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Abstract Background Allergic rhinitis (AR) subjects might have their microenvironment changed due to pathogenesis and living environment. Whether the nasal microbe in AR children differs from healthy subjects and how it interplays with dermal, oral and indoor dust microbe needs to be elucidated. Methods In this case–control study, we analyzed and compared the bacterial characterization and associations in nasal, dermal, oral swab samples and dust samples in 62 children with physician-diagnosed AR(cases) and 51 age- and gender-matched healthy ones with no history of allergic diseases(controls). Full-length 16S rRNA sequencing(swabs) and shotgun metagenomics(dust) were applied. Bacterial diversity, composition, abundance difference characteristics and fast expectation–maximization for microbial source tracking(FEAST) analysis were performed and compared between cases and controls. Results The α-diversity of dust microorganisms in AR was lower than that in control group (P = 0.034), and the β-diversity indices of microorganisms in nasal cavity (P = 0.020), skin (P = 0.001) and dust (P = 0.004) were significantly different from those in control group. At species levels, a total of 10, 15, 12, and 15 bacterial species were differentially enriched in either cases or controls in nasal, dermal, oral, and dust samples, respectively(Linear Discriminant Analysis(LDA) score > 2, P

Abstract The serum uric acid to serum creatinine ratio (SUA/sCr) is a standardized index of renal function. More importance was attached to the significance of this ratio in the progression of hypertension. While the association between the prognosis of hypertension and SUA/sCr is unknown. Therefore, we aimed to prospectively examine the associations of serum uric acid to serum creatinine ratio and all-cause and CVD mortality in adults with hypertension. Participants with hypertension from NHANES 1999–2018 (n = 15,269) were included. They were stratified by 1 increment of SUA/sCr ratio and categorized into 6 groups as ≤ 4, > 4 to 5, > 5 to 6, > 6 to 7, > 7 to 8, and > 8. The reason for categorization in 6 groups was to analyze the influence of different ratios on outcomes accurately and provide more precise guidance. The sample size is large enough that even if divided into 6 groups, it does not affect the statistical power. The primary outcomes were all-cause and CVD mortality. Weighted multivariable Cox proportional hazards regression models were used to estimate hazard ratio (HRs) of mortality. Restricted cubic spline regression models were utilized to examine dose–response associations between the serum uric acid to serum creatinine ratio and all-cause and CVD mortality. Relatively comprehensive stratified analyses were conducted to confirm the accuracy and stability of the results. There were 15,269 total participants, 49.4% of whom were men, with an average age of 56.6 years. Weighted multivariable Cox proportional hazards regression models demonstrated participants in the lowest group (≤ 4) had the HRs (95% CIs) of 1.43 (1.18, 1.73) for all-cause mortality and 2.8 (1.92, 4.10) for CVD mortality when compared to the reference group. Participants in the highest group (> 8) had the HRs (95% CIs) of 0.47 (0.25, 0.89) for CVD mortality when compared to the reference group. There were progressively lower risks for all-cause and CVD mortality with the SUA/sCr ratio increased (both P trend

ABSTRACTEstimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.

Abstract Background At present, the most effective treatment for symptomatic moyamoya disease (MMD) is surgery. However, the high incidence of postoperative complications is a serious problem plaguing the surgical treatment of MMD, especially the acute cerebral infarction. Decreased cerebrovascular reserve is an independent risk factor for ischemic infarction, and the pulsatility index (PI) of transcranial Doppler (TCD) is a common intuitive index for evaluating intracranial vascular compliance. However, the relationship between PI and the occurrence of ischemic stroke after operation is unclear. Objective To explore whether the PI in the middle cerebral artery (MCA) could serve as a potential predictor for the occurrence of ischemic infarction after bypass surgery in MMD. Methods We performed a retrospective analysis of data from 71 patients who underwent combined revascularization surgery, including superficial temporal artery-middle cerebral artery (STA-MCA) anastomosis and encephalo-duro-myo-synangiosis (EDMS). The patients were divided into two groups according to the median of ipsilateral MCA-PI before operation, low PI group (MCA-PI

Background: The Albumin-Bilirubin (ALBI) score, recommended for assessing the prognosis of hepatocellular carcinoma patients, has garnered attention. The efficacy of ALBI score in forecasting the risk of death in sepsis patients remains limited. We designed two cohort studies to assess the association between ALBI score and in-hospital mortality in patients with sepsis. Methods: A retrospective analysis was conducted utilizing data from the Second Affiliated Hospital of Guangzhou Medical University and the Medical Information Mart for Intensive Care IV(MIMIC-IV). Patients diagnosed with sepsis were included in the analysis. The primary outcome was the in-hospital mortality. Multivariate Cox regression analysis was conducted to assess the independent association between the ALBI score and mortality, with adjustment for potential confounders. Subgroup analysis was conducted to assess the robustness of the findings. Results: The Guangzhou Sepsis Cohort (GZSC) of the Second Affiliated Hospital of Guangzhou Medical University comprised 2969 participants, while the MIMIC-IV database included 8841 participants. The ALBI score were categorized into -1.39. After adjusting for confounders, a linear relationship was observed between ALBI score and mortality. Patients with a high ALBI grade were associated with higher in-hospital mortality in both the GZSC (HR: 1.52, 95%CI: 1.24–1.87, p

The use of technology to provide contextual information for decision-making has been a long-standing practice. The study aims to identify the antecedents influencing tourists’ use of context-aware applications. It employs the Unified Model of Electronic Government Adoption (UMEGA) and the Expectation Confirmation Model (ECM) to investigate tourists’ behaviors in the use of Personal Locator Beacons (PLBs) during the pre-adoption and post-adoption stages at the Mole National Park in Ghana. Data was analyzed using Smart PLS 3.0 software. The findings suggest that pre-adoption performance expectancy, motivational needs, and personal situation significantly affect tourists’ pre-adoption intentions to continue using context-aware applications. In addition, information recommendation, service quality, confirmation expectation, and satisfaction influenced continued use and recommendation. However, social influence and enjoyment experience did not significantly impact tourists’ behavioral intentions. The findings contribute to theory by integrating components of the two models (UMEGA & ECM) and delving into the antecedents of users’ decision-making processes.

Indoor microorganisms impact asthma and allergic rhinitis (AR), but the associated microbial taxa often vary extensively due to climate and geographical variations. To provide more consistent environmental assessments, new perspectives on microbial exposure for asthma and AR are needed. Home dust from 97 cases (32 asthma alone, 37 AR alone, 28 comorbidity) and 52 age- and gender-matched controls in Shanghai, China, were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry. Homes of healthy children were enriched with environmental microbes, including Paracoccus, Pseudomonas, and Psychrobacter, and metabolites like keto acids, indoles, pyridines, and flavonoids (astragalin, hesperidin) (False Discovery Rate

A severe ozone episode occurred in cities along the Yangtze River of Jiangsu Province (UAYRJS) from 6 to 8 September 2022, with daily maximum 8-h average ozone concentrations in the range of 65.8–119 ppb, peaking in Nanjing on 7 September. We used the air quality model WRF-CMAQ-ISAM and the Lagrange trajectory model HYSPLIT to quantify the ozone contribution of each region and analyze the causes and regional transmission pathways of ozone pollution in the UAYRJS. Based on simulated emissions, we also estimated the contribution of biogenic volatile organic compounds. We found that weather has a negative impact on pollution, and ozone pollution tracks the movement of the Western Pacific Subtropical High. UAYRJS was affected by oceanic pollution, and there was a mutual influence among the area’s cities. On 6 September, the ozone in UAYRJS was mostly locally generated (50–98%); on 7 September, it was dominated by extra-regional transport (50–80%). Isoprene concentrations in UAYRJS increased by 0.03–0.1 ppb on 6 and 7 September compared with 5 September. Sensitivity testing showed that the hourly ozone concentration increased by 0.1–27.8 ppb (7.6–19.1%) under the influence of biogenic emissions. The results provide a scientific basis for future ozone control measures.

Abstract Background Cerebral ischaemia‒reperfusion (I/R) frequently causes late-onset neuronal damage. Breviscapine promotes autophagy in microvascular endothelial cells in I/R and can inhibit oxidative damage and apoptosis. However, the mediation mechanism of breviscapine on neuronal cell death is unclear. Methods First, transcriptome sequencing was performed on three groups of mice: the neuronal normal group (Control group), the oxygen-glucose deprivation/ reoxygenation group (OGD/R group) and the breviscapine administration group (Therapy group). Differentially expressed genes (DEGs) between the OGD/R and control groups and between the Therapy and OGD/R groups were obtained by the limma package. N6-methyladenosine (m6A) methylation-related DEGs were selected by Pearson correlation analysis. Then, prediction and confirmation of drug targets were performed by Swiss Target Prediction and UniProt Knowledgebase (UniProtKB) database, and key genes were obtained by Pearson correlation analysis between m6A-related DEGs and drug target genes. Next, gene set enrichment analysis (GSEA) and Ingenuity pathway analysis (IPA) were used to obtain the pathways of key genes. Finally, a circRNA-miRNA‒mRNA network was constructed based on the mRNAs, circRNAs and miRNAs. Results A total of 2250 DEGs between the OGD/R and control groups and 757 DEGs between the Therapy and OGD/R groups were selected by differential analysis. A total of 7 m6A-related DEGs, including Arl4d, Gm10653, Gm1113, Kcns3, Olfml2a, Stk26 and Tfcp2l1, were obtained by Pearson correlation analysis. Four key genes (Tfcp2l1, Kcns3, Olfml2a and Arl4d) were acquired, and GSEA showed that these key genes significantly participated in DNA repair, e2f targets and the g2m checkpoint. IPA revealed that Tfcp2l1 played a significant role in human embryonic stem cell pluripotency. The circRNA-miRNA‒mRNA network showed that mmu_circ_0001258 regulated Tfcp2l1 by mmu-miR-301b-3p. Conclusions In conclusion, four key genes, Tfcp2l1, Kcns3, Olfml2a and Arl4d, significantly associated with the treatment of OGD/R by breviscapine were identified, which provides a theoretical basis for clinical trials.

Iptacopan, the first orally available small-molecule complement factor B inhibitor, was developed by Novartis AG of Switzerland. Iptacopan for the treatment of PNH was just approved by the FDA in December 2023. Other indications for treatment are still in phase III clinical trials. Iptacopan is a small-molecule inhibitor targeting complement factor B, showing positive therapeutic effects in the treatment of PNH, C3 glomerulonephritis, and other diseases. Although Iptacopan is already on the market, there has been no detailed synthesis process or specific parameter report on the intermediates during the synthesis of its compounds except for the original research patent. In this study, a practical synthesis route for Iptacopan was obtained through incremental improvement while a biosynthesis method for ketoreductase was used for the synthesis of the pivotal intermediate 12. Moreover, by screening the existing enzyme library of our research group on the basis of random as well as site-directed mutagenesis methods, an enzyme (M8) proven to be of high optical purity with a high yield for biocatalectic reduction was obtained. This enzyme was used to prepare the compound benzyl (2S,4S)-4-hydroxy-2-(4-(methoxycarbonyl)-phenyl)-piperidine-1-carboxylate) white powder (36.8 g HPLC purity: 98%, ee value: 99%). In the synthesis of intermediate 15, the reaction was improved from two-step to one-step, which indicated that the risk of chiral allosterism was reduced while the scale was expanded. Finally, Iptacopan was synthesized in a seven-step reaction with a total yield of 29%. Since three chiral intermediate impurities were synthesized directionally, this paper lays a solid foundation for the future of pharmaceutical manufacturing.

AbstractObjectives To ascertain if postoperative thrombocytopenia following open aortic surgery with a median sternotomy can predict early- and intermediate-term morbidity and mortality.Methods From January 2018 to December 2022, a comparison was made between patients who had and didn’t have postoperative thrombocytopenia (defined as a nadir < 75 × 103/μL after 72 h of open aortic surgery with median sternotomy). Intermediate-term mortality during follow-up was the main result, with cerebrovascular accident and acute renal injury requiring dialysis as secondary events. Inverse probability treatment weighting (IPTW) was used to account for selection bias between groups. The Kaplan-Meier method with the log-rank test was used to assess intermediate-term survivals following IPTW modification. To identify the nonlinear link between platelet nadir and mortality probability, a generalized additive mix model was applied. To help increase power in testing for the overall effect of platelet nadir on outcomes in the generalized additive mix model, the hazard ratios and 95% CIs for each subgroup and their interactions were examined.Results The study included 457 patients, 347 male (75.9%), with mean age of 54 ± 12 years. The last follow-up was done on April 14th, 2023 and the median follow-up time was 16 (6-31) months. Following IPTW, patient characteristics were balanced among cohorts. Platelet nadir was found to be significantly inversely related to early-term mortality (IPTW-adjusted hazard ratio = 0.968 (0.960, 0.977), p

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016–2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%–41.6%; RR 1.196 [0.698–2.049] for the predicted risk of 41.6%). By using artificial intelligence to define an individual’s benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

Abstract Purpose The present study evaluated the efficacy of C-reactive protein (CRP) and D-dimer and the combination of them as prognostic indicators for patients with acute type A aortic dissection (ATAAD). Methods This is a retrospective cohort study. From January 2019 to December 2021, patients with ATAAD admitted to the emergency medicine center of our hospital within 24 h after symptoms (chest pain, back pain, abdominal pain and so on) onset were enrolled in our study. Serum concentration of CRP and D-dimer were measured during hospitalization. Logistic regression was used to evaluate the association between these two biomarkers and in-hospital adverse outcomes (IAO) by adjusting confounding factors. Predictive efficacy was assessed by area under the curve (AUC) of receiver operating characteristic curve. Results A total of 199 patients with ATAAD were finally enrolled. They were categorized as Non-IAO group (n = 146) and IAO group (n = 53) according to postoperative outcomes. After controlling for potentially confounding variables, we found categorized variables that admission CRP > 54.28 mg/L, admission D-dimer > 8.45 mg/L and peak D-dimer > 24.89 mg/L were independent predictors of in-hospital adverse outcomes. Multiple Logistic regression analysis revealed that the odd ratios were 2.9 for admission D-dimer > 8.45 [95% Confidence Interval (CI) 1.11–7.5, p = 0.03], 4.9 for admission CRP > 54.28 (95% CI 1.6–14.9, p = 0.005) and 5.7 for peak D-dimer > 24.89 (95% CI 2.49–13, p 8.45 mg/L, peak D-dimer > 24.89 mg/L and admission CRP > 54.28 mg/L are independent predictors of in-hospital adverse outcomes in patients with ATAAD. Combination of these three markers will improve the predictive efficacy.

ObjectiveTo examine the association between primary medical and health preparedness (PMHP), satisfaction with medical and health services (SMHS), and life satisfaction (LS).MethodsUsing the latest national representative data from the 2019 Chinese Social Survey and the 2018 Health Statistics Yearbook for each province in China, we conducted multi-level models to test the effect of three aspects of PMHP (the number of primary medical and health institutions (PMHIs), the number of beds in PMHIs, and the number of staff in PMHIs) on LS, and the mediation role of SMHS in this effect.ResultsThe number of staff in PMHIs has a significant positive effect on people's LS. Besides, this effect is mediated completely by SMHS. However, the number of PMHIs and the number of beds in PMHIs do not affect LS significantly.ConclusionsPMHP has a positive impact on LS, but this impact is associated only with the number of staff in PMHIs. Therefore, governments should focus on optimizing human resources in PMHIs to meet the LS needs of individuals.

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods.

Objectives: This study aims to evaluate the association among framed messages (egoism-, altruism-, and loss-framed information), perceived net benefits (PNB), and willingness to receive a COVID-19 vaccine.Methods: A between-subject survey experiment was designed to assess the above association. A total of 1,316 individuals were included in this study. The participants were randomly assigned to one control group (receiving non-framed information) and three experimental groups (receiving egoism-, altruism-, and loss-framed information). The participants then reported their vaccination willingness and perceived effectiveness and side effects of vaccination. PNB was determined by subtracting the perceived side effects from perceived effectiveness.Results: Compared with the control group, participants in the experimental groups exhibited stronger vaccination willingness. Higher PNB levels were associated with enhanced vaccination willingness. However, only loss-framed messages indirectly affected vaccination willingness through PNB.Conclusion: PNB can mediate the impact of message framing on vaccination willingness. However, the mediation effect of PNB was only found in the relationship between loss-framed messages and vaccination willingness.

The rapid development of convolutional neural networks has significant implications for automated underwater fishing operations. Among these, object detection algorithms based on underwater robots have become a hot topic in both academic and applied research. Due to the complexity of underwater imaging environments, many studies have employed large network structures to enhance the model’s detection accuracy. However, such models contain many parameters and consume substantial memory, making them less suitable for small devices with limited memory and computing capabilities. To address these issues, a YOLOv6-based lightweight underwater object detection model, YOLOv6-ESG, is proposed to detect seafood, such as echinus, holothurian, starfish, and scallop. First, a more lightweight backbone network is designed by rebuilding the EfficientNetv2 with a lightweight ODConv module to reduce the number of parameters and floating-point operations. Then, this study improves the neck layer using lightweight GSConv and VoVGSCSP modules to enhance the network’s ability to detect small objects. Meanwhile, to improve the detection accuracy of small underwater objects with poor image quality and low resolution, the SPD-Conv module is also integrated into the two parts of the model. Finally, the Adan optimizer is utilized to speed up model convergence and further improve detection accuracy. To address the issue of interference objects in the URPC2022 dataset, data cleaning has been conducted, followed by experiments on the cleaned dataset. The proposed model achieves 86.6% mAP while the detection speed (batch size = 1) reaches 50.66 FPS. Compared to YOLOv6, the proposed model not only maintains almost the same level of detection accuracy but also achieves faster detection speed. Moreover, the number of parameters and floating-point operations reaches the minimum levels, with reductions of 75.44% and 79.64%, respectively. These results indicate the feasibility of the proposed model in the application of underwater detection tasks.

The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.

Automatic personality trait recognition has attracted increasing interest in psychology, neuropsychology, and computer science, etc. Motivated by the great success of deep learning methods in various tasks, a variety of deep neural networks have increasingly been employed to learn high-level feature representations for automatic personality trait recognition. This paper systematically presents a comprehensive survey on existing personality trait recognition methods from a computational perspective. Initially, we provide available personality trait data sets in the literature. Then, we review the principles and recent advances of typical deep learning techniques, including deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Next, we describe the details of state-of-the-art personality trait recognition methods with specific focus on hand-crafted and deep learning-based feature extraction. These methods are analyzed and summarized in both single modality and multiple modalities, such as audio, visual, text, and physiological signals. Finally, we analyze the challenges and opportunities in this field and point out its future directions.

For decades, E-government has been integrated into the governance aspect of developed and developing economies. Its adoption enabled public officials and agencies to discharge their functions and make data-driven decisions in an equitable and timely manner. Literature however is limited in terms of explaining the cognitive and attitudinal factors that contribute to the adoption and utilization of e-government systems and platforms. This study contributes to the ongoing discussions by means of exploring behavioral factors that could influence individuals’ attitudes and behavior toward e-government adoption. The study utilized the theory of planned behavior to assess the psychological and cognitive influencers that elicit the attitude and behavior of employees toward the use of the new technology. The case study was conducted among the public sector institutions in Ghana. The structural model connecting the variables in the study was examined using Partial Least Squares (PLS) Structural Equation Model (SEM) techniques. Findings from the study indicate that subjective norms and attitudes have a significant effect on individuals’ behavioral intentions. However, perceived behavioral control was found not to have a significant effect on the behavioral intentions of employees as suggested by previous studies. Although the findings contribute to theory by elucidating which components of the theory of planned behavior truly impact individuals’ attitudes and mental models, it pays limited attention to the role of cultural dynamics and how it can influence the behavioral intentions of individuals. This can be addressed in future studies.

The purpose of this study is to explore the impact of entrepreneurial leadership on entrepreneurial performance in start-ups. Specifically, a moderated serial mediation model was developed to investigate the mediating role of tacit knowledge sharing and job embeddedness and the moderating effect of career growth opportunities. Data was collected from 376 start-up employees via an online survey platform. Using hierarchical multiple regression and Hayes' PROCESS Macro by SPSS 21.0, and structural equation modeling by AMOS 23.0, support was found for both mediation and moderation effects. Results showed that entrepreneurial leadership significantly positively affects entrepreneurial performance by mediating with tacit knowledge sharing and job embeddedness. Moreover, career growth opportunities moderate the serial mediating effect of tacit knowledge sharing and job embeddedness between entrepreneurial leadership and entrepreneurial performance. This study provides theoretical guidance for entrepreneurial leadership to improve entrepreneurial performance.

Emerging evidence suggests that the nervous system is involved in tumor development in the periphery, however, the role of the central nervous system remains largely unknown. Here, by combining genetic, chemogenetic, pharmacological, and electrophysiological approaches, we show that hypothalamic oxytocin (Oxt)-producing neurons modulate colitis-associated cancer (CAC) progression in mice. Depletion or activation of Oxt neurons could augment or suppress CAC progression. Importantly, brain treatment with celastrol, a pentacyclic triterpenoid, excites Oxt neurons and inhibits CAC progression, and this anti-tumor effect was significantly attenuated in Oxt neuron-lesioned mice. Furthermore, brain treatment with celastrol suppresses sympathetic neuronal activity in the celiac-superior mesenteric ganglion (CG-SMG), and activation of β2 adrenergic receptor abolishes the anti-tumor effect of Oxt neuron activation or centrally administered celastrol. Taken together, these findings demonstrate that hypothalamic Oxt neurons regulate CAC progression by modulating the neuronal activity in the CG-SMG. Stimulation of Oxt neurons using chemicals, for example, celastrol, might be a novel strategy for colorectal cancer treatment.

Abstract Herein, a 3D composite electrode supported by g-C3N4 nanowire framework as scaffold and poly(3,4-ethylenedioxythiophene): poly(4-styrenesulfonate) (PEDOT: PSS) as conducting polymer is reported for flexible solid-state electrochemical capacitors. Compared to pure PEDOT: PSS, the composite electrodes have a greatly increased specific surface and showed good electrochemical performance. A specific capacitance of 202 F g−1 is achieved, and 83.5% of initial capacitance maintained after 5000 cycles. The device based on the 3D g-C3N4/PEDOT: PSS electrode also exhibits good performance in capacitance, flexibility, and cycling stability.

This study focuses on altering the diffusion combustion characteristics of the self-reflux burner by optimizing the burner nozzle structure. By adjusting the jet angle of each nozzle, enhancing the diffusion effect of fuel and air in the combustion chamber, a low-oxygen combustion atmosphere is formed in the high-temperature zone of the combustion chamber, avoiding local high temperature caused by fuel accumulation and combustion, reducing the combustion rate of nitrogen, and then the thermal NOx emission is reduced. Parallel, inwardly inclined, and outwardly inclined jet angles are studied in simulation, and five working conditions (-10°, -5°, 0°, 5°, and 10°) are calculated, compared, and analyzed. The cooperative coupling mechanism between multiple fields (velocity, concentration, and temperature) is compared and analyzed. We use the diffusion effect to reduce the oxygen concentration in the central axis and achieve the goal of reducing NOx by reducing the maximum combustion temperature. The simulation results show that the maximum and average temperatures of the combustion chamber are reduced by 28.9 K and 15.38 K, respectively, when the jet angle is outwardly tilted by 5°, which reduces the local high temperature; and NOx emissions are in turn reduced by 104.72 ppm.

Objective: To investigate the effect and the underlying mechanism of Pertussis toxin (PTX) on microglia in the setting of cerebral ischemia.Methods: We tested the effect of PTX 400 ng/days on middle cerebral artery occlusion stroke model by evaluating the neurologic function, infarct size, microglial distribution, and activation. In parallel, we also tested the effect of PTX on primary cultured microglia by evaluating microglial proliferation, activation, cytokine release, and CX3CR1 expression.Results: PTX reduced the poststroke infarct size, improved the neurologic function as evaluated by Longa score, and reduced microglial aggregation and activation in the infarcted area. Further, PTX significantly decreased lipopolysaccharide-stimulated microglial proliferation, the release of interleukin 1β (IL-1β) and tumor necrosis factor α (TNF-α), and the expression of CX3CR1.Interpretation: PTX treatment in stroke reduced microglial accumulation and activation in the infarct zone, resulting in a better functional outcome. The benefits of PTX treatment may be attributed to the reduced production of proinflammatory cytokine such as IL-1β and TNF-α and reduced expression of chemokine CX3CR1.

Abstract An activated carbon aerogel (ACA-500) with high surface area (1765 m2 g−1), pore volume (2.04 cm3 g−1), and hierarchical porous nanonetwork structure is prepared through direct activation of organic aerogel (RC-500) with a low potassium hydroxide ratio (1:1). Based on this substrate, a polyaniline (PANi)-coated activated carbon aerogel/sulfur (ACA-500-S@PANi) composite is prepared via a simple two-step procedure, including melt-infiltration of sublimed sulfur into ACA-500, followed by an in situ polymerization of aniline on the surface of ACA-500-S composite. The obtained ACA-500-S@PANi composite delivers a high reversible capacity up to 1208 mAh g−1 at 0.2C and maintains 542 mAh g−1 even at a high rate (3C). Furthermore, this composite exhibits a discharge capacity of 926 mAh g−1 at the initial cycle and 615 mAh g−1 after 700 cycles at 1C rate, revealing an extremely low capacity decay rate (0.48‰ per cycle). The excellent electrochemical performance of ACA-500-S@PANi can be attributed to the synergistic effect of hierarchical porous nanonetwork structure and PANi coating. Activated carbon aerogels with high surface area and unique three-dimensional (3D) interconnected hierarchical porous structure offer an efficient conductive network for sulfur, and a highly conductive PANi-coating layer further enhances conductivity of the electrode and prevents the dissolution of polysulfide species.

A research prototype CT scanner is currently under development in our lab. One of the key components in this project is the CT detector. This paper describes the design and performance evaluation of the modular CT detector unit for our proposed scanner. It consists of a Photodiode Array Assembly which captures irradiating X-ray photons and converts the energy into electrical current, and a mini Data Acquisition System which performs current integration and converts the analog signal into digital samples. The detector unit can be easily tiled together to form a CT detector. Experiments were conducted to characterize the detector performance both at the single unit level and system level. The noise level, linearity and uniformity of the proposed detector unit were reported and initial imaging studies were also presented which demonstrated the potential application of the proposed detector unit in actual CT scanners.

Depth estimation becomes the key technology to resolve the communications of the stereo vision. We can get the real-time depth map based on hardware, which cannot implement complicated algorithm as software, because there are some restrictions in the hardware structure. Eventually, some wrong stereo matching will inevitably exist in the process of depth estimation by hardware, such as FPGA. In order to solve the problem a postprocessing function is designed in this paper. After matching cost unique test, the both left-right and right-left consistency check solutions are implemented, respectively; then, the cavities in depth maps can be filled by right depth values on the basis of right-left consistency check solution. The results in the experiments have shown that the depth map extraction and postprocessing function can be implemented in real time in the same system; what is more, the quality of the depth maps is satisfactory.

The use of technology to provide contextual information for decision-making has been a long-standing practice. The study aims to identify the antecedents influencing tourists' use of context-aware applications. It employs the Unified Model of Electronic Government Adoption (UMEGA) and the Expectation Confirmation Model (ECM) to investigate tourists' behaviors in the use of Personal Locator Beacons (PLBs) during the pre-adoption and post-adoption stages at the Mole National Park in Ghana. Data was analyzed using Smart PLS 3.0 software. The findings suggest that pre-adoption performance expectancy, motivational needs, and personal situation significantly affect tourists' pre-adoption intentions to continue using context-aware applications. In addition, information recommendation, service quality, confirmation expectation, and satisfaction influenced continued use and recommendation. However, social influence and enjoyment experience did not significantly impact tourists' behavioral intentions. The findings contribute to theory by integrating components of the two models (UMEGA & ECM) and delving into the antecedents of users' decision-making processes. [ABSTRACT FROM AUTHOR]