Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015-2022 in a Coastal City in Eastern China.

Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emissions, which has led to rapid improvement in air quality over China. Here, we evaluated the impact of anthropogenic emissions and meteorological conditions on trends in air pollutants in a coastal city (Lianyungang) in eastern China from 2015 to 2022 based on a random forest model. The annual mean concentration of observed air pollutants, including fine particles, inhalable particles, sulfur dioxide, nitrogen dioxide, and carbon monoxide, presented significant decreasing trends during 2015-2022, with dominant contributions (55-75%) by anthropogenic emission reduction. An increasing trend in ozone was observed with an important contribution (28%) by anthropogenic emissions. The impact of meteorological conditions on air pollution showed significant seasonality. For instance, the negative impact on aerosol pollution occurred during cold months, while the positive impact was in warm months. Health-risk-based air quality decreased by approximately 40% in 8 years, for which anthropogenic emission made a major contribution (93%).

Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022.

The Tibetan Plateau (known as the Earth's Third Pole) has significant impact on climate. Fine particulate matter (PM2.5) is an important air pollutant in this region and has significant impact on health and climate. To mitigate PM2.5 air pollution over China, a series of clean air actions has been implemented. However, interannual trends in particulate air pollution and its response to anthropogenic emissions in the Tibetan Plateau are poorly understood. Here, we applied a random forest (RF) algorithm to quantify drivers of PM2.5 trends in six cities of the Tibetan Plateau from 2015 to 2022. The decreasing trends (-5.31 to -0.73 µg m-3 a-1) in PM2.5 during 2015-2022 were observed in all cities. The RF weather-normalized PM2.5 trends - which were driven by anthropogenic emissions - were -4.19 to -0.56 µg m-3 a-1, resulting in dominant contributions (65 %-83 %) to the observed PM2.5 trends. Relative to 2015, such anthropogenic emission driver was estimated to contribute -27.12 to -3.16 µg m-3 to declines in PM2.5 concentrations in 2022. However, the interannual changes in meteorological conditions only made a small contribution to the trends in PM2.5 concentrations. Potential source analysis suggested biomass burning from local residential sector and/or long-range transports originated from South Asia could significantly promote PM2.5 air pollution in this region. Based on health-risk air quality index (HAQI) assessment, the HAQI value was decreased by 15 %-76 % between 2015 and 2022 in these cities, with significant contributions (47 %-93 %) from anthropogenic emission abatements. Indeed, relative contribution of PM2.5 to the HAQI was decreased from 16 %-30 % to 11 %-18 %, while increasing and significant contribution from ozone was observed, highlighting that further effective mitigation of both PM2.5 and ozone air pollution could obtain more substantial health benefits in the Tibetan Plateau.

CircITGA7 Suppresses Gastric Cancer Progression Through miR-1471/MTDH Axis.

In recent years, there have been reports about the involvement of circular RNAs (circRNAs) in the pathogenesis of gastric cancer (GC), but the molecular mechanism in cell proliferation, invasion, and migration is still unclear. Based on The Cancer Genome Atlas (TCGA) database, we analyzed differentially expressed circRNAs between GC and non-tumor tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were used to clarify the functional role in GC. Here, we showed that circITGA7 was lowly expressed in GC tissues based on the TCGA database. In vitro, silencing the expression of circITGA7 increased cell proliferation and metastasis, whereas overexpression did the opposite. Mechanistically, miR-1471 has circITGA7 as a sponge, and miR-1471 has metadherin (MTDH) as a target gene. Consequently, functional analysis showed that the tumor suppressor effect of circITGA7 was the result of regulating the miR-1471/MTDH axis. Overall, the circITGA7/miR-1471/MTDH signaling pathway may play a crucial role in GC, providing a new potential mechanism involved in GC progression.

Numerical Investigation on the Effects of Dielectric Barrier on a Nanosecond Pulsed Surface Dielectric Barrier Discharge.

In order to understand the impacts of dielectric barrier on the discharge characteristics of a nanosecond pulsed surface dielectric barrier discharge (NS-DBD), the effects of dielectric constant and dielectric barrier thickness are numerically investigated by using a three-equation drift-diffusion model with a 4-species 4-reaction air chemistry. When the dielectric constant increases, while the dielectric barrier thickness is fixed, the streamer propagation speed (V), the maximum streamer length (L), the discharge energy ( Q D _ e i ), and the gas heating ( Q G H ) of a pulse increase, but the plasma sheath thickness (h), the fast gas heating efficiency η , and the charge densities on the wall surface decrease. When the dielectric barrier thickness increases, while the dielectric constant is fixed, V, L, Q D _ e i , and Q G H of a pulse decrease, but h, η , and the charge densities on the wall surface increase. It can be concluded that the increase of the dielectric constant or the decrease of the dielectric barrier thickness results in the increase of the capacitance of the dielectric barrier, which enhances the discharge intensity. Increasing the dielectric constant and thinning the dielectric barrier layer improve the performance of the NS-DBD actuators.