090 right eyes) between the ages of 4 and 6 years from Pinggu and Chaoyang District, Beijing. Dioptre values were ascertained following cycloplegic autorefraction. Predicted AL was obtained through the application of the Gaussian process regression model as an optimisation technique. Subsequently, the annual growth rate of physiological AL for non-myopic preschool children (n=1061) was computed via the backward difference method.Results In total, 85.4% of preschool children (931 individuals) had hyperopic refractive status in the 4–6 years age group, while only 2.7% (29 individuals) showed myopia. Boys had longer AL, larger AL-to-corneal radius ratio, deeper anterior chamber depth and lower lens power. The average physiological axial growth for boys and girls ranged from 0.050 mm/year to 0.165 mm/year and 0.063 mm/year to 0.168 mm/year, respectively. As age increased, the physiological AL growth in non-myopic cases decreased. Additionally, as hyperopic spherical equivalent refraction error lessened, the physiological AL growth component slowed down.Conclusions In preschool children, refractive development predominantly exhibits mild hyperopia. The concept of physiological AL provides valuable insights into the complexities of ocular development.

BackgroundSeveral nutrients have been found to be associated with the prevalence of myopia, and the role of dietary patterns in influencing myopia risk has recently garnered significant attention. We aim to explore the relationship between the Dietary Inflammatory Index (DII) and refractive status in adolescents.MethodsData from 7,331 participants were analyzed from the 2005–2008 US National Health and Nutrition Examination Survey (NHANES). Smooth curve fitting and linear regression analysis were used to explore both non-linear and linear relationships between DII and spherical equivalent refraction (SER). The threshold effect of DII on SER was analyzed using a two-piecewise linear regression model.ResultsDII was connected with a lower SER, indicating a shift toward myopia (β: –0.0586; 95% CI: −0.1109 to −0.0063; p 0.05). However, for DII values ≥0.81, a significant association with lower SER was observed (β: –0.1197; 95% CI: −1.1722 to −0.0672; p

Abstract The limited of texture details information in low‐resolution facial or eye images presents a challenge for gaze estimation. To address this, FSKT‐GE (feature maps similarity knowledge transfer for low‐resolution gaze estimation) is proposed, a gaze estimation framework consisting of both a high resolution (HR) network and low resolution (LR) network with the identical structure. Rather than mere feature imitation, this issue is addressed by assessing the cosine similarity of feature layers, emphasizing the distribution similarity between the HR and LR networks. This enables the LR network to acquire richer knowledge. This framework utilizes a combination loss function, incorporating cosine similarity measurement, soft loss based on probability distribution difference and gaze direction output, along with a hard loss from the LR network output layer. This approach on low‐resolution datasets derived from Gaze360 and RT‐Gene datasets is validated, demonstrating excellent performance in low‐resolution gaze estimation. Evaluations on low‐resolution images obtained through 2×, 4×, and 8× down‐sampling are conducted on two datasets. On the Gaze360 dataset, the lowest mean angular errors of 10.97°, 11.22°, and 13.61° were achieved, while on the RT‐Gene dataset, the lowest mean angular errors of 6.73°, 6.83°, and 7.75° were obtained.

Objective: Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs). Methods: Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result. Results: 4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718. Conclusion: SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

Background: Stroke often damages the basal ganglia, leading to atypical and transient aphasia, indicating that post-stroke basal ganglia aphasia (PSBGA) may be related to different anatomical structural damage and functional remodeling rehabilitation mechanisms. The basal ganglia contain dense white matter tracts (WMTs). Hence, damage to the functional tract may be an essential anatomical structural basis for the development of PSBGA. Methods: We first analyzed the clinical characteristics of PSBGA in 28 patients and 15 healthy controls (HCs) using the Western Aphasia Battery and neuropsychological test batteries. Moreover, we investigated white matter injury during the acute stage using diffusion magnetic resonance imaging scans for differential tractography. Finally, we used multiple regression models in correlation tractography to analyze the relationship between various language functions and quantitative anisotropy (QA) of WMTs. Results: Compared with HCs, patients with PSBGA showed lower scores for fluency, comprehension (auditory word recognition and sequential commands), naming (object naming and word fluency), reading comprehension of sentences, Mini-Mental State Examination, and Montreal Cognitive Assessment, along with increased scores in Hamilton Anxiety Scale-17 and Hamilton Depression Scale-17 within 7 days after stroke onset (P < 0.05). Differential tractography revealed that patients with PSBGA had damaged fibers, including in the body fibers of the corpus callosum, left cingulum bundles, left parietal aslant tracts, bilateral superior longitudinal fasciculus II, bilateral thalamic radiation tracts, left fornix, corpus callosum tapetum, and forceps major, compared with HCs (FDR < 0.02). Correlation tractography highlighted that better comprehension was correlated with a higher QA of the left inferior fronto-occipital fasciculus (IFOF), corpus callosum forceps minor, and left extreme capsule (FDR < 0.0083). Naming was positively associated with the QA of the left IFOF, forceps minor, left arcuate fasciculus, and uncinate fasciculus (UF) (FDR < 0.0083). Word fluency of naming was also positively associated with the QA of the forceps minor, left IFOF, and thalamic radiation tracts (FDR < 0.0083). Furthermore, reading was positively correlated with the QA of the forceps minor, left IFOF, and UF (FDR < 0.0083). Conclusion: PSBGA is primarily characterized by significantly impaired word fluency of naming and preserved repetition abilities, as well as emotional and cognitive dysfunction. Damaged limbic pathways, dorsally located tracts in the left hemisphere, and left basal ganglia pathways are involved in PSBGA pathogenesis. The results of connectometry analysis further refine the current functional localization model of higher-order neural networks associated with language functions.

Commensal microbial–host interaction is crucial for host metabolism, growth, development, and immunity. However, research on microbial–host immunity in large animal models has been limited. This study was conducted to investigate the effects of the commensal microbiota on immune function in two model groups: germ-free (GF) and specific-pathogen-free (SPF) piglets. The weight and organ index of the spleen of the GF piglet were larger than those in the SPF piglet (P < 0.05). The histological structure of the red pulp area and mean area of germinal centers were larger in the SPF piglet than in the GF piglet (P < 0.05), whereas the areas of staining of B cells and T cells in the spleen and mesenteric lymph nodes (MLNs) were lower in the GF piglet (P < 0.05). We identified immune-related genes in the spleen and MLNs using RNA sequencing, and used real-time quantitative PCR to analyze the expression of core genes identified in gene set enrichment analysis. The expression levels of genes in the transforming growth factor-β/SMAD3 signaling pathway, Toll-like receptor 2/MyD88/nuclear factor-κB signaling pathway, and pro-inflammatory factor genes IL-6 and TNF-α in the spleen and MLNs were higher in the SPF piglet and in splenic lymphocytes compared with those in the GF and control group, respectively, under treatment with acetic acid, propionic acid, butyric acid, lipopolysaccharide (LPS), or concanavalin A (ConA). The abundances of plasma cells, CD8++ T cells, follicular helper T cells, and resting natural killer cells in the spleen and MLNs were significantly greater in the SPF piglet than in the GF piglet (P < 0.05). In conclusion, the commensal microbiota influenced the immune tissue structure, abundances of immune cells, and expression of immune-related pathways, indicating the importance of the commensal microbiota for spleen and MLNs development and function. In our study, GF piglet was used as the research model, eliminating the interference of microbiota in the experiment, and providing a suitable and efficient large animal research model for exploring the mechanism of “microbial-host” interactions.

The optimal load capacity provides sustainable production and high quality in 'Fuji' apple production. Determining optimal crop loads with simple and affordable thinning methods is critical for apple. We set different load capacities on different apple tree rootstocks and evaluated their effects on tree development, fruit yield and quality. The optimal load capacity for vigorous stock, dwarfing rootstock, and dwarfing interstock were 240, 90, and 100 for apples above 80 mm in diameter, respectively. To explore the optimal strategies of diverse fruit thinning agents (carbaryl, 6-BA, NAA, and Metamitron) used in the 'Fuji' apple tree, we assess from thinning agents' types, varying spraying concentrations, and application time in the most widely used dwarfing interstock. The results showed that the best use of fruit thinning agents is 2,000 mg/L carbaryl at 10 d after flowering could significantly reduce fruit set rate and improve the fruit quality in the dwarfing interstock. During the research, only the fruit thinner NAA treatment suppresses fruit development. Thus, we performed the transcriptome analysis on the NAA-treated and control fruits at 60 (FS1), 90 (FS2), 120 (FS3), and 150 (FS4) days after flowering stages to investigate the potential transcriptional regulations of NAA on fruit development and ripening. Transcriptome results showed that genes related to fruit expansion (expansin A15, expansin B3), phytohormone-related genes (HVA22C, PRE1, AHP1, etc.), fruit coloring-related genes (PAL), and many ripening-related transcription factors (EIN3, ERF, ARF, etc.) might be regulated by exogenous NAA. Our results provide an important reference for the sustainable production and optimal use of fruit thinning agents.

The global hospitality industry is fast-turning sustainable and environmentally friendly. Behaviour-driven energy conservation is an emerging green hotel operation strategy to support this change. The long-stay accommodation services have gained momentum in the hospitality sector since the COVID-19 pandemic. However, the characteristics of long-stay hotel guests are often overlooked in sustainable interventions. Based on an empirical survey in China, this study aims to explore the factors driving energy-saving behaviours of long-stay hotel guests and to compare their effects on guests for different visiting purposes (leisure, business, and extended-stay resident). The analysis indicates that attitude, personal norm and place attachment present a direct contribution to energy-saving behaviour. Besides, the results support that attitude and personal norm connect environmental values and energy-saving behaviour. Both altruistic and biospheric values have positive effects, while egoistic values seem to play a negative role. Biospheric values have stronger impact on attitude and personal norm of business guests. Place attachment has a stronger influence on extended-stay residents while its contribution to energy-saving behaviours of business guests is smaller than other guests. Besides, leisure guests are more sensitive to moral obligations. This research sheds novel lights on the psychological perspectives of the observed heterogeneity of energy-saving behaviours of hotel guests with different visiting purposes. The findings provide hotel operators with a novel theoretical reference for targeted energy-saving interventions to promote energy-saving actions of long-term hotel guests. The study, therefore, can contribute to sustainable tourism policymaking and behaviour-driven hotel energy management.

Under the combined influences of climate and vegetation change, land–atmosphere interactions have enhanced, and precipitation recycling is an important part of this. Previous studies of the precipitation recycling process have focused on calculating the precipitation recycling rate (PRR) and analyzing the influencing factors. However, the climate-driven and vegetation-induced precipitation recycling process variations were not quantified. This study has systematically examined the precipitation recycling process in a typical arid region using the Eltahir and Bras model, random forest algorithm, and partial least-squares structural equation modeling. During 1982–2018, the leaf area index (LAI) and evapotranspiration (ET) rate both increased significantly, with growth rates of 0.06 m2m−2/decade and 13.99 mm/decade, respectively. At the same time, the average PRR in Xinjiang was 13.92% and experienced significant growth at a rate of 1.28%/decade. The climate-driven and vegetation-induced PRR variations were quantified, which contributed 79.12% and 20.88%, respectively. In addition, the positive effects of both of these on PRR variations through ET did not increase with the increase in ET, but rather decreased sharply and then stabilized. This study can provide favorable theoretical support for mitigating the contradiction in water use and balancing economic development and ecological security by quantifying the regulation of precipitation by vegetation.

Abstract Background To investigate the control effect on the axial length elongation using corneal refractive therapy (CRT) with different optical zone diameters (BOZDs). We also sought to compare the difference in higher-order aberrations (HOAs), treatment zone (TZ) size and Zernike defocus coefficient with different BOZDs and seek the optimal parameter for predicting axial elongation. Methods This prospective cohort study included 7- to 14-year-olds fitted with orthokeratology (ortho-K) lenses of 5-mm (5-mm group) or 6-mm BOZD (6-mm group). Axial length (AL), corneal topography, HOAs and Zernike defocus coefficient were obtained at baseline, and 1, 3, 6, 9 and 12 months follow-up visits. Multivariate regression analyses were used to explore the association between AL change and ocular biometric parameters. Receiver operating characteristic (ROC) curve analysis was used to determine the best diagnostic value for AL change in ocular biometric parameters. Results In total, 301 participants completed the one-year follow-up. The mean AL change in the 5-mm group (0.13 ± 0.18 mm) was less than that in the 6-mm group (0.27 ± 0.15 mm) at the 12 months visit. The TZ size and decentration were smaller, while the Zernike defocus coefficient and HOAs were higher in the 5-mm group (all P

Moyamoya disease (MMD) is a chronic cerebrovascular disease of unknown etiology characterized by progressive stenosis or occlusion of unilateral or bilateral terminal internal carotid arteries, accompanied by the abnormal cerebrovascular network (moyamoya vessels) at the skull base. In recent years, with the continuous improvement of medical technology and research level, the detection rate of peri-operative cognitive changes in MMD patients has gradually increased. This paper reviews the clinical manifestations, pathogenesis and influencing factors of peri-operative cognitive changes in patients with ischemic MMD, and focuses on the electroencephalogram(EEG) and magnetic resonance imaging(MRI) features associated with cognitive impairment in patients with MMD.

Abstract Introduction There are only four sizes of implantable collamer lens (ICL) available for selection, which cannot completely fit all patients as a result of the discontinuity of ICL sizes. Sizing an optimal ICL and predicting postoperative vault are still unresolved problems. This study aimed to develop and validate innovative data-level data-balancing machine learning-based models for predicting ICL size and postoperative vault. Methods The patients were randomly assigned to training and internal validation sets in a 4:1 ratio. Feature selection was performed using analysis of variance (ANOVA) and Kruskal–Wallis feature importance methods. Traditional linear regression model and machine learning-based models were used. The accuracy of models was assessed using the area under the curve (AUC) and confusion matrix. Results A total of 564 patients (1127 eyes) were eligible for this study, consisting of 808 eyes in the training set, 202 eyes in the internal validation set, and 117 eyes in the external validation set. Compared with the traditional linear regression method, the machine learning model bagging tree showed the best performance for ICL size selection, with an accuracy of 84.5% (95% confidence interval (CI) 83.2–85.8%), and the AUC ranged from 0.88 to 0.99; the prediction accuracy of 12.1 mm and 13.7 mm ICL sizes was improved by 49% and 59%, respectively. The bagging tree model achieved the best accuracy [90.2%, (95% CI 88.9–91.5%)] for predicting the postoperative vault, and the AUC ranged from 0.90 to 0.94. The prediction accuracies of internal and external validation dataset for ICL sizing were 82.2% (95% CI 81.1–83.3%) and 82.1% (95% CI 81.1–83.1%), respectively. Conclusions The innovative data-level data balancing-based machine learning model can be used to predict ICL size and postoperative vault more accurately, which can assist surgeons in choosing optimal ICL size, thus reducing risks of postoperative complications and secondary surgery.

Background and purposeEarly blood–brain barrier (BBB) disruption in patients with acute ischemic stroke (AIS) can be detected on perfusion computed tomography (PCT) images before undergoing reperfusion therapy. In this study, we aimed to determine whether early disruption of the BBB predicts intracranial hemorrhage transformation (HT) in patients with AIS undergoing endovascular therapy and further identify factors influencing BBB disruption.MethodsWe retrospectively analyzed general clinical and imaging data derived from 159 consecutive patients with acute anterior circulation stroke who were admitted to the Department of Neurology of the First Hospital of Jilin University, and who underwent endovascular treatment between January 1, 2021, and March 31, 2023. We evaluated the relationship between BBB destruction and intracranial HT before endovascular reperfusion therapy and examined the risk factors for early BBB destruction.ResultsA total of 159 patients with assessable BBB leakage were included. The median (interquartile range, IQR) age was 63 (54–70) years, 108 (67.9%) patients were male, and the median baseline National Institutes of Health Stroke Scale (NHISS) score was 12 (10–15). Follow-up non-contrast computed tomography (NCCT) detected HT in 63 patients. After logistic regression modeling adjustment, we found that BBB leakage in the true leakage area was slightly more than 2-fold risk of HT (odds ratio [OR], 2.01; 95% confidence interval [CI] 1.02–3.92). Heart rate was also associated with HT (OR, 1.03, 95% CI, 1.00–1.05). High Blood–brain barrier permeability (BBBP) in the true leakage area was positively correlated with infarct core volume (OR, 1.03; 95% CI, 1.01–1.05).ConclusionEarly BBB destruction before endovascular reperfusion therapy was associated with HT, whereas high BBBP correlated positively with infarct core volume.

Pigs are widely used as animal models in various studies related to humans. The interaction between the gut microbiota and the host has significant effects on the host’s health and disease status. However, although there have been many studies investigating the pig gut microbiota, the findings have been inconsistent due to variations in rearing conditions. Interactions between the gut microbiota and host have not been fully explored in pigs. Specific pathogen-free (SPF) pigs are ideal non-primate large animals to study the interactions between the gut microbiota and the host. In this study, we performed high-throughput sequencing analysis of the gut microbiota and the gut tissue transcriptome of six SPF pigs to provide a systematic understanding of the composition, function, and spatial distribution of gut microbiota in SPF pigs. We identified significant differences in microbial diversity and functionality among different gastrointestinal tract sites. Metagenomics data analysis revealed significant differences in alpha diversity and beta diversity of microbiota in different gastrointestinal sites of SPF pigs. Additionally, transcriptomic data indicated significant differences in gene expression as well as KEGG and GO functional enrichment between the small intestine and large intestine. Furthermore, by combining microbial metagenomics and host transcriptomics analyses, specific correlations were found between gut microbiota and host genes. These included a negative correlation between the TCN1 gene and Prevotella dentalis, possibly related to bacterial metabolic pathways involving vitamin B12, and a positive correlation between the BDH1 gene and Roseburia hominis, possibly because both are involved in fatty acid metabolism. These findings lay the groundwork for further exploration of the co-evolution between the microbiota and the host, specifically in relation to nutrition, metabolism, and immunity. In conclusion, we have elucidated the diversity of the gut microbiota in SPF pigs and conducted a detailed investigation into the interactions between the gut microbiota and host gene expression. These results contribute to our understanding of the intricate dynamics between the gut microbiota and the host, offering important references for advancements in life science research, bioproduct production, and sustainable development in animal husbandry.

The flower coloration of Brassica crops possesses significant application and economic value, making it a research hotspot in the field of genetics and breeding. In recent years, great progress has been made in the research on color variation and creation of Brassica crops. However, the underlying molecular mechanisms and evolutional processes of flower colors are poorly understood. In this paper, we present a comprehensive overview of the mechanism of flower color formation in plants, emphasizing the molecular basis and regulation mechanism of flavonoids and carotenoids. By summarizing the recent advances on the genetic mechanism of flower color formation and regulation in Brassica crops, it is clearly found that carotenoids and anthocyanins are major pigments for flower color diversity of Brassica crops. Meantime, we also explore the relationship between the emergence of white flowers and the genetic evolution of Brassica chromosomes, and analyze the innovation and multiple utilization of Brassica crops with colorful flowers. This review aims to provide theoretical support for genetic improvements in flower color, enhancing the economic value and aesthetic appeal of Brassica crops.

Subway station fires frequently result in massive casualties, economic losses and even social panic due to the massive passenger flow, semiconfined space and limited conditions for escape and smoke emissions. The combination of different states of fire hazard factors increases the uncertainty and complexity of the evolution path of subway station fires and causes difficulty in assessing fire risk. Traditional methods cannot describe the development process of subway station fires, and thus, cannot assess fire risk under different fire scenarios. To realise scenario-based fire risk assessment, the elements that correspond to each scenario state during fire development in subway stations are identified in this study to explore the intrinsic driving force of fire evolution. Accordingly, a fire scenario evolution model of subway stations is constructed. Then, a Bayesian network is adopted to construct a scenario evolution probability calculation model for calculating the occurrence probability of each scenario state during subway station fire development and identifying critical scenario elements that promote fire evolution. Xi’an subway station system is used as a case to illustrate the application of Bayesian network-based scenario evolution model, providing a practical management tool for fire safety managers. The method adopted in this study enables managers to predict fire risk in each scenario and understand the evolution path of subway station fire, supporting the establishment of fire response strategies based on “scenario–response” planning.