Does digital economy development reduce carbon emission intensity?

Carbon emissions from human activities are the main cause of climate warming. Under the background of economic and social digital transformation, accurately assessing the carbon emission reduction effect of the development of the digital economy is of great significance for countries to deal with climate warming in the post-COVID-19 era. This paper constructs a dynamic evaluation model of orthogonal projection to measure the level of digital economy development at the provincial level in China from 2007 to 2019. On this basis, the panel fixed effects model and mediation model are used to empirically test the impact of digital economy development on carbon emission intensity and its mechanism. The results indicate that: (1) The development of China’s digital economy is unbalanced among regions, showing a geospatial pattern of decreasing from east to west. (2) China’s carbon emission intensity has a trend of decreasing year by year, and there are geospatial differences of “high in the west and low in the east” and “high in the north and low in the south.” (3) The digital economy development can effectively reduce regional carbon emission intensity through industrial structure optimization effect and resource allocation effect, and the industrial structure optimization effect can suppress carbon emission intensity more obviously. (4) The development of digital economy in different regions has different degrees of reducing carbon emission intensity. The development of digital economy in the eastern region has a stronger inhibitory effect on carbon emission intensity than that in the middle and western regions, and the development of digital economy in economically developed regions can suppress carbon emission intensity more. This paper provides enlightenment for policy makers to deal with climate warming

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

Abstract-Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation models. In this paper a parameter calibration method of microscopic traffic flow simulation models based on orthogonal genetic algorithm is presented. In order to improve the capacity of locating a possible solution in solution space, the proposed method incorporates the orthogonal experimental design method into the genetic algorithm. The proposed method is applied to an arterial section of Ronghua Road in Beijing. Through comparing with the parameter calibration method based on genetic algorithm, the advantage of the proposed method is shown

PTPRO-related CD8⁺ T-cell signatures predict prognosis and immunotherapy response in patients with breast cancer

Background: Poor immunogenicity and extensive immunosuppressive T-cell infiltration in the tumor immune microenvironment (TIME) have been identified as potential barriers to immunotherapy success in “immune-cold” breast cancers. Thus, it is crucial to identify biomarkers that can predict immunotherapy efficacy. Protein tyrosine phosphatase receptor type O (PTPRO) regulates multiple kinases and pathways and has been implied to play a regulatory role in immune cell infiltration in various cancers. Methods: ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) were performed to uncover the TIME landscape. The correlation analysis of PTPRO and immune infiltration was performed to characterize the immune features of PTPRO. Univariate and multivariate Cox analyses were applied to determine the prognostic value of various variables and construct the PTPRO-related CD8+ T-cell signatures (PTSs). The Kaplan–Meier curve and the receiver operating characteristic (ROC) curve were used to estimate the performance of PTS in assessing prognosis and immunotherapy response in multiple validation datasets. Results: High PTPRO expression was related to high infiltration levels of CD8+ T cells, as well as macrophages, activated dendritic cells (aDCs), tumor-infiltrating lymphocytes (TILs), and Th1 cells. Given the critical role of CD8+ T cells in the TIME, we focused on the impact of PTPRO expression on CD8+ T-cell infiltration. The prognostic PTS was then constructed using the TCGA training dataset. Further analysis showed that the PTS exhibited favorable prognostic performance in multiple validation datasets. Of note, the PTS could accurately predict the response to immune checkpoint inhibitors (ICIs). Conclusion: PTPRO significantly impacts CD8+ T-cell infiltration in breast cancer, suggesting a potential role of immunomodulation. PTPRO-based PTS provides a new immune cell paradigm for prognosis, which is valuable for immunotherapy decisions in cancer patients

Enhanced HMGB1 Expression May Contribute to Th17 Cells Activation in Rheumatoid Arthritis

Rheumatoid arthritis(RA) is a common autoimmune disease associated with Th17 cells, but what about the effect of high-mobility group box chromosomal protein 1 (HMGB1) and the relationship between Th17-associated factors and HMGB1 in RA remains unknown. In the present study, we investigated the mRNA levels of HMGB1, RORγt, and IL-17 in peripheral blood mononuclear cells (PBMCs) from patients with rheumatoid arthritis by quantitative real-time PCR (RT-qPCR), and the concentrations of HMGB1, IL-17, and IL-23 in plasma were detected by ELISA. And then, the effect of HMGB1 on Th17 cells differentiation was analyzed in vitro. Our clinical studies showed that the mRNAs of HMGB1, RORγt, and IL-17 in patients were higher than that in health control (P < 0.05), especially in active RA patients (P < 0.05). The plasma HMGB1, IL-17, and IL-23 in RA patients were also higher than that in health control (P < 0.05); there was a positive correlation between the expression levels of HMGB1 and the amount of CRP, ERS, and RF in plasma. In vitro, the IL-17-produced CD4+T cells were increased with 100 ng/mL rHMGB1 for 12h, which indicated that the increased HMGB1 might contribute to Th17 cells activation in RA patients

Role of Positive Selection in Functional Divergence of Mammalian Neuronal Apoptosis Inhibitor Proteins during Evolution

Neuronal apoptosis inhibitor proteins (NAIPs) are members of Nod-like receptor (NLR) protein family. Recent research demostrated that some NAIP genes were strongly associated with both innate immunity and many inflammatory diseases in humans. However, no similar phenomena have been reported in other mammals. Furthermore, some NAIP genes have undergone pseudogenization or have been lost during the evolution of some higher mammals. We therefore aimed to determine if functional divergence had occurred, and if natural selection had played an important role in the evolution of these genes. The results showed that NAIP genes have undergone pseudogenization and functional divergence, driven by positive selection. Positive selection has also influenced NAIP protein structure, resulting in further functional divergence

De Novo Transcriptome of Safflower and the Identification of Putative Genes for Oleosin and the Biosynthesis of Flavonoids

Safflower (Carthamus tinctorius L.) is one of the most extensively used oil crops in the world. However, little is known about how its compounds are synthesized at the genetic level. In this study, Solexa-based deep sequencing on seed, leaf and petal of safflower produced a de novo transcriptome consisting of 153,769 unigenes. We annotated 82,916 of the unigenes with gene annotation and assigned functional terms and specific pathways to a subset of them. Metabolic pathway analysis revealed that 23 unigenes were predicted to be responsible for the biosynthesis of flavonoids and 8 were characterized as seed-specific oleosins. In addition, a large number of differentially expressed unigenes, for example, those annotated as participating in anthocyanin and chalcone synthesis, were predicted to be involved in flavonoid biosynthesis pathways. In conclusion, the de novo transcriptome investigation of the unique transcripts provided candidate gene resources for studying oleosin-coding genes and for investigating genes related to flavonoid biosynthesis and metabolism in safflower

A general thermal model of machine tool spindle

As the core component of machine tool, the thermal characteristics of the spindle have a significant influence on machine tool running status. Lack of an accurate model of the spindle system, particularly the model of load–deformation coefficient between the bearing rolling elements and rings, severely limits the thermal error analytic precision of the spindle. In this article, bearing internal loads, especially the function relationships between the principal curvature difference F ( ρ ) and auxiliary parameter n δ , semi-major axis a , and semi-minor axis b , have been determined; furthermore, high-precision heat generation combining the heat sinks in the spindle system is calculated; finally, an accurate thermal model of the spindle was established. Moreover, a conventional spindle with embedded fiber Bragg grating temperature sensors has been developed. By comparing the experiment results with simulation, it indicates that the model has good accuracy, which verifies the reliability of the modeling process