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Abstract Flue-curing and redrying are important processing stages before tobacco fermentation, closely linked to microbial actions that influence the fermentation process. It is necessary to investigate the effects of flue-curing and redrying on diversity and succession of tobacco fungal communities. It was shown that a total of 9 phyla, 33 classes, 94 orders, 266 families, 646 genera, and 6,396 amplicon sequence variants (ASVs) were identified in the fungi communities of 36 samples from different processing stages (before flue-curing, after flue-curing, before redrying and after redrying) based on high-throughput sequencing technology. Dominant genera shared by tobacco leaves at different stages were Alternaria and Sampaiozyma. About 80% of fungi in stored tobacco leaves after redrying originated from fresh tobacco leaves before flue-curing, while the rest were primarily enriched in the post-harvest processing environment. After flue-curing, major molds like Aspergillus and Penicillium were notably enriched. The distribution of fungal communities suggested that the flue-curing and redrying had a significant impact on fungal composition. Functional annotation of fungal communities at the guild level exhibited differences during processing stages. Main fungal functional groups were identified. In summary, our study elucidated dynamic changes in the composition of fungal communities and highlighted key stages in mold enrichment during tobacco leaf processing, laying groundwork for mildew prevention and control during tobacco leaf fermentation.

Abstract Enterobacter ludwigii has been proven by numerous studies to be an effective plant growth promoter. Enterobacter ludwigii T977 was isolated from leaves of Nicotiana tabacum L. Yunyan 97 which showing high starch degrading ability. The optimal fermentation carbon source of strain T977 was starch, with optimal starch concentration as 2.5 g/L, and the most suitable fermentation nitrogen source for the strain T977 was ammonium acetate, with optimal concentration as 0.25 g/L. The spaying treatment of strain T977 could reduce the starch content of upper leaves from 3.77% to 1.43%, the total sugar and reducing sugar decreased slightly, the starch content of middle leaves decreased from 5.63% to 3.18%, the content of total sugar and reducing sugar increased in middle leaves, and the other chemical components were in the appropriate range. Here, we reported 4.77 MB whole genome of a starch-degrading E. ludwigii T977 that encodes 4501 proteins, 11 α-amylases in GH13 family were identified, and the amylase (GM000159) with signal peptide may play important role in degradation of starch in tobacco leaves. Our study may provide an effective microbiological mean for reducing starch content in tobacco leaves.

ABSTRACT To explore the factors influencing the occurrence of bacterial wilt, the differences in the physicochemical properties, microbial community composition and function between rhizosphere soil of tobacco plants with bacterial wilt and healthy plants in the tobacco planting area of Fuzhou City, Jiangxi Province were analyzed and compared. The results showed that the rhizosphere soil of diseased tobacco exhibited significantly reduced levels of exchangeable potassium, water-soluble potassium, nitrate nitrogen, total nitrogen and pH, in comparison to the rhizosphere soil of healthy plants. Conversely, the available phosphorus content of the rhizosphere soil of diseased tobacco was significantly increased. The amount of Ralstonia solanacearum in soil was negatively correlated with pH, nitrate nitrogen and total nitrogen, and positively correlated with exchangeable potassium and water-soluble potassium. A total of 43 genera were significantly different between the two groups of rhizosphere soil, of which 24 genera were enriched in the rhizosphere of healthy plants, including Ideonella, Rhizophagus, Rhizobacter, Altererythrobacter and Ignavibacterium associated with plant disease resistance, Thermodesulfovibrio, Syntrophorhabdus, Syntrophus, Chlorobium, Hydrogenophaga and Limnohabitans associated with soil sulfur metabolism, as well as Ignavibacterium, Ideonella, Derxia and Azohydromonas associated with soil nitrogen cycling. Kyoto Encyclopedia of Genes and Genomes functional analysis of the unigenes obtained by metagenomic sequencing also showed that the differential unigenes were significantly enriched in the sulfur metabolism pathway. In addition, the rhizosphere soil of diseased tobacco plants exhibited a higher abundance of antibiotic-producing actinomycetes and an increased load of antibiotic resistance genes compared to that of healthy plants. In general, lower pH value, less content of nitrate nitrogen and total nitrogen, and more content of exchangeable potassium and water-soluble potassium could contribute to onset of bacterial wilt. Twenty-four genera, including Ideonella and Rhizophagus, may construct a healthy microecological network in the rhizosphere of tobacco plants. All these factors may interact with each other to control the development of bacterial wilt. This complicated interaction network needs to be explored further.IMPORTANCEPrevious studies have mainly focused on the differences in microbial species composition between healthy and diseased soils, but the differences in microbial community functions between two types of soil have not been well characterized. In this study, soil samples in diseased and healthy plant rhizospheres were collected for physicochemical property testing and metagenomic sequencing. We focused on analyzing the differences in physicochemical properties and microbial community functions between these soils, as well as the correlation between these factors and pathogen content. The results of this study provide a theoretical basis for further understanding the occurrence of tobacco bacterial wilt in the field.

Objective To investigate the associations of the oral microbiome status with diabetes characteristics in elderly patients with type 2 diabetes mellitus.Methods A questionnaire was used to assess age, sex, smoking status, drinking status, flossing frequency, T2DM duration and complications, and a blood test was used to determine the glycated haemoglobin (HbA1c) level. Sequencing of the V3-V4 region of the 16S rRNA gene from saliva samples was used to analyze the oral microbiome.Results Differential analysis revealed that Streptococcus and Weissella were significantly enriched in the late-stage group, and Capnocytophaga was significantly enriched in the early-stage group. Correlation analysis revealed that diabetes duration was positively correlated with the abundance of Streptococcus (r= 0.369, p= 0.007) and negatively correlated with the abundance of Cardiobacterium (r= -0.337, p= 0.014), and the level of HbA1c was not significantly correlated with the oral microbiome. Network analysis suggested that the poor control group had a more complex microbial network than the control group, a pattern that was similar for diabetes duration. In addition, Streptococcus has a low correlation with other microorganisms.Conclusion In elderly individuals, Streptococcus emerges as a potential biomarker linked to diabetes, exhibiting elevated abundance in diabetic patients influenced by disease exposure and limited bacterial interactions.

Abstract The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.

Wound healing is a complex process that is critical for maintaining the barrier function of the skin. However, when a large quantity of microorganisms invade damaged skin for an extended period, they can cause local and systemic inflammatory responses. If left untreated, this condition may lead to chronic infected wounds. Infected wounds significantly escalate wound management costs worldwide and impose a substantial burden on patients and healthcare systems. Recent clinical trial results suggest that the utilization of effective antimicrobial wound dressing could represent the simplest and most cost-effective strategy for treating infected wounds, but there has hitherto been no comprehensive evaluation reported on the efficacy of antimicrobial wound dressings in promoting wound healing. Therefore, this review aims to systematically summarize the various types of antimicrobial wound dressings and the current research on antimicrobial agents, thereby providing new insights for the innovative treatment of infected wounds.

【Objective】The pathogenicity and genomic differences between two Ralstonia solanacearum strains were compared to analyze the key genes leading to the differences, and help the control of tobacco bacterial wilt disease in the future.【Method】Different R. solanacearum strains were isolated and purified from tobacco plants infected with bacterial wilt disease. The infestation tests were conducted to compare pathogenicity differences and the genomes of two strains were obtained by high-throughput sequencing technique to analyze the sequence difference, to explore the key genes affecting the pathogenicity of the strains.【Result】Two strains RsTP2 and RsTY2 were isolated from diseased tobacco plants in Guangchang county and Nanfeng county of Fuzhou, Jiangxi Province. egl gene sequencing results revealed that both strains were sequevar 13 belonged to Asian phylotype I. The infestation test results indicated that strain RsTP2 was more aggressive to Yunyan87 tobacco plants, in contrast to less pathogenicity of strain RsTY2. Draft genome maps of strains RsTP2 and RsTY2 (5.43 MB and 5.23 MB, respectively) were obtained by high-throughput sequencing technique. Then, the gene and protein sequences were compared with relevant databases to obtain gene function and species annotation information. Through analysis, it was found that 52 proteins of RsTY2 and 44 proteins of RsTP2 were different in the alignment with similarity between 50% and 99.88%. Most of the differential proteins were lectin-like protein, followed by cytolysin secretory activator protein and perforin family protein【Conclusion】By comparing the genomic sequences of strains RsTP2 and RsTY2 with different pathogenicity, it was found that there were significant differences in the coding sequences of lectin-like protein, cytolysin secretory activator protein and perforin family protein. These proteins mainly participated in bacterial adhesion, membrane lysis and penetration, and other functions, which were closely related to the ability of bacteria to invade host cells. The differences in these types of coding proteins are speculated to be one of the important factors leading to the differences in pathogenicity in strains RsTP2 and RsTY2.

Abstract Background The aim of this study was to investigate the risk factors of neglected osteochondral fractures in primary acute traumatic patellar dislocation in the pediatric population. Methods A total of 113 patients with primary acute traumatic patellar dislocation for whom coincident osteochondral fractures could not be confirmed by X-ray examination at initial diagnosis between January 2010 and February 2022 were retrospectively analyzed. Medical history, physical examination, and radiographic images were recorded in detail. The greatest dimension of the suprapatellar pouch (SP) effusion on radiograph was measured. Computed tomography and magnetic resonance imaging were used to confirm the presence of neglected osteochondral fractures and measure the fragment size. Potential risk factors were calculated and correlated with reference to the neglected osteochondral fractures and fragment size using multivariate linear regression analysis. Results Weight, walking ability, effusion grade, and SP measurement had a significant correlation with neglected osteochondral fractures in primary acute traumatic patellar dislocation (p = 0.046; p

Abstract During the processing of tobacco leaves, flue-curing and redrying can affect the structure of bacterial community, having an effect on the aging quality of tobacco leaves. In order to characterize the effects of flue-curing and redrying on the bacterial community of tobacco leaves, the bacterial community of samples at different processing stages (before flue-curing, after flue-curing, before redrying and after redrying) was analyzed using Illumina sequencing. A total of 33 phyla, 79 classes, 195 orders, 344 families, 826 genera and 7922 ASVs were obtained from 36 samples. There was no significant difference in the core bacterial groups of tobacco leaf at four processing stages. Proteobacteria dominated at the phylum level. Sphingomonas, Pseudomonas and Methylobacterium were the main genera shared by all samples. The functional prediction by PICRUSt showed an increase in the relative abundance of pathway related to metabolism after flue-curing and pathway related to environmental information processing after redrying. This study, we analyzed the changes of bacterial community and structural composition of tobacco leaves from flue-curing to redrying, and found that flue-curing had a greater effect on the microbial community than redrying. This is conducive for the exploration of microbial resources and improvement of tobacco leaf quality.

Abstract Microorganisms play an important role in the tobacco aging process. Before the aging process, raw tobacco leaves must be threshed and redried. In order to explore the differences of microbial community structure of threshed and redried tobacco leaves from different origins at home and abroad, 14 groups of tobacco leaves from 8 different countries were tested by high-throughput DNA sequencing and microbiology analysis. Then, through amplicon sequence variants (ASV) cluster analysis, Venn diagram and species labeling and other microbial diversity analysis, the dominant bacteria and fungi on the surface of threshed and redried tobacco leaves were obtained. The results showed that there were significant differences in the composition of tobacco bacteria and fungi after threshing and redrying from different geographical areas. The relative abundance of Microbacterium and Sphingomonas in domestic tobacco leaves was significantly higher than that of foreign tobacco leaves. The relative abundance of Pseudomonas in foreign tobacco bacterial colonies was significantly higher than that of domestic tobacco leaves. In terms of fungi, the relative abundance of Aspergillus and Alternaria in domestic tobacco leaves was significantly higher than that of foreign tobacco leaves. Septoria, Sampaiozyma, Cladosporium and Phoma account for significantly higher proportions of foreign tobacco leaves. These microorganisms may be indispensable in aging process to form different flavors of tobacco leaves. It provides an important theoretical basis for the further use of microorganisms to promote tobacco leaf aging.

Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm’s capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes.

Abstract Most metals and alloys suffer from high friction and wear due to their low hardness and lack of self‐lubrication. Although plenty of strategies have been proposed, it is still a long‐standing challenge to achieve diamond‐like wear in metals. Metallic glasses (MGs) are supposed to possess low coefficient of friction (COF) because of their high hardness and fast surface mobility. However, their wear rate is larger than that of diamond‐like materials. Here, this work reports the discovery of Ta‐rich MGs that exhibit diamond‐like wear. This work develops an indentation approach for high‐throughput characterization of crack resistance. By employing deep indentation loading, this work is able to efficiently identify the alloys that exhibit better plasticity and crack resistance according to the differences of indent morphology. With high temperature stability, high hardness, improved plasticity, and crack resistance, the discovered Ta‐based MGs exhibit diamond‐like tribological properties, featured by COF as low as ≈0.05 for diamond ball test and ≈0.15 for steel ball test, and specific wear rate of only ≈10−7 mm3 N−1m−1. The discovery approach and the discovered MGs exemplifie the promise to substantially reduce friction and wear of metals and may unleash the potential of MGs in tribological applications.

Abstract Background Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) allows quantification of biventricular blood flow by flow components and kinetic energy (KE) analyses. However, it remains unclear whether 4D flow parameters can predict cardiopulmonary exercise testing (CPET) as a clinical outcome in repaired tetralogy of Fallot (rTOF). Current study aimed to (1) compare 4D flow CMR parameters in rTOF with age- and gender-matched healthy controls, (2) investigate associations of 4D flow parameters with functional and volumetric right ventricular (RV) remodelling markers, and CPET outcome. Methods Sixty-three rTOF patients (14 paediatric, 49 adult; 30 ± 15 years; 29 M) and 63 age- and gender-matched healthy controls (14 paediatric, 49 adult; 31 ± 15 years) were prospectively recruited at four centers. All underwent cine and 4D flow CMR, and all adults performed standardized CPET same day or within one week of CMR. RV remodelling index was calculated as the ratio of RV to left ventricular (LV) end-diastolic volumes. Four flow components were analyzed: direct flow, retained inflow, delayed ejection flow and residual volume. Additionally, three phasic KE parameters normalized to end-diastolic volume (KEiEDV), were analyzed for both LV and RV: peak systolic, average systolic and peak E-wave. Results In comparisons of rTOF vs. healthy controls, median LV retained inflow (18% vs. 16%, P = 0.005) and median peak E-wave KEiEDV (34.9 µJ/ml vs. 29.2 µJ/ml, P = 0.006) were higher in rTOF; median RV direct flow was lower in rTOF (25% vs. 35%, P

Abstract Background To assess the accuracy and reproducibility of right ventricular (RV) and left ventricular (LV) function and flow measurements in children with repaired tetralogy of Fallot (rTOF) using four-dimensional (4D) flow, compared with conventional two-dimensional (2D) magnetic resonance imaging (MRI) sequences. Methods Thirty pediatric patients with rTOF were retrospectively enrolled to undergo 2D balanced steady-state free precession cine (2D b-SSFP cine), 2D phase contrast (PC), and 4D flow cardiac MRI. LV and RV volumes and flow in the ascending aorta (AAO) and main pulmonary artery (MPA) were quantified. Pearson’s or Spearman’s correlation tests, paired t-tests, the Wilcoxon signed-rank test, Bland–Altman analysis, and intraclass correlation coefficients (ICC) were performed. Results The 4D flow scan time was shorter compared with 2D sequences (P 0.05), and showed strong correlations (r > 0.90, P 0.60, P 0.85). Conclusions RV and LV function and flow quantification in pediatric patients with rTOF using 4D flow MRI can be measured accurately and reproducibly compared to those with conventional 2D sequences.

The development of a versatile antibacterial coating, irrespective of material characteristics, is greatly attractive but still a challenge. In this work, mussel-inspired dopamine-modified sodium alginate (SA-DA) was successfully synthesized as the adhesion layer, and antibacterial coatings on three types of substrates, namely cotton fabric, aluminum sheet, and polyurethane membrane, were constructed through the layer-by-layer (LbL) deposition of polyhexamethylene guanidine and sodium alginate. Among the coated materials, the coated cotton fabric was systematically characterized, and the results showed that it still exhibited ideal hydrophilicity, and its liquid absorption capacity increased with an increase in the coating layers. The growth of Escherichia coli and Staphylococcus aureus was notably inhibited on the coated cotton fabric, and 10 coating bilayers achieved 100% inhibition of bacterial growth within 10 min. Furthermore, an ideal antibacterial ability maintained after 10 cycles of antibacterial trials or 50 washing or soaping cycles. In vitro evaluation of the hemostatic effect indicated that the coated cotton fabric could promote blood clotting by concentrating the components of blood and activating the platelets, and no significant hemolysis and cytotoxicity were observed in the coated cotton fabric. Moreover, the coated aluminum and polyurethane film also displayed an obvious antibacterial effect, which proved that the constructed coating could successfully adhere to the metal and polymer surfaces. Therefore, this work provided a proper way for the progress of a current antibacterial coating tactics for different substrate surfaces.

Flow and phase separation in gas–liquid multiphase pumps is easy to occur, which deteriorates their performance and mixed transportability. Many research achievements have been made in the experiment, CFD simulation and performance improvement of multiphase pumps. However, there are many challenges for the test technology, accurate numerical model development and gas–liquid flow control. This paper is mainly aimed at critically reviewing various technologies for experimental observation, flow calculation and analysis, and the optimization design of gas–liquid multiphase pumps. In this regard, the experimental results including the energy performance, flow pattern and bubble movement in the multiphase pump are presented in detail. Discussions on the turbulence model, multiphase flow model and bubble balance model are carried out for the flow prediction in such pumps. Various numerical results are presented, including energy performance, bubble distribution, vorticity, phase interaction and pressure fluctuation. Moreover, the flow control and optimization strategy are briefly introduced. Having carried out an extensive literature review of flow characteristics in multiphase pumps, the deficiencies of relevant fields and suggestions for future research direction are given.

Abstract Background Arthrogryposis multiplex congenita (AMC) is a rare syndrome with multiple joint contractures. Within the medical community, there is controversy surrounding AMC in terms of the ideal surgical approach and age for performing a reduction of dislocated hips. The purpose of this retrospective study was to evaluate the clinical outcomes of early open reduction of infant hip dislocation with arthrogryposis multiplex congenita following a modified Smith-Petersen approach that preserves the rectus femoris. Methods From 2010 to 2017, we performed this procedure on 28 dislocated hips in 20 infants under 12 months of age with AMC. The clinical and radiology data were reviewed retrospectively. The mean age at surgery was 6.9 ± 5.1 months, with a mean follow-up of 42.4 ± 41.1 months. Results After open reduction, the average hip acetabular index (AI), the international hip dysplasia institute classification (IHDI), and the hip range of motion significantly improved (all P

Abstract Background The accuracy of 2D and 3D strain analyses was evaluated by comparing strain and cardiac function parameters in Fontan repair patients and normal child volunteers. Methods We retrospectively enrolled 32 patients with Fontan circulation and 32 child volunteers who had undergone clinical cardiac magnetic resonance (CMR) assessment of the dominant ventricle with a 1.5-Tesla MRI scanner. Global and regional strain (2D and 3D) of the dominant ventricle in both groups was assessed using CMR feature-tracking. Correlations between cardiac function and strain data were assessed using Pearson’s correlation coefficient values. The intraclass correlation coefficient (ICC) and coefficient of variation (CoV) were determined to evaluate repeatability and agreement. Results The 2D GLS showed significant differences between the Fontan repair patients and volunteers (− 16.49 ± 5.00 vs. -19.49 ± 2.03; p = 0.002). The 2D GRS and 2D GCS showed no significant differences between two groups. 2D GRS: 38.96 ± 14.48 vs. 37.46 ± 7.77; 2D GCS: − 17.64 ± 5.00 vs. -16.89 ± 2.96, respectively; p > 0.05). The 3D global radial strain (GRS), global circumferential strain (GCS), and global longitudinal strain (GLS) showed significant differences between the Fontan repair patients and volunteers (3D GRS: 36.35 ± 16.72 vs. 44.96 ± 9.98; 3D GLS: − 8.86 ± 6.84 vs. -13.67 ± 2.44; 3D GCS: − 13.70 ± 7.84 vs. -18.01 ± 1.78; p

This paper reviews studies on neural networks in aerodynamic data modeling. In this paper, we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs). Subsequently, the history and fundamental methodologies of neural networks are introduced. Furthermore, we classify the neural networks based studies in aerodynamic data modeling and illustrate comparisons among them. These studies demonstrate that neural networks are effective approaches to aerodynamic data modeling. Finally, we identify three important trends for future studies in aerodynamic data modeling: a) the transformation method and physics informed models will be combined to solve high-dimensional partial differential equations; b) in the research area of steady aerodynamic response predictions, model-oriented studies and data-integration-oriented studies will become the future research directions, while in unsteady aerodynamic response predictions, radial basis function neural networks (RBFNNs) are the best tools for capturing the nonlinear characteristics of flow data, and convolutional neural networks (CNNs) are expected to replace long short-term memories (LSTMs) to capture the temporal characteristics of flow data; and c) in the field of steady or unsteady flow field reconstructions, the CNN-based conditional generative adversarial networks (cGANs) will be the best frameworks in which to discover the spatiotemporal distribution of flow field data.

Metallic glasses (MGs) possess excellent corrosion resistance due to their amorphous nature. Zr-Cu-Ni-Al is one of the most investigated metallic glass (MG) systems due to its excellent corrosion resistance, relatively low cost, and nontoxic qualities. Yet the application of the Zr-Cu-Ni-Al MGs is still limited by its glass forming ability (GFA). It has been found that the doping of yttrium can purify the Zr-Cu-Ni-Al melt and increase GFA. However, research on the yttrium doping’s corrosion resistance is still lacking. In the present work, (Zr50.7Cu28Ni9Al12.3)100−xYx (x = 0 (Y0), 1 (Y1), 2 (Y2), 3 (Y3)) alloys were prepared, and the effects of the yttrium doping on the thermodynamic parameters, crystallization activation energy, and corrosion resistance were studied. It was found that the doping of yttrium can improve the GFA and the crystallization-activation energy of the Zr50.7Cu28Ni9Al12.3 MG. The largest GFA is obtained when the yttrium-doping content reaches 2 at.%. The parameter γ is found to be most suitable for the GFA evaluation of the (Zr50.7Cu28Ni9Al12.3)100−xYx alloy system. Y2 alloy exhibits the best corrosion resistance among the Y0–Y3 alloys according to the potentiodynamic polarization results. The simultaneous increase of the GFA, thermal stability, and corrosion resistance was achieved in the Zr50.7Cu28Ni9Al12.3 MG by proper yttrium doping.

(1) Background: The left atrium (LA) is much more than a reservoir for left ventricular filling. The aim of this study was to assess the LA volume and function in patients with repaired pulmonary artery atresia with ventricular septal defect (rPA/VSD) using CMR. (2) Methods: 31 pediatric patients with rPA/VSD and 30 healthy controls were prospectively recruited. Left atrial ejection fraction (EF), strain and strain rate of three phases (reservoir, conduit, and pump) and left atrial volume were measured with cardiac function analysis software. (3) Results: Patients with rPA/VSD had decreased maximal volume index (p = 0.008). Compared to controls, LA reservoir strain and strain rate, conduit strain and strain rate, booster pump strain rate, total EF and passive EF were significantly lower (p = 0.001, p < 0.001, p = 0.001, p = 0.02, p = 0.03, p < 0.001, p < 0.001); the patients with preserved but lower RVEF(p = 0.01, p = 0.02, p = 0.04, respectively) than the patients with higher RVEF (≥50%). (4) Conclusions: In patients with rPA/VSD, LA function was altered when biventricular EF was preserved, which may provide an early indication of left ventricular diastolic dysfunction. CMR can detect LA dysfunction at an early stage, even before LA enlargement.

The question of preserving the patent ductus arteriosus (PDA) during the modified Blalock–Taussig shunt (MBTS) procedure remains controversial. The goal of this study was to investigate the effects of the PDA on the flow features of the MBTS to help with preoperative surgery design and postoperative prediction. In this study, a patient with pulmonary atresia and PDA was included. A patient-specific three-dimensional model was reconstructed, and virtual surgeries of shunt insertion and ductus ligation were performed using computer-aided design. Computational fluid dynamics was utilized to analyze the hemodynamic parameters of varied models based on the patient-specific anatomy and physiological data. The preservation of the PDA competitively reduced the shunt flow but increased total pulmonary perfusion. The shunt flow and ductal flow collided, causing significant and complicated turbulence in the pulmonary artery where low wall shear stress, high oscillatory shear index, and high relative residence time were distributed. The highest energy loss was found when the PDA was preserved. The preservation of PDA is not recommended during MBTS procedures because it negatively influences hemodynamics. This may lead to pulmonary overperfusion, inadequate systemic perfusion, and a heavier cardiac burden, thus increasing the risk of heart failure. Also, it seems to bring no benefit in terms of reducing the risk for thrombosis.

Jasmonate (JA), as an important signal, plays a key role in multiple processes of plant growth, development and stress response. Nicotine and related pyridine alkaloids in tobacco (Nicotiana tabacum L.) are essential secondary metabolites. Whether environmental factors control nicotine biosynthesis and the underlying mechanism remains previously unreported. Here, we applied physiological and biochemical approaches to investigate how salt stress affects nicotine biosynthesis in tobacco. We found that salt stress induced the biosynthesis of JA, which subsequently triggered the activation of JA-responsive gene expression and, ultimately, nicotine synthesis. Bioinformatics analysis revealed the existence of many NtMYC2a-recognized G-box motifs in the promoter regions of NtLOX, NtAOS, NtAOC and NtOPR genes. Applying exogenous JA increased nicotine content, while suppressing JA biosynthesis reduced nicotine biosynthesis. Salt treatment could not efficiently induce nicotine biosynthesis in transgenic anti-COI1 tobacco plants. These results demonstrate that JA acts as the essential signal which triggers nicotine biosynthesis in tobacco after salt stress.

Gamma-aminobutyric acid (GABA) is a four-carbon non-protein amino acid conserved from bacteria to plants and vertebrates. Increasing evidence supports a regulatory role for GABA in plant development and the plant's response to environmental stress. The biosynthesis of nicotine, the main economically important metabolite in tobacco, is tightly regulated. GABA has not hitherto been reported to function in nicotine biosynthesis. Here we report that water flooding treatment (hypoxia) markedly induced the accumulation of GABA and stimulated nicotine biosynthesis. Suppressing GABA accumulation by treatment with glutamate decarboxylase inhibitor impaired flooding-induced nicotine biosynthesis, while exogenous GABA application directly induced nicotine biosynthesis. Based on these results, we propose that GABA triggers nicotine biosynthesis in tobacco seedlings subjected to flooding. Our results provide insight into the molecular mechanism of nicotine biosynthesis in tobacco plants exposed to environmental stress.

This article obtains the impact data of drivers’ unsafe driving behaviors to urban traffic flow through spot field investigation, questionnaires, and traffic accidents dossier. The influence of drivers’ unsafe driving behaviors (driving on the road markings, short following distance, being forced to change lanes, and illegal parking) to the vehicle speed is analyzed, and the impact descending order (illegal parking, being forced to change lanes, driving on the road markings, and the last is short following distance) of the four typical unsafe driving behaviors to the vehicle speed is obtained. At the same time, the impact descending order (being forced to change lanes, short following distance, illegal parking, and the last is driving on the road markings) of the four typical drivers’ unsafe driving behaviors to the traffic flow density is obtained. Some of the unsafe situation which is caused by the drivers’ unsafe driving behaviors is analyzed, such as the lane suddenly narrowing but the number of lanes does not change, reducing the number of lanes, and lane exit confusion. According to these, the basic road traffic capacity is corrected under the road condition of drivers’ unsafe driving behaviors. The research results provide a theoretical basis for improving the relevant laws and regulations, which has an important theoretical and practical value on improving the transportation efficiency and the safety standards of the road traffic system.

The yellow light dilemma zone is widely known as an area on the high-speed intersection approach, where vehicles neither safely stop before the stop line nor proceed through the intersection during amber interval. Within such an area, a vehicle might be involved in a right-angle crash or rear-end collision. This issue has been extensively discussed over five decades in traffic engineering field, covering from theory to practice. However, few comprehensive review literatures on the amber signal dilemma zone problems can be found. The objective of this paper is to summarize the evolution of yellow light dilemma zone researches. Basic definition and boundary of dilemma zone followed by driver behavior and dilemma zone hazard measurement are depicted. At last, the future directions of yellow light dilemma zone research are discussed.

Environmental stress elevates the level of jasmonic acid (JA) and activates the biosynthesis of nicotine and related pyridine alkaloids in tobacco (Nicotiana tabacum L.) by up-regulating the expression of putrescine N-methyltransferase 1 (NtPMT1), which encodes a putrescine N-methyl transferase that catalyzes nicotine formation. The JA signal suppressor JASMONATE ZIM DOMAIN 1 (NtJAZ1) and its target protein, NtMYC2a, also regulate nicotine biosynthesis; however, how these proteins interact to regulate abiotic-induced nicotine biosynthesis is poorly understood. In this study, we found that high-temperature (HT) treatment activated transcription of NtMYC2a, which subsequently stimulated the transcription of genes associated with JA biosynthesis, including Lipoxygenase (LOX), Allene oxide synthase (AOS), Allene oxide cyclase (AOC), and 12-oxophytodienodate reductase (OPR). Overexpression of NtMYC2a increased nicotine biosynthesis by enhancing its binding to the promoter of NtPMT1. Overexpression of either NtJAZ1 or proteasome-resistant NtJAZ1∆C suppressed nicotine production under normal conditions, but overexpression only of the former resulted in low levels of nicotine under HT treatment. These data suggest that HT induces NtMYC2a accumulation through increased transcription to activate nicotine synthesis; meanwhile, HT-induced NtMYC2a can activate JA synthesis to promote additional NtMYC2a activity by degrading NtJAZ1 at the post-transcriptional level.

Aiming to reduce the number of crashes caused by speeding at night on road section with a crosswalk, a study was conducted on the maximum speed limit and safe average luminance at night. In order to investigate the potential relationship between drivers’ recognitive characteristics and driving speed under different road lighting features, data of remaining driving time (period from the time that crossing pedestrian is recognized to the time that vehicle arrives at crosswalk with an uniform speed) were recorded. The results of the data analysis show that it is more difficult for divers to recognize crossing pedestrian at night when a single pedestrian is statistic and wears dark clothes. The remaining driving time decreases with the increase of driving speed and the decrease of road luminance. With the collected data, several multivariate nonlinear regression models were established to capture the relationship among the variables of remaining driving time at night, the driving speed, and the average luminance. Then the modeling results were used to develop the reasonable speed limit and safe average luminance by physical equations. The case studies are also introduced at the end of the paper.

Gravitropism is a complex process involving a series of physiological pathways. Despite ongoing research, gravitropism sensing and response mechanisms are not well understood. To identify the key transcripts and corresponding pathways in gravitropism, a whole-genome microarray approach was used to analyze transcript abundance in the shoot base of rice (Oryza sativa sp. japonica) at 0.5 h and 6 h after gravistimulation by horizontal reorientation. Between upper and lower flanks of the shoot base, 167 transcripts at 0.5 h and 1202 transcripts at 6 h were discovered to be significantly different in abundance by 2-fold. Among these transcripts, 48 were found to be changed both at 0.5 h and 6 h, while 119 transcripts were only changed at 0.5 h and 1154 transcripts were changed at 6 h in association with gravitropism. MapMan and PageMan analyses were used to identify transcripts significantly changed in abundance. The asymmetric regulation of transcripts related to phytohormones, signaling, RNA transcription, metabolism and cell wall-related categories between upper and lower flanks were demonstrated. Potential roles of the identified transcripts in gravitropism are discussed. Our results suggest that the induction of asymmetrical transcription, likely as a consequence of gravitropic reorientation, precedes gravitropic bending in the rice shoot base.

In ship detection through remote sensing images, optical images often provide rich details and texture information; however, the quality of such optical images can be affected by cloud and fog interferences. In contrast, Synthetic Aperture Radar (SAR) provides all-weather and all-day imaging capabilities; however, SAR images are susceptible to interference from complex sea clutter. Cooperative ship detection combining the advantages of optical and SAR images can enhance the detection performance of ships. In this paper, by focusing on the slight shift of ships in a small neighborhood range in the prior and later temporal images, we propose a method for cooperative ship detection based on neighborhood saliency in multisource heterogeneous remote sensing images, including optical and SAR data. Initially, a sea-land segmentation algorithm of optical and SAR images is applied to reduce interference from land regions. Next, single-source ship detection from optical and SAR images is performed using the RetinaNet and YOLOv5s models, respectively. Then, we introduce a multisource cooperative ship target detection strategy based on the neighborhood window opening of single-source detection results in remote sensing images and secondary detection of neighborhood salient ships. This strategy further leverages the complementary advantages of both optical and SAR heterogeneous images, reducing the possibility of missing ship and false alarms to improve overall detection performance. The performance of the proposed method has been validated using optical and SAR remote sensing data measured from Yantai, China, in 2022. Compared with existing ship detection methods, our method improves detection accuracy AP50 by ≥1.9%, demonstrating its effectiveness and superiority.

BackgroundThis research seeks to formulate a prognostic model for forecasting prostate cancer recurrence by examining the interaction between mitochondrial function and programmed cell death (PCD).MethodsThe research involved analyzing four gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) using univariate Cox regression. These analyses identified genes linked with mitochondrial function and PCD that correlate with recurrence prognosis. Various machine learning algorithms were then employed to construct an optimal predictive model.ResultsA key outcome was the creation of a mitochondrial-related programmed cell death index (mtPCDI), which effectively predicts the prognosis of prostate cancer patients. It was observed that individuals with lower mtPCDI exhibited higher immune activity, correlating with better recurrence outcomes.ConclusionThe study demonstrates that mtPCDI can be used for personalized risk assessment and therapeutic decision-making, highlighting its clinical significance and providing insights into the biological processes affecting prostate cancer recurrence.

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends.

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a category to each pixel in remote sensing images, plays a vital role in a broad range of applications, such as environmental monitoring, urban planning, and land resource utilization. Recently, with the successful application of deep learning in remote sensing, a substantial amount of work has been aimed at developing SSRSI methods using deep learning models. In this survey, we provide a comprehensive review of SSRSI. First, we review the current mainstream semantic segmentation models based on deep learning. Next, we analyze the main challenges faced by SSRSI and comprehensively summarize the current research status of deep-learning-based SSRSI, especially some new directions in SSRSI are outlined, including semisupervised and weakly-supervised SSRSI, unsupervised domain adaption in SSRSI, multimodal data-fusion-based SSRSI, and pretrained models for SSRSI. Then, we examine the most widely used datasets and metrics and review the quantitative results and experimental performance of some representative methods of SSRSI. Finally, we discuss promising future research directions in this area.

Background: Artesunate (ART) has the potential to modulate the nuclear factor kappa B (NF-κB) and Notch1/Hes1 signaling pathways, which play crucial roles in the pathogenesis of osteoporosis. This study aims to explore whether ART participates in the progression of osteoporosis by regulating these signaling pathways. Methods: In the in vitro experiments, we treated bone marrow mesenchymal stem cells (BMSCs) with different concentrations of ART (0, 3, 6, 12 µM) and evaluated osteogenic differentiation using alkaline phosphatase staining (ALP) and alizarin red S staining (ARS) staining. The expression levels of osteocalcin (OCN), RUNT-related transcription factor 2 (RUNX2), osteoprotegerin (OPG), and receptor activator of the nuclear factor kappa ligand (RANKL) were detected by real-time quantitative PCR (RT-qPCR). The effects of ART on NF-κB p65 and Notch1 protein expression were analyzed by Western blot (WB) and immunofluorescence (IF). In the in vivo experiments, a postmenopausal osteoporosis rat model was established via ovariectomy. Bone tissue pathological injury was evaluated using hematoxylin eosin (HE) staining. Serum ALP levels were measured using a kit, bone density was determined by dual-energy X-ray absorptiometry, and serum levels of bone gla protein (BGP), OPG, RANKL, tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), and IL-1β were measured by enzyme-linked immunosorbent assay (ELISA). Additionally, the expression of NF-κB p65 and Notch1 in tissues was assessed by immunohistochemistry. Results: In vitro experiments revealed that compared to the control group, ART dose-dependently promoted BMSCs proliferation and enhanced their osteogenic differentiation capability. The expression of OCN, RUNX2, and OPG significantly increased in the ART-treated group, while RANKL expression decreased significantly (p < 0.05). ART significantly inhibited the expression of NF-κB p65 and Notch1/Hes1 signaling pathway proteins (p < 0.05). Compared to ART treatment alone, combined treatment with ART and phorbol myristate acetate (PMA) or valproic acid (VPA) resulted in increased expression of NF-κB p65 and Notch1 proteins and decreased osteogenic differentiation capability (p < 0.05). In vivo experiments showed that in rats treated with ART, bone damage was significantly reduced, bone density and mineral content were restored considerably, and the expression of inflammatory factors (TNF-α, IL-6, IL-1β) decreased significantly (p < 0.05). Additionally, ART treatment significantly reduced the expression of NF-κB p65 and Notch1 proteins, increased OPG expression, and decreased BGP and RANKL levels (p < 0.05). Conclusion: In summary, ART facilitates the osteogenic differentiation of BMSCs by inhibiting the NF-κB and Notch1/Hes1 signaling pathways, thereby exerting significant protective effects against osteoporosis.

Abstract Background Knee osteoarthritis (KOA) is an age-related degenerative disease characterized by abrasion of articular cartilage. Tongbi Huoluo Decoction (TBHLD) has been transformed from the famous traditional Chinese medicine Duhuo Jisheng Decoction, which can effectively alleviate pain symptoms in KOA. However, the active components and mechanisms of TBHLD in treating KOA have not yet been elucidated. The purpose of the study was to demonstrate the molecular mechanism of TBHLD in treating KOA. Methods The components and targets of TBHLD and KOA were collected from multiple databases, and the protein to protein interaction (PPI) network was constructed. Next, we performed topological calculation and enrichment analysis. Besides, we performed virtual screening for molecular docking and molecular dynamics simulation (MDS). Furthermore, the vitro and vivo experiments were performed to evaluate the validity and mechanism of TBHLD. Results 206 active components and 187 potential targets were screened from Tongbi Huoluo Decoction. A total of 50 intersecting genes were identified between TBHLD and KOA, 20 core targets were calculated by network topology analysis. The core targets were enriched in the ECM interaction pathways. The results of virtual screening for molecular docking and MDS showed that the active components of TBHLD had steady binding conformations with core genes. Moreover, we identified 32 differential serum components in TBHLD-containing serum using LC–MS, including 22 upregulated and 10 downregulated serum components. TBHLD improved the proliferation activity of OA chondrocytes, decreased the expression of Col1a1, Col1a2, Mmp2, Mmp13 in OA chondrocytes, ameliorated the cartilage lesions and restored the cartilage abrasion. Conclusion TBHLD inhibited degradation of cartilage ECM by regulating the expression of type I collagens and Mmps to ameliorate cartilage degeneration in KOA.

IntroductionSpiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.MethodsRecognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of inaccurate gradient backpropagation caused by Maxpooling in SNNs and address it by developing a new Maxpooling module. Furthermore, we adopt spatio-temporal block (STB) in the classification head instead of global average pooling, facilitating the aggregation of spatial and temporal features.ResultsSGLFormer demonstrates its superior performance on static datasets such as CIFAR10/CIFAR100, and ImageNet, as well as dynamic vision sensor (DVS) datasets including CIFAR10-DVS and DVS128-Gesture. Notably, on ImageNet, SGLFormer achieves a top-1 accuracy of 83.73% with 64 M parameters, outperforming the current SOTA directly trained SNNs by a margin of 6.66%.DiscussionWith its high performance, SGLFormer can support more computer vision tasks in the future. The codes for this study can be found in https://github.com/ZhangHanN1/SGLFormer.