Infer thermal information from visual information: a cross imaging modality edge learning (CIMEL) framework

The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rate required. Therefore, there is a strong demand to improve the quality of IR images, particularly on edges, without upgrading the hardware in the context of surveillance and industrial inspection systems. This paper proposes a novel Conditional Generative Adversarial Networks (CGAN)-based framework to enhance IR edges by learning high-frequency features from corresponding visual images. A dual-discriminator, focusing on edge and content/background, is introduced to guide the cross imaging modality learning procedure of the U-Net generator in high and low frequencies respectively. Results demonstrate that the proposed framework can effectively enhance barely visible edges in IR images without introducing artefacts, meanwhile the content information is well preserved. Different from most similar studies, this method only requires IR images for testing, which will increase the applicability of some scenarios where only one imaging modality is available, such as active thermograph

The roles of serum vitamin D and tobacco smoke exposure in insomnia: a cross-sectional study of adults in the United States

AimTobacco smoke exposure and vitamin D (VD) status were both associated with insomnia. However, the combined effect of smoking and VD on insomnia has not been discussed. This study aimed to explore the role of VD in the association between tobacco smoke exposure and insomnia.MethodsData on adults were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2005–2008 for this cross-sectional study. Weighted univariate and multivariate logistic regression analyses were used to explore the associations between serum cotinine, serum VD, and insomnia. A surface diagram was drawn to reflect the effect of VD on the association between serum cotinine and insomnia. In addition, the potential regulating effect of VD in subgroups of smoking status was also performed. The evaluation index was odds ratios (ORs) with 95% confidence intervals (CIs).ResultsAmong the eligible participants, 1,766 had insomnia. After adjusting for covariates, we found that elevated serum cotinine levels were associated with higher odds of insomnia [OR = 1.55, 95% CI: (1.22, 1.97)]. However, the relationship between serum VD level and insomnia was not significant (P = 0.553). Higher serum cotinine levels were also associated with higher odds of insomnia [OR = 1.52, 95% CI: (1.17, 1.98)] when serum VD level was <75 nmol/L; however, this relationship became non-significant when serum VD concentration was elevated (P = 0.088). Additionally, the potential regulating effect of VD was also found in adults who were not smoking.ConclusionVD may play a potential regulative role in the association between tobacco smoke exposure and insomnia. Further studies are needed to clarify the causal relationships between VD, tobacco smoke exposure, and insomnia

Experimental verification and identifying biomarkers related to insomnia

IntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.MethodsFirst, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.DiscussionIn summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia

Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification

Sepsis is a systemic inflammatory response syndrome caused by bacteria and other pathogenic microorganisms. Every year, approximately 31.5 million patients are diagnosed with sepsis, and approximately 5.3 million patients succumb to the disease. In this study, we identified biomarkers for diagnosing sepsis analyzed the relationships between genes and Immune cells that were differentially expressed in specimens from patients with sepsis compared to normal controls. Finally, We verified its effectiveness through animal experiments. Specifically, we analyzed datasets from four microarrays(GSE11755、GSE12624、GSE28750、GSE48080) that included 106 blood specimens from patients with sepsis and 69 normal human blood samples. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The composition of 22 immune cell components in patients with sepsis were also determined using CIBERSORT. The expression level of the biomarkers in Sepsis was examined by the use of qRT-PCR and Western Blot (WB). We identified 50 differentially expressed genes between the cohorts, including 2 significantly upregulated and 48 significantly downregulated genes, and KEGG pathway analysis identified Salmonella infection, human T cell leukemia virus 1 infection, Epstein−Barr virus infection, hepatitis B, lysosome and other pathways that were significantly enriched in blood from patients with sepsis. Ultimately, we identified COMMD9, CSF3R, and NUB1 as genes that could potentially be used as biomarkers to predict sepsis, which we confirmed by ROC analysis. Further, we identified a correlation between the expression of these three genes and immune infiltrate composition. Immune cell infiltration analysis revealed that COMMD9 was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. CSF3R was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. NUB1 was correlated with T cells regulatory (Tregs), T cells gamma delta, T cells follicular helper, et al. Taken together, our findings identify potential new diagnostic markers for sepsis that shed light on novel mechanisms of disease pathogenesis and, therefore, may offer opportunities for therapeutic intervention

Diet and lifestyle interventions in postpartum women in China: study design and rationale of a multicenter randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>"Doing the month", or "sitting month", is a traditional practice for postpartum women in China and other Asian countries, which includes some taboos against well-accepted healthy diet and lifestyles in general population. Previous studies have shown this practice may be associated with higher prevalence of postpartum problems. The current multicenter randomized controlled trial (RCT) aims to evaluate outcomes of diet and lifestyle interventions in Chinese postpartum women.</p> <p>Methods/Design</p> <p>The current multicenter RCT will be conducted in three representative areas in China, Shandong province, Hubei province and Guangdong province, which locate in northern, central and southern parts of China, respectively. Women who attend routine pregnancy diagnosis in hospitals or maternal healthcare centers will be invited to take part in this study. At least 800 women who meet our eligibility criteria will be recruited and randomly assigned to the intervention group (n > = 400) and the control group (n > = 400). A three-dimension comprehensive intervention strategy, which incorporates intervention measures simultaneously to individual postpartum woman, their family members and community environment, will be utilized to maximize the effectiveness of intervention. Regular visiting and follow-up will be done in both group; nutrition and health-related measurements will be assessed both before and after the intervention.</p> <p>Discussion</p> <p>To our knowledge, this current study is the first and largest multicenter RCT which focus on the effectiveness of diet and lifestyle intervention on reducing the incidence rate of postpartum diseases and improving health status in postpartum women. We hypothesize that the intervention will reduce the incidence rates of postpartum diseases and improve nutrition and health status due to a balanced diet and reasonable lifestyle in comparison with the control condition. If so, the results of our study will provide especially important evidence for changes in both the concept and action of traditional postpartum practice in China.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov ID NCT01039051.</p

Influence of Sustainable Environment Based on a SWOT-PEST Model on Sports Tourism Service Integration Development

The rapid growth of the social economy allows the masses to have more free time to enjoy a material and civilized life, and also allows many families to participate in sports tourism activities. However, as the development of sports tourism has just started, it still has some deficiencies in service integration. Meanwhile, the concept of sustainable development has been applied to sports tourism services. Through the survey on service attitudes and quality of the service staff by selected tourists, it was found that, according to the concept of sustainable development, 140 tourists have described service attitude and quality as very good and good, and only 35 tourists made other choices. In addition, to solve the shortcomings of sports tourism, this paper studied sports tourism services according to the sustainable environment of a SWOT-PEST model. Through expert scoring, the SWOT-PEST model was scored and compared with the model previously used. The score after use was above 4.3 points, and the score before use was below 4 points. Therefore, the research methods in this paper were valuable for research into sports tourism services

A Study of Factors Influencing Construction Workers’ Intention to Share Safety Knowledge

There is a growing body of research on the factors influencing individual knowledge-sharing behavior, but the exploration of knowledge sharing in the construction industry is still in its infancy. Based on the theory of planned behavior (TPB), this paper introduced factors from the social exchange theory (SET) to develop a comprehensive model for exploring the intention of construction workers to share their safety knowledge. Data were collected from a total of 329 construction workers at five sites. Using the structural equation model method, the research model and path hypotheses of this study were analyzed. The results showed that altruism, trust, and reputation positively influenced the construction workers’ attitude towards sharing safety knowledge. Attitude, safety training, organizational climate, and knowledge-sharing self-efficacy could increase the construction workers’ intention to share their safety knowledge. However, the relationship between workers’ attitudes towards safety knowledge sharing and anticipated extrinsic rewards was not significant. Through identifying the factors underlying workers’ intention to share safety knowledge in the construction industry, the study helps to further understand the influencing factors and mechanisms of safety knowledge sharing willingness among the special group of construction workers and provides practical implications for engineering managers to strengthen construction safety management from the perspective of knowledge sharing

Supplementary document for Channel transformer U-Net: an automatically and effective skeletons extraction network for electronic speckle pattern interferometry - 6186404.pdf

Supplementary Materials for CCT and experimental result