Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

PD-L1 translocation to the plasma membrane enables tumor immune evasion through MIB2 ubiquitination.

Programmed death-ligand 1 (PD-L1), a critical immune checkpoint ligand, is a transmembrane protein synthesized in the endoplasmic reticulum of tumor cells and transported to the plasma membrane to interact with programmed death 1 (PD-1) expressed on T cell surface. This interaction delivers coinhibitory signals to T cells, thereby suppressing their function and allowing evasion of antitumor immunity. Most companion or complementary diagnostic devices for assessing PD-L1 expression levels in tumor cells used in the clinic or in clinical trials require membranous staining. However, the mechanism driving PD-L1 translocation to the plasma membrane after de novo synthesis is poorly understood. Herein, we showed that mind bomb homolog 2 (MIB2) is required for PD-L1 transportation from the trans-Golgi network (TGN) to the plasma membrane of cancer cells. MIB2 deficiency led to fewer PD-L1 proteins on the tumor cell surface and promoted antitumor immunity in mice. Mechanistically, MIB2 catalyzed nonproteolytic K63-linked ubiquitination of PD-L1, facilitating PD-L1 trafficking through Ras-associated binding 8-mediated (RAB8-mediated) exocytosis from the TGN to the plasma membrane, where it bound PD-1 extrinsically to prevent tumor cell killing by T cells. Our findings demonstrate that nonproteolytic ubiquitination of PD-L1 by MIB2 is required for its transportation to the plasma membrane and tumor cell immune evasion.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation.

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

Mediating role of stressful life events between sleep chronotype and depressive mood in adolescents / 中国学校卫生

Objective@#A longitudinal mediating model was designed to explore the relationship between sleep chronotype and depressive mood with stressful life events as the mediating variable, so as to provide reference basis for preventing depression in adolescents.@*Methods@#From March (T1) to October (T2) 2022, the Munich Chronotype Questionnaire, and Adolescent Self-rating Life Events Checklist and Center for Epidemiologic Studies Depression Scale were administered in 1 251 middle school students in the first and second grades of a middle school in Meishan, Sichuan Province for a follow up study. Two wave of date on sleep time type, stressful life events, and depressive moods were collected.@*Results@#At T1, the detection rate of depressive mood was 22.5% in adolescents but 27.0% at T2.At the same time point, there were significant positive correlations between chronotype, stressful life events and depressive mood ( r=0.06-0.69, P <0.05). Cross lagged model results showed that early sleep chronotype could significantly predict later depressive mood ( β=0.53, P <0.05), and early depressive mood could significantly predict later sleep chronotype ( β=0.01, P <0.01). Longitudinal mediation model analysis showed that T1 sleep chronotype not only directly affected T2 depressive mood (direct effect value=1.421, 95% CI =0.985- 1.858 , P <0.01), but also affect T2 depressive mood through the partial mediating effects of T2 stressful life events (indirect effect value=1.268, 95% CI =0.810-1.726, P <0.05). Although T1 depressive mood was a direct predictor of T2 sleep chronotype (direct effect value=0.025, 95% CI =0.017-0.032), it was not a direct predictor of T2 sleep chronotype by partial intermediate action of T2 stressful life events (indirect effect value=-0.001, 95% CI = -0.006- 0.004, P >0.05).@*Conclusions@#Sleep chronotype can not only directly predict depressive mood of adolescents, but also indirectly affect depressive mood of adolescents through the longitudinal mediating effect of stressful life events. To prevent depression and improve mental health in this population, adolescents should develop the habit of early sleep schedule, and improve their ability to cope with stressful life events.

The complete chloroplast genome sequence of Hordeum distichon (Poales: Poaceae).

Hordeum distichon (H. distichon) is a two-row cultivated barley used as food and as a feed crop. Chloroplast genome is an excellent way to study the genetic structure and evolutionary process of natural population of plant species in recent years. In this study, the complete chloroplast genome of H. distichon was sequenced and analyzed: the size of the chloroplast genome is 136,462 bp in length, including a large single copy region (LSC) of 80,597 bp, a small single copy region (SSC) of 12,701 bp, and a pair of inverted repeated regions (IR) of 21,582 bp; the H. distichon chloroplast genome encodes 129 genes, including 83 protein-coding genes, 38 tRNA genes, and eight rRNA genes; the overall GC-content of the chloroplast genome was 38.32%, with the LSC, SSC, and IR regions being 36.31%, 32.33%, and 43.83%, respectively. Phylogenetic analysis based on 32 species with the maximum likelihood (ML) method indicated that H. distichon was closely related to Hordeum vulgare.