Integration of scRNA-Seq and bulk RNA-Seq uncover perturbed immune cell types and pathways of Kawasaki disease

IntroductionKawasaki disease (KD) is an acute febrile illness primarily affecting children and characterized by systemic inflammation and vasculitis that can lead to coronary artery complications. The aim of this study was to gain a comprehensive understanding of immune dysregulation in KD.MethodsTo this end, we employed integration of single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data. Furthermore, we conducted flow cytometry analysis for a cohort of 82 KD patients.ResultsOur analysis revealed significant heterogeneity within immune cell populations in KD patients, with distinct clusters of T cells, B cells, and natural killer (NK) cells. Importantly, CD4+ naïve T cells in KD patients were found to predominantly differentiate into Treg cells and Th2 cells, potentially playing a role in the excessive inflammation and vascular damage characteristic of the disease. Dysregulated signaling pathways were also identified, including the mTOR signaling pathway, cardiomyopathy pathway, COVID-19 signaling pathway, and pathways involved in bacterial or viral infection.DiscussionThese findings provide insights into the immunopathogenesis of KD, emphasizing the importance of immune cell dysregulation and dysregulated signaling pathways. Integration of scRNA-Seq and bulk RNA-Seq data offers a comprehensive view of the molecular and cellular alterations in KD and highlights potential therapeutic targets for further investigation. Validation and functional studies are warranted to elucidate the roles of the identified immune cell types and pathways in KD pathogenesis and to develop targeted interventions to improve patient outcomes

Expected nucleation effects of carboxylic acid salts on poly(1-butene)

9,10-Dihydro-9,10-ethano-anthracene-11,12-dicarboxylic acid disodium salt (DHEAS) was synthesized and used as a nucleating agent for poly(1-butene) (iPB). The isothermal crystallization kinetics of iPB having different nucleating agents were investigated by differential scanning calorimetry (DSC) and polarized optical microscopy (POM). The results showed that the nucleating agents increased the crystallization temperature and the crystallization rate and shortened the crystallization half-time (t1/2). As well, the nucleating agents could be used as heterogeneous nuclei in the iPB matrix and decreased the size of iPB. When the nucleating agent was DHEAS, the crystallization temperature of iPB was up to 93.6°C which was higher than that of other nucleating agents for iPB and pure iPB. The crystallization half-time in the presence of DHEAS was 0.58 min which was less than that of other nucleating agents for iPB and pure iPB. In this case, the spherulitic size of iPB was the smallest and the morphology was changed, which indicated that DHEAS displayed better nucleation effect among the studied nucleating agents

Associations of fecal microbial profiles with breast cancer and non-malignant breast disease in the Ghana Breast Health Study

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and nonmalignant breast disease in a case-control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 nonmalignant breast disease cases, N = 414 population-based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray-Curtis and unweighted/weighted UniFrac distance), and the presence and relative abundance of select taxa with breast cancer and nonmalignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13-0.36; Ptrend < .001). Alpha diversity associations were similar for nonmalignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen-conjugating and immune-related functions were strongly associated with breast cancer (all Ps < .001). There were no statistically significant differences between breast cancer and nonmalignant breast disease cases in any microbiota metric. In conclusion, fecal bacterial characteristics were strongly and similarly associated with breast cancer and nonmalignant breast disease. Our findings provide novel insight into potential microbially-mediated mechanisms of breast disease

A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models

A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models

Efficiency Evaluation of Urban Road Transport and Land Use in Hunan Province of China Based on Hybrid Data Envelopment Analysis (DEA) Models

Urban road transport and land use (RTLU) jointly promote economic development by concentrating labor, material, and capital. This paper presents an integrated RTLU efficiency analysis that explores the degree of coordination between these two systems to provide guidance for future adaptations necessary for sustainable urban development. Both a super efficiency Data Envelopment Analysis model and window analysis were used to spatiotemporally evaluate RTLU efficiency from 2012 to 2016 in 14 cities of Hunan province, central China. The Malmquist index was decomposed into technical efficiency and technology change to reveal reasons for changes in RTLU efficiency. These evaluation results show regional disparities in efficiency across Hunan province, with western cities being the least efficient. Eight cities showed an increasing trend in RTLU efficiency while Yueyang exhibited a decreasing trend. In 13 of 14 regions, productivity improved every year. At the same time, five regions had a decline in technical efficiency even though technical progress increased in all regions. Our analysis shows that greater investment in road transport and urban construction are not enough to ensure sustainable urban growth. Policy must instead promote the full use of current resources according to local conditions to meet local, regional, and national development goals

Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate