Solanesol Ameliorates Anxiety-like Behaviors via the Downregulation of Cingulate T Cell-Restricted Intracellular Antigen-1 in a Complete Freund's Adjuvant-Induced Mouse Model.

Anxiety disorder is a universal disease related to neuro-inflammation. Solanesol has shown positive effects because of its anti-inflammatory, anti-tumor, and anti-ulcer properties. This study focused on determining whether solanesol could ameliorate anxiety-like behaviors in a mouse model of neuro-inflammation and identify its working targets. Complete Freund's adjuvant (CFA)-induced mice that were intra-peritoneally administered with solanesol (50 mg/kg) for 1 week showed a statistically significant reduction in anxiety-like behaviors, as measured by open field and elevated plus-maze tests. Western blot analysis revealed that CFA-induced upregulation of the levels of pro-inflammatory cytokines interleukin (IL)-1ß and tumor necrosis factor α (TNF-α), which played crucial roles in regulating anxiety, returned to normal in the anterior cingulate cortex (ACC) after solanesol treatment. The level of T cell-restricted intracellular antigen-1 (TIA1), a key component of stress granules, also decreased in the ACC. Moreover, immunofluorescence results indicated that solanesol suppressed CFA-induced microglial and astrocytic activation in the ACC. CFA was injected in the hind paws of TIA1Nestin conditional knockout (cKO) mice to confirm whether TIA1 is a potential modulatory molecule that influences pro-inflammatory cytokines and anxiety-like behaviors. Anxiety-like behaviors could not be observed in cKO mice after CFA injection with IL-1ß and TNF-α levels not remarkedly increasing. Our findings suggest that solanesol inhibits neuro-inflammation by decreasing the TIA1 level to reduce IL-1ß and TNF-α expression, meanwhile inhibiting microglial and astrocytic activation in the ACC and ultimately ameliorating anxiety-like behaviors in mice.

Particulate matter emissions from light-duty gasoline vehicles under different ambient temperatures: Physical properties and chemical compositions.

Fine particulate matter (PM2.5) from vehicle exhaust is typically emitted at breathing height and thus imposes severe adverse effects on human health and air quality. However, there is currently limited knowledge on the characteristics of PM2.5 in exhaust, specifically its chemical components, at different ambient temperatures. Particulate emissions from typical light-duty gasoline vehicles (LDGVs) were investigated on a chassis dynamometer according to the Worldwide Harmonized Light-Duty Test Cycle at ambient temperatures of 38 °C, 28 °C, 15 °C, 5 °C and - 7 °C. The results showed a significant increase in particulate mass (PM) and particle number (PN) emissions with decreasing ambient temperature, particularly during cold starts below 5 °C. The particle size distributions exhibited distinct bimodal patterns, with accumulation-mode (AM) particles (60-125 nm) dominating the gasoline direct injection (GDI) distribution and nucleation-mode (NM) particles (8-12 nm) dominating the port fuel injection (PFI) distribution. AM particles were more temperature-sensitive than NM particles. Lower temperatures produced higher emissions of elements, carbonaceous components, and large-ring polycyclic aromatic hydrocarbons, while water-soluble ions showed an opposite trend. The total toxic equivalent, primarily influenced by benzo[a]pyrene, was significantly higher at -7 °C. The penalty distribution of LDGV PM and PN, defined by comparing the emissions at the various temperatures to those at regulated temperatures (23-30 °C), exhibited notable temporal heterogeneity (winter > autumn > spring > summer) and spatial heterogeneity (northern China > southern China). These findings are essential for establishing more stringent vehicle emission standards and improving emission models in cold environments.

Chiral Topological Superconductivity in Superconductor-Obstructed Atomic Insulator-Ferromagnetic Insulator Heterostructures.

Implementing topological superconductivity (TSC) and Majorana states (MSs) is one of the most significant and challenging tasks in both fundamental physics and topological quantum computations. In this work, taking the obstructed atomic insulator (OAI) Nb_{3}Br_{8}, s-wave superconductor (SC) NbSe_{2}, and ferromagnetic insulator (FMI) CrI_{3} as an example, we propose a new setup to realize the 2D chiral TSC and MSs in the SC/OAI/FMI heterostructure, which could avoid the subband problem effectively and has the advantage of huge Rashba spin-orbit coupling. As a result, the TSC phase can be stabilized in a wide region of chemical potential and Zeeman splitting, and four distinct TSC phases with superconducting Chern number N=-1,-2,-3, 3 can be achieved. Moreover, a 2D Bogoliubov-de Gennes Hamiltonian based on the triangular lattice of obstructed Wannier charge centers, combined with the s-wave superconductivity paring and Zeeman splitting, is constructed to understand the whole topological phase diagram analytically. These results expand the application of OAIs and pave a new way to realize the TSC and MSs with unique advantages.

High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator.

Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87-0.92 and error indices decreasing by 50-67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.

Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games.

Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized­oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.

Nonlinear effect of NOx concentration decrease on secondary aerosol formation in the Beijing-Tianjin-Hebei region: Evidence from smog chamber experiments and field observations.

During the COVID-19 lockdown in the Beijing-Tianjin-Hebei (BTH) region in China, large decrease in nitrogen oxides (NOx) emissions, especially in the transportation sector, could not avoid the occurrence of heavy PM2.5 pollution where nitrate dominated the PM2.5 mass increase. To experimentally reveal the effect of NOx control on the formation of PM2.5 secondary components (nitrate in particular), photochemical simulation experiments of mixed volatile organic compounds (VOCs) under various NOx concentrations with smog chamber were performed. The proportions of gaseous precursors in the control experiment were comparable to ambient conditions typically observed in the BTH region. Under relatively constant VOCs concentrations, when the initial NOx concentration was reduced to 40% of that in the control experiment (labelled as NOx,0), the particle mass concentration was not significantly reduced, but when the initial NOx concentration decreased to 20 % of NOx,0, the mass concentration of particles as well as nitrate and organics showed a sudden decrease. A "critical point" where the mass concentration of secondary aerosol started to decline as the initial NOx concentration decreased, located at 0.2-0.4 NOx,0 (or 0.18-0.44 NO2,0) in smog chamber experiments. The oxidation capacity and solar radiation intensity played key roles in the mass concentration and compositions of the formed particles. In field observations in the BTH region in the autumn and winter seasons, the "critical point" exist at 0.15-0.34 NO2,0, which coincided mostly with the laboratory simulation results. Our results suggest that a reduction of NOx emission by >60% could lead to significant reductions of secondary aerosol formation, which can be an effective way to further alleviate PM2.5 pollution in the BTH region.

Identification of quality markers for Cyanotis arachnoidea and analysis of its physiological mechanism based on chemical pattern recognition, network pharmacology, and experimental validation.

Cyanotis arachnoidea C. B. Clarke is a traditional Chinese medicinal herb that has a limited clinical use in the treatment of diabetes mellitus (DM) in minority areas of Guizhou in China. However, few prior reports are available on the quality control of Cyanotis arachnoidea, and its quality markers and hypoglycemic mechanism are still unclear. The purpose of this study is to explore the quality markers (Q-markers) of Cyanotis arachnoidea and predict its hypoglycemic mechanism. In this study, ultra-high-performance liquid chromatography (UHPLC) fingerprint combined with chemical pattern recognition were performed, and four differential components were screened out as quality markers, including 20-Hydroxyecdysone, 3-O-acetyl-20-hydroxyecdysone, Ajugasterone C, and 2-O-acetyl-20-hydroxyecdysone. Network pharmacology analysis revealed 107 therapeutic target genes of Cyanotis arachnoidea in DM treatment, and the key targets were Akt1, TNF, IL-6, MAPK3, and JUN. The hypoglycemic mode of action of Cyanotis arachnoidea may be mediated by tumor necrosis factor (TNF) signaling, cancer, insulin resistance, and JAK-STAT pathways. Molecular docking analysis disclosed that the foregoing quality markers effectively bound their key target genes. An in vitro experiment conducted on pancreatic islet ß-cells indicated that the forenamed active components of Cyanotis arachnoidea had hypoglycemic efficacy by promoting PI3K/Akt and inhibiting MAPK signaling. UHPLC also accurately quantified the quality markers. The identification and analysis of quality markers for Cyanotis arachnoidea is expected to provide references for the establishment of a quality control evaluation system and clarify the material basis and hypoglycemic mechanisms of this traditional Chinese medicine (TCM).

Complex temperature dependence of vehicular emissions: Evidence from a global meta-analysis.

The significant impact of low ambient temperature, which was less regulated, on vehicle exhaust emissions had garnered considerable attention. This study investigated the impact of ambient temperature on exhaust emissions based on the global meta-analysis. The estimated sizes (mean difference, MDt) of 11 exhaust pollutants were quantified with 1795 observations at low ambient temperatures (LATs, -18 °C to -7 °C) versus warm ambient temperatures (WATs, 20 °C-30 °C). The results indicated a strong and positive effect of LATs on vehicular emissions, with the average ratio of vehicular emission factors at LATs to those at WATs (EFLAT/EFWAT) ranging from 1.14 to 3.84. Oil-based subgroup analysis indicated a quite large MDt [NOx] of diesel engines (12.42-15.10 mg km-1·k-1). Particulate emissions were 0.22-1.41 mg km-1·k-1 enhanced during cold-start tests at LATs. The application of particulate filters on motor vehicles greatly reduced the impact of ambient temperature on tailpipe particulate emissions, at the expense of induced NOx emissions. During the Federal Test Procedure (FTP-75), exhaust emissions showed higher temperature dependence compared to the averaged levels (1.31-39.31 times). Locally weighted regression was used to determine exhaust temperature profiles, revealing that gasoline vehicles emitted more particulates at LATs, while diesel vehicles showed the opposite trend. Given the widespread use of motor vehicles worldwide, future motor vehicle emission standards should include tighter limits on exhaust emissions at LATs.

Efficacy and safety of methylphenidate and ginseng in cancer-related fatigue: a network meta-analysis of randomized controlled trials.

Background: Incidence of cancer-related fatigue (CRF), which can persist 5 to 10 years, is nearly 85% in cancer patients. It severely affects the quality of life and is strongly associated with poor prognosis. As clinical trial data on CRF treated with methylphenidate and ginseng, two potential medicines, has been accumulating, an updated meta-analysis was performed to evaluate and compare the efficacy and safety of the two medicines in CRF. Methods: Randomized controlled trials that investigated methylphenidate or ginseng in the treatment of CRF were identified through a literature search. The primary outcome was CRF relief. Standardized mean difference (SMD) was used to analyze the effect. Results: Eight studies on methylphenidate were included and the pooled SMD was 0.18 [95% confidence interval (95% CI): -0.00 to 0.35, P=0.05]. Five studies on ginseng were included and the SMD was 0.32 (95% CI: 0.17-0.46, P<0.0001). Results of network meta-analysis showed that the order was ginseng, methylphenidate, placebo from high efficacy to low and ginseng was significantly better than methylphenidate (SMD =0.23, 95% CI: 0.01-0.45). Incidences of insomnia and nausea caused by ginseng were significantly lower than those caused by methylphenidate (P<0.05). Conclusions: Both methylphenidate and ginseng can significantly ameliorate CRF. Ginseng may be superior to methylphenidate because ginseng may be more effective and might cause less adverse events. Head-to-head trials with fixed protocol are warranted to identify the optimal medical strategy.

[Emission Inventory of Building Material Industry in Henan Province Based on Multi-source Data Integration].

The building materials industry is a typical resource and energy-consuming industry, as well as one of the major sources of air pollution. As the world's largest producer and consumer of building material products, China thus far has insufficient research on the emissions of the building materials industry, and the data sources are short of multiplicity. In this study, the building materials industry in Henan Province was chosen,and the control measures inventory for pollution emergency response (CMIPER) was applied to the development of the emission inventory for the first time. Through the integration of multi-source data such as CMIPER, a pollution discharge permit, and environmental statistics, the activity data of the building materials industry was refined, and a more accurate emission inventory of the building materials industry in Henan Province was established. The results showed that the SO2, NOx, primary PM2.5, and PM10 emissions of the building materials industry in Henan Province in 2020 were 21788, 51427, 10107, and 14471 t, respectively. Cement and bricks and tiles were the two categories with the highest contribution of emissions from the building materials industry in Henan Province, accounting for more than 50% in total. TheNOx emission of the cement industry was a key issue, and the overall emission control level of the brick and tile industry was relatively unadvanced. The central and northern parts of Henan Province contributed the most emissions in the building materials industry, accounting for more than 60%. It is recommended to further implement ultra-low emission retrofit in the cement industry, and for other industries such as the bricks and tiles, the improvement of local emission standards is encouraged to persistently promote the emission control of the building materials industry.