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ObjectivesThe systemic immune-inflammation index (SII), a novel and systematic inflammatory biomarker that is associated with chronic kidney disease (CKD), has not received much attention. This study aimed to investigate the relationship between SII and CKD in the United States (U.S.) population.MethodsOur study ultimately included a nationally representative sample of 10,787 adults who participated in the 2007-2018 National Health and Nutrition Examination Survey. Weighted multivariate logistic regression was used to assess the correlation between SII and CKD, and a restricted cubic spline (RCS) model was subsequently used to explore the non-linear relationship between SII and CKD. Subgroup analyses were performed to further the effects of other covariates on the relationship between SII and CKD.ResultsFollowing confounder adjustment, a higher SII was related to the incidence of CKD (OR =1.36; 95% CI, 1.07–1.73; p =0.01), as validated by multivariable logistic regression. The RCS curve revealed a non-linear positive correlation between SII/1000 and CKD incidence (p for non-linear =0.0206). Additionally, subgroup analysis confirmed a stronger correlation for male participants (OR =2.628; 95% CI, 1.829-3.776) than for female participants (OR =1.733; 95% CI, 1.379-2.178) (p for interaction =0.046).ConclusionsSII is positively associated with the incidence of CKD among U.S. adults, especially in males. However, further studies are needed to confirm our findings and explore the causal factors that can contribute to the prevention and treatment of CKD.

Examining fuel droplet evaporation is crucial for enhancing fuel engine efficiency, conserving energy, and reducing emissions. This study utilizes experimental methods involving ultrasonic standing wave levitation and high-speed cameras to investigate the impact of temperatures and droplet properties, including initial diameter and composition, on the evaporation process. The evaporation behaviors of fuel droplets, like hexadecane and diesel, are documented across a temperature spectrum spanning 150 °C to 550 °C, with an initial droplet equivalent diameter ranging from 0.10 to 0.30 mm. The evaporation rate positively correlates with temperature and may vary by 15% to 71% between hexadecane and diesel droplets.

The characteristics of the tumour cells, as well as how tumour cells interact with their surroundings, affect the prognosis of cancer patients. The resident cells in the tumour microenvironment are mast cells (MCs), which are known for their functions in allergic responses, but their functions in the cancer milieu have been hotly contested. Several studies have revealed a link between MCs and the development of tumours. Mast cell proliferation in colorectal cancer (CRC) is correlated with angiogenesis, the number of lymph nodes to which the malignancy has spread, and patient prognosis. By releasing angiogenic factors (VEGF-A, CXCL 8, MMP-9, etc.) and lymphangiogenic factors (VEGF-C, VEGF-D, etc.) stored in granules, mast cells play a significant role in the development of CRC. On the other hand, MCs can actively encourage tumour development via pathways including the c-kit/SCF-dependent signaling cascade and histamine production. The impact of MC-derived mediators on tumour growth, the prognostic importance of MCs in patients with various stages of colorectal cancer, and crosstalk between MCs and CRC cells in the tumour microenvironment are discussed in this article. We acknowledge the need for a deeper comprehension of the function of MCs in CRC and the possibility that targeting MCs might be a useful therapeutic approach in the future.

With the rapid development of new energy technology, the number of grid-connected systems and switching control frequency are also increasing. It makes the operation state of the power grid more complicated. Therefore, a small step real-time model is urgently needed to monitor the operation of power grid. Based on the existing computing hardware, FPGA has the characteristics of high concurrency and multi-pipeline computing, which ensures the real-time performance of the simulator. Because non-isolated bidirectional half-bridge DC–DC converter is widely used in power grid system, the real-time simulation design based on FPGA is carried out in this paper. This paper proposes an improved Associated Discrete Circuit (ADC) method and derives the formula of the optimal conductivity parameters. By using the transient initialization method, the accuracy of the ADC modeling method is promoted. The simulation model of the non-isolated bidirectional half-bridge converter is built on FPGA with a time-step of 1μs. Under the premise of real-time performance, the relative error of the proposed simulation module is controlled at about 3%.

Abstract Background Internship is a critical period during which nursing students develop clinical skills and establish professional attitudes. Requirements for nursing interns are evolving with the development of medicine and the transformation of teaching models. The emotional intelligence (EI) of nursing students has an influence on their clinical performance. This study aimed to investigate the impact of EI on the clinical ability of nursing interns. Methods A cross-sectional observational study was designed to include nursing students interning in a tertiary hospital in Shanghai, China from April 1, 2019 to April 30, 2020 (N = 310). Chinese versions of the EI scale (EIS) and holistic clinical assessment tool (HCAT) were used to measure the EI and clinical ability of interns, respectively. Pearson’s correlation coefficient was utilized to determine the correlation between EI and clinical ability. Multiple linear regression analysis was performed to further explore the influence of EI on clinical ability, and the structural equation model (SEM) was used for multivariate path analysis. Results The mean EI and clinical ability scores of interns were 125.17 ± 14.98 and 97.91 ± 19.55, respectively, indicating an upper-moderate level in both aspects. EI scores were correlated positively with clinical ability ones (R = 0.534, p < 0.05). Multivariate path analysis showed that “managing emotions” and “facilitating thought” of EI branches have direct effects on clinical ability. Furthermore, the type of school, family financial state and the knowledge of EI indirectly influence clinical ability through their impact on “managing emotions” and “facilitating thought”. Conclusions EI is essential to enhancing the clinical ability of nursing students. EI training should focus on facilitating thought and managing emotions. It is also necessary for educators to consider the context of nursing students and the characteristics of schools.

For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.

Abstract Background Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Patient prognosis is poor, and the existing therapeutic strategies for LUAD are far from satisfactory. Recently, targeting N6-methyladenosine (m6A) modification of RNA has been suggested as a potential strategy to impede tumor progression. However, the roles of m6A modification in LUAD tumorigenesis is unknown. Methods Global m6A levels and expressions of m6A writers, erasers and readers were evaluated by RNA methylation assay, dot blot, immunoblotting, immunohistochemistry and ELISA in human LUAD, mouse models and cell lines. Cell viability, 3D-spheroid generation, in vivo LUAD formation, experiments in cell- and patient-derived xenograft mice and survival analysis were conducted to explore the impact of m6A on LUAD. The RNA-protein interactions, translation, putative m6A sites and glycolysis were explored in the investigation of the mechanism underlying how m6A stimulates tumorigenesis. Results The elevation of global m6A level in most human LUAD specimens resulted from the combined upregulation of m6A writer methyltransferase 3 (METTL3) and downregulation of eraser alkB homolog 5 (ALKBH5). Elevated global m6A level was associated with a poor overall survival in LUAD patients. Reducing m6A levels by knocking out METTL3 and overexpressing ALKBH5 suppressed 3D-spheroid generation in LUAD cells and intra-pulmonary tumor formation in mice. Mechanistically, m6A-dependent stimulation of glycolysis and tumorigenesis occurred via enolase 1 (ENO1). ENO1 mRNA was m6A methylated at 359 A, which facilitated it’s binding with the m6A reader YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) and resulted in enhanced translation of ENO1. ENO1 positively correlated with METTL3 and global m6A levels, and negatively correlated with ALKBH5 in human LUAD. In addition, m6A-dependent elevation of ENO1 was associated with LUAD progression. In preclinical models, tumors with a higher global m6A level showed a more sensitive response to the inhibition of pan-methylation, glycolysis and ENO activity in LUAD. Conclusions The m6A-dependent stimulation of glycolysis and tumorigenesis in LUAD is at least partially orchestrated by the upregulation of METTL3, downregulation of ALKBH5, and stimulation of YTHDF1-mediated ENO1 translation. Blocking this mechanism may represent a potential treatment strategy for m6A-dependent LUAD.

Toxic pollutants in the environment, such as toxins and abused drugs, have posed a major threat to human health and ecosystem security. It is extremely desirable to develop simple, low-cost, sensitive, and reliable techniques for the detection of these pollutants in the environment. As a booming analytical method, photoelectrochemical (PEC) sensors possess low background noise and high sensitivity. The performances of PEC sensors are fundamentally related to the photoelectric conversion efficiency, which mainly depends on the properties of photoactive materials. This review aims to summarize the engineered photoactive materials, i.e., semiconductors and semiconductor-based heterojunctions, as well as their actual applications, with emphasis on sensing mechanisms in PEC sensors for the analysis of toxins and abused drugs in the environment. Finally, the future research perspectives in this field are also discussed.

Abstract Aim To explore the mediating role of post‐traumatic growth and perceived professional benefits between resilience and intent to stay among Chinese nurses to support Wuhan in managing COVID‐19. Design A cross‐sectional questionnaire survey. Methods In May 2020, the study recruited a convenience sample of 200 Chinese nurses to support Wuhan in managing COVID‐19. A set of self‐rating questionnaires was used to measure resilience, post‐traumatic growth, perceived professional benefits and intent to stay. Structural equation modelling was performed with 5,000 bootstrap samples using AMOS 23.0. Results The final model provided a good fit for the data. Resilience had the strongest direct effect on intent to stay. Perceived professional benefits partially mediated the association between resilience and intent to stay. Overall, the serial multiple mediations of post‐traumatic growth and perceived professional benefits in the relationship between resilience and intent to stay was statistically significant.

Neuromarketing has become a new and important topic in the field of marketing in recent years. Consumer behavior research has received increasing attention. In the past decade, the importance of marketing has also been recognized in many fields such as consumer behavior, advertising, information systems, and e-commerce. Neuromarketing uses neurological methods to determine the driving forces behind consumers’ choices. Various neuroscience tools, such as eye movements, have been adopted to help reveal how consumers react to particular advertisements or objects. This information can be used as the basis for new advertising campaigns and brand promotions. To effectively explore the research trends in this field, we must understand the current situation of neuromarketing. A systematic bibliometric analysis can solve this problem by providing publishing trends and information on various topics. In this study, journals that focused on neuromarketing in the field of marketing between 2010 and 2021 were analyzed. These journals were core journals rated by the Association of Business Schools with three or more stars. According to the data analysis results, neuromarketing has 15 main journals with relevant papers. Based on the data collected by the Web of Science (WOS), this study mainly collected 119 references and analyzed the most productive countries, universities, authors, journals, and prolific publications in the field of neuromarketing via Citespace. Through the analysis of knowledge maps, this study explored the mapping of co-citation, bibliographic coupling (BC), and co-occurrence (CC). Moreover, the strongest citation bursts were used to study popular research at different time stages and analyze the research trends of neuromarketing research methods and tools. This study provides an overview of the trends and paths in neuromarketing, which can help researchers understand global trends and future research directions.

Abstract Deep learning is a hot method in face super-resolution reconstruction in recent years, but it needs to further improve the details of reconstructed images and speed up network training. This paper improves the deep residual network from two aspects of residual unit and network structure and proposes a face super-resolution reconstruction algorithm based on the improved model. We improve the network structure of the residual unit by connecting with a densely connected convolutional layer and removing the BN layer, thereby enhancing the information flow between the inner convolutional layers and eliminate the damage to the spatial information of the image by batch normalization processing. At the same time, we combine the output characteristics of each residual unit on the basis of the global residual structure, so the face feature information is more fully utilized and the model detail recovery ability is also improved. Experiments on FDDB and AFLW face datasets show that the proposed method has better performance in feature description and detail information reconstruction, and higher PSNR and SSIM than other methods.

Eimeria tenella encodes a genome of approximately 8000 genes. To date, however, very few data are available regarding E. tenella rhoptry kinase family proteins. In the present study, the gene fragment encoding the mature peptide of the rhoptry kinase family protein 17 of E. tenella (EtROP17) was amplified by PCR and expressed in E. coli. Then, we generated polyclonal antibodies that recognize EtROP17 and investigated the expression of EtROP17 in the merozoite stage of E. tenella by immunofluorescent staining and Western blot analysis. Meanwhile, the protective efficacy of rEtROP17 against E. tenella was evaluated in chickens. Sequencing analysis showed that a single base difference at sequence position 1901 was observed between the SD-01 strain and the Houghton strain. EtROP17 was expressed in the merozoite stage of E. tenella. The results of the animal challenge experiments demonstrated that vaccination with rEtROP17 significantly reduced cecal lesions and oocyst outputs compared with the challenged control group. Our findings indicate that EtROP17 could serve as a potential candidate for developing a new vaccine against E. tenella.

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

(1) Background: Understanding the function of circular RNAs (circRNAs), a class of noncoding RNA, in psoriatic skin can provide important insights into the complex regulation of genes contributing to the pathogenesis of psoriasis. (2) Methods: A novel method was applied to RNA-seq datasets from 93 skin biopsy samples to comprehensively identify circRNAs of all types, i.e., canonical circRNAs from the intron-exon junctions of mRNAs and interior circRNAs (i-circRNAs) from the interior regions of exons, introns, and intergenic regions. Selected circRNAs were experimentally validated by qRT-PCR and Sanger sequencing. CircRNAs with abundant and differential expression were identified and their putative function as competing endogenous RNAs (ceRNAs) was analyzed by an integrated analysis of circRNAs, microRNAs, and mRNAs. (3) Results: With a comprehensive search using no information of splicing signals, we systematically identified 179 highly abundant circRNAs in psoriatic skin. Many of these were reported for the first time and many were differentially expressed in involved versus normal or uninvolved skin. Validation based on three additional RNA-seq datasets confirmed most of the identified circRNAs in psoriatic skin. Experimental analyses confirmed the expression of the well-known circRNA CDR1as, a canonical circRNA, and a novel i-circRNA in psoriasis. We also identified many circRNAs that may act as ceRNAs to regulate the expression of mRNA genes in psoriasis-related signaling pathways in psoriasis. (4) Conclusions: The result of the study suggested that circRNAs are abundant in psoriatic skin, have distinct characteristics, and contribute to psoriatic pathogenesis.

Intrahepatic cholestasis is a kind of clinical syndrome along with hepatotoxicity which caused by intrahepatic and systemic accumulations of bile acid. There are several crucial generating factors of the pathogenesis of cholestasis, such as inflammation, dysregulation of bile acid transporters and oxidative stress. SIRT1 is regarded as a class III histone deacetylase (HDAC). According to a set of researches, SIRT1 is one of the most important factors which can regulate the hepatic bile acid metabolism. SRT1720 is a kind of activator of SIRT1 which is 1000 times more potent than resveratrol, and this paper is aimed to study its protective influence on hepatotoxicity and cholestasis induced by alpha-naphthylisothiocyanate (ANIT) in mice. The findings revealed that SRT1720 treatment increased FXR and Nrf2 gene expressions to shield against hepatotoxicity and cholestasis induced by ANIT. The mRNA levels of hepatic bile acid transporters were also altered by SRT1720. Furthermore, SRT1720 enhanced the antioxidative system by increasing Nrf2, SOD, GCLc, GCLm, Nqo1, and HO-1 gene expressions. In conclusion, a protective influence could be provided by SRT1720 to cure ANIT-induced hepatotoxicity and cholestasis, which was partly through FXR and Nrf2 activations. These results indicated that SIRT1 could be regarded as a therapeutic target to cure the cholestasis.

Context-aware recommender systems generate more relevant recommendations by adapting them to the specific contextual situation of the user and have become one of the most active research areas in the recommender systems. However, there remains a key issue as how contextual information can be used to create intelligent and useful recommender systems. To assist the development and use of context-aware recommendation capabilities, we propose a graph-based framework to model and incorporate contextual information into the recommendation process in an advantageous way. A contextual graph-based relevance measure (CGR) is specifically designed to assess the potential relevance between the target user and the items further used to make an item recommendation. We also propose a probabilistic-based postfiltering strategy to refine the recommendation results as contextual conditions are explicitly given in a query. Depending on the experimental results on the two datasets, the CGR-based method is much superior to the traditional collaborative filtering methods, and the proposed postfiltering method is much effective in context-aware recommendation scenario.