Two Binary trees of Rational numbers -- the S-tree and the SC-tree

In this study, we explore a novel approach to demonstrate the countability of rational numbers and illustrate the relationship between the Calkin-Wilf tree and the Stern-Brocot tree in a more intuitive manner. By employing a growth pattern akin to that of the Calkin-Wilf tree, we construct the S-tree and establish a one-to-one correspondence between the vertices of the S-tree and the rational numbers in the interval $(0,1]$ using 0-1 sequences. To broaden the scope of this concept, we further develop the SC-tree, which is proven to encompass all positive rational numbers, with each rational number appearing only once. We also delve into the interplay among these four trees and offer some applications for the newly introduced tree structures.Comment: 24 pages, 15 figures, v

Integrated single-cell RNA-seq analysis reveals the vital cell types and dynamic development signature of atherosclerosis

Introduction: In the development of atherosclerosis, the remodeling of blood vessels is a key process involving plaque formation and rupture. So far, most reports mainly believe that macrophages, smooth muscle cells, and endothelial cells located at the intima and media of artery play the key role in this process. Few studies had focused on whether fibroblasts located at adventitia are involved in regulating disease process.Methods and results: In this study, we conducted in-depth analysis of single-cell RNA-seq data of the total of 18 samples from healthy and atherosclerotic arteries. This study combines several analysis methods including transcription regulator network, cell-cell communication network, pseudotime trajectory, gene set enrichment analysis, and differential expression analysis. We found that SERPINF1 is highly expressed in fibroblasts and is involved in the regulation of various signaling pathways.Conclusion: Our research reveals a potential mechanism of atherosclerosis, SERPINF1 regulates the formation and rupture of plaques through the Jak-STAT signaling pathway, which may provide new insights into the pathological study of disease. Moreover, we suggest that SRGN and IGKC as potential biomarkers for unstable arterial plaques

Exploring the mechanism of JiGuCao capsule formula on treating hepatitis B virus infection via network pharmacology analysis and in vivo/vitro experiment verification

The JiGuCao capsule formula (JCF) has demonstrated promising curative effects in treating chronic hepatitis B (CHB) in clinical trials. Here, we aimed to investigate JCF’s function and mechanism in diseases related to the hepatitis B virus (HBV). We used mass spectrometry (MS) to identify the active metabolites of JCF and established the HBV replication mouse model by hydrodynamically injecting HBV replication plasmids into the mice’s tail vein. Liposomes were used to transfect the plasmids into the cells. The CCK-8 kit identified cell viability. We detected the levels of HBV s antigen (HBsAg) and HBV e antigen (HBeAg) by the quantitative determination kits. qRT-PCR and Western blot were used to detect the genes’ expression. The key pathways and key genes related to JCF on CHB treatment were obtained by network pharmacological analysis. Our results showed that JCF accelerated the elimination of HBsAg in mice. JCF and its medicated serum inhibited HBV replication and proliferation of HBV-replicating hepatoma cells in vitro. And the key targets of JCF in treating CHB were CASP3, CXCL8, EGFR, HSPA8, IL6, MDM2, MMP9, NR3C1, PTGS2, and VEGFA. Furthermore, these key targets were related to pathways in cancer, hepatitis B, microRNAs in cancer, PI3K-Akt signaling, and proteoglycans in cancer pathways. Finally, Cholic Acid, Deoxycholic Acid, and 3′, 4′, 7-Trihydroxyflavone were the main active metabolites of JCF that we obtained. JCF employed its active metabolites to perform an anti-HBV effect and prevent the development of HBV-related diseases

Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification

Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clothing. Despite great progress, limited cloth-changing training samples in existing CC-ReID datasets still prevent the model from adequately learning cloth-irrelevant features. In addition, due to the absence of explicit supervision to keep the model constantly focused on cloth-irrelevant areas, existing methods are still hampered by the disruption of clothing variations. To solve the above issues, we propose an Identity-aware Dual-constraint Network (IDNet) for the CC-ReID task. Specifically, to help the model extract cloth-irrelevant clues, we propose a Clothes Diversity Augmentation (CDA), which generates more realistic cloth-changing samples by enriching the clothing color while preserving the texture. In addition, a Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained identity-related features and effectively transfers cloth-irrelevant knowledge. Moreover, a Counterfactual-guided Attention Module (CAM) is presented, which learns cloth-irrelevant features from channel and space dimensions and utilizes the counterfactual intervention for supervising the attention map to highlight identity-related regions. Finally, a Semantic Alignment Constraint (SAC) is designed to facilitate high-level semantic feature interaction. Comprehensive experiments on four CC-ReID datasets indicate that our method outperforms prior state-of-the-art approaches

Association between leptin and NAFLD: a two-sample Mendelian randomization study

Abstract Background The etiology of nonalcoholic fatty liver disease (NAFLD) involves a complex interaction of genetic and environmental factors. Previous observational studies have revealed that higher leptin levels are related to a lower risk of developing NAFLD, but the causative association remains unknown. We intended to study the causal effect between leptin and NAFLD using the Mendelian randomization (MR) study. Methods We performed a two-sample Mendelian randomization (TSMR) analysis using summary GWAS data from leptin (up to 50,321 individuals) and NAFLD (8,434 cases and 770,180 controls) in a European population. Instrumental variables (IVs) that satisfied the three core assumptions of Mendelian randomization were selected. The TSMR analysis was conducted using the inverse variance weighted (IVW) method, MR-Egger regression method, and weighted median (WM) method. To ensure the accuracy and stability of the study results, heterogeneity tests, multiple validity tests, and sensitivity analyses were conducted. Results The findings of the TSMR correlation analysis between NAFLD and leptin were as follows: IVW method (odds ratio (OR) 0.6729; 95% confidence interval (95% CI) 0.4907–0.9235; P = 0.0142), WM method (OR 0.6549; 95% CI 0.4373–0.9806; P = 0.0399), and MR-Egger regression method (P = 0.6920). Additionally, the findings of the TSMR correlation analysis between NAFLD and circulating leptin levels adjusted for body mass index (BMI) were as follows: IVW method (OR 0.5876; 95% CI 0.3781–0.9134; P = 0.0181), WM method (OR 0.6074; 95% CI 0.4231–0.8721; P = 0.0069), and MR-Egger regression method (P = 0.8870). It has also been shown that higher levels of leptin are causally linked to a lower risk of developing NAFLD, suggesting that leptin may serve as a protective factor for NAFLD. Conclusions Using TSMR analysis and the GWAS database, we investigated the genetic relationship between elevated leptin levels and lowered risk of NAFLD in this study. However, further research is required to understand the underlying mechanisms

Design of a Femtosecond Laser Percussion Drilling Process for Ni-Based Superalloys Based on Machine Learning and the Genetic Algorithm

Femtosecond laser drilling is extensively used to create film-cooling holes in aero-engine turbine blade processing. Investigating and exploring the impact of laser processing parameters on achieving high-quality holes is crucial. The traditional trial-and-error approach, which relies on experiments, is time-consuming and has limited optimization capabilities for drilling holes. To address this issue, this paper proposes a process design method using machine learning and a genetic algorithm. A dataset of percussion drilling using a femtosecond laser was primarily established to train the models. An optimal method for building a prediction model was determined by comparing and analyzing different machine learning algorithms. Subsequently, the Gaussian support vector regression model and genetic algorithm were combined to optimize the taper and material removal rate within and outside the original data ranges. Ultimately, comprehensive optimization of drilling quality and efficiency was achieved relative to the original data. The proposed framework in this study offers a highly efficient and cost-effective solution for optimizing the femtosecond laser percussion drilling process

Sustaining Biomaterials in Bioeconomy: Roles of Education and Learning in Mekong River Basin

The demands to improve the livelihood of small farmers require a systemic shift from fossil fuel-based and destructive approaches to sustainable renewable raw materials and non-destructive approaches. This should be accompanied by a fundamental reorganization of education and learning policies to create new bio-oriented value chains for biomaterials, food, wood, and energy, as well as in large parts of the health, manufacturing, and service industries. In the long run, the successful implementation of bio-oriented production depends on the systemic linking of both first- and second-hand learning in communities in rural as well as urban settings. The purpose of this paper is to present a concept for the co-design of a new curriculum to better equip new graduates with the ability to support the effort of the sustainable production of biomaterials that are non-destructive to the environment. To sustain biomaterials and enhance non-destructive ways of thinking, learning needs a community of practice in both online and onsite platforms—allowing students to better understand and support cascade use. Therefore, the use of by-products and recycling products after use will increase in importance. A community of practice, and institutions, must create education and learning platforms for improved actions regarding biomaterials across generations and experiences, which will subsequently be integrated into the circular value chains of the bioeconomy. The first- and second-hand learning to sustain these value chains depends on higher education and learning institutions with both legal mandates and systems approaches

Sustaining Biomaterials in Bioeconomy: Roles of Education and Learning in Mekong River Basin

The demands to improve the livelihood of small farmers require a systemic shift from fossil fuel-based and destructive approaches to sustainable renewable raw materials and non-destructive approaches. This should be accompanied by a fundamental reorganization of education and learning policies to create new bio-oriented value chains for biomaterials, food, wood, and energy, as well as in large parts of the health, manufacturing, and service industries. In the long run, the successful implementation of bio-oriented production depends on the systemic linking of both first- and second-hand learning in communities in rural as well as urban settings. The purpose of this paper is to present a concept for the co-design of a new curriculum to better equip new graduates with the ability to support the effort of the sustainable production of biomaterials that are non-destructive to the environment. To sustain biomaterials and enhance non-destructive ways of thinking, learning needs a community of practice in both online and onsite platforms—allowing students to better understand and support cascade use. Therefore, the use of by-products and recycling products after use will increase in importance. A community of practice, and institutions, must create education and learning platforms for improved actions regarding biomaterials across generations and experiences, which will subsequently be integrated into the circular value chains of the bioeconomy. The first- and second-hand learning to sustain these value chains depends on higher education and learning institutions with both legal mandates and systems approaches

Residential Green and Blue Spaces and Type 2 Diabetes Mellitus: A Population-Based Health Study in China

Evidence on the health benefits of green space in residential environments is still limited, and few studies have investigated the potential association between blue space and type 2 diabetes mellitus (T2DM) prevalence. This study included 39,019 participants who had completed the baseline survey from the Henan Rural Cohort Study, 2015–2017. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were employed to characterize the residential green space, and the distance from the participant’s residential address to the nearest water body was considered to represent the residential blue space. Mixed effect models were applied to evaluate the associations of the residential environment with T2DM and fasting blood glucose (FBG) levels. An interquartile range (IQR) increase in NDVI and EVI was significantly associated with a 13.4% (odds ratio (OR): 0.866, 95% Confidence interval (CI): 0.830,0.903) and 14.2% (OR: 0.858, 95% CI: 0.817,0.901) decreased risk of T2DM, respectively. The residential green space was associated with lower fasting blood glucose levels in men (%change, −2.060 in men vs. −0.972 in women) and the elderly (%change, −1.696 in elderly vs. −1.268 in young people). Additionally, people who lived more than 5 km from the water body had a 15.7% lower risk of T2DM (OR: 0.843, 95% CI: 0.770,0.923) and 1.829% lower fasting blood glucose levels (95% CI: −2.335%,−1.320%) than those who lived closer to the blue space. Our findings suggest that residential green space was beneficially associated with T2DM and fasting blood glucose levels. However, further research is needed to explore more comprehensively the relationship between residential blue space and public health