A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China.

In spite of the state-of-the-art performances of machine learning in the PM2.5 estimation, the high-value PM2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM2.5 with R2 of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM2.5 concentrations, the spatial evolution of PM2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM2.5 (PM2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM2.5 (PM2.5_D) were CO, AOD and NO2. This study not only provided an effective solution to the PM2.5 underestimation and AOD missing problems in the PM2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM2.5 and some spatiotemporal variables.

Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State.

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.

Co-effect assessment on regional air quality: A perspective of policies and measures with greenhouse gas reduction potential.

Clean air policies have achieved remarkable air quality improvement in China for the last decade. However, as more importance was attached to climate issues and further improvement of air quality, policies with greenhouse gas (GHG) reduction potential were supposed to play a significant role. Here, we designed a conventional legislation pathway scenario (CLP) and an enhanced greenhouse gas reduction scenario (EGR), to estimate the co-effects of policies effective in GHG reduction on air pollutant control and air quality improvement in the Yangtze River Delta (YRD) region from 2014 to 2020, adopting a measure-specific evaluation method and an integrated WRF-CAMx model simulation. Results showed that: 1) With the implementation of enhanced measures with GHG reduction potential, emissions of SO2, NOx, PM2.5, PM10, VOCs and NH3 decreased by 16.4 %, 21.6 %, 18.6 %, 16.5 %, 23.9 % and 15.4 % in EGR scenario respectively, compared with CLP scenario. And the annual mean simulated concentrations of PM2.5, SO2 and NO2 of the YRD decreased by 11.2 %, 15.4 % and 20.6 %, respectively. 2) The average 8-h maxima (MDA8) concentration of O3 presented a slightly increasing trend under the impacts of measures with GHG reduction potential, which might be on account of the unbalanced control of NOx and VOCs, the two major precursors of O3. 3) Based on the source apportionment analysis, major partition of total ozone in the four receptors in YRD was from regional transportation, rather than local formation. And the major sectors contributing to ozone were industry and transportation sector. This study quantitatively assessed the co-benefits of GHG-control-effective policies and specific measures on air quality improvement, which would help to provide implications for future policy-making to achieve air pollution and climate change co-control.

Vascular Smooth Muscle Cells Phenotypic Switching in Cardiovascular Diseases.

Vascular smooth muscle cells (VSMCs), the major cell type in the arterial vessel wall, have a contractile phenotype that maintains the normal vessel structure and function under physiological conditions. In response to stress or vascular injury, contractile VSMCs can switch to a less differentiated state (synthetic phenotype) to acquire the proliferative, migratory, and synthetic capabilities for tissue reparation. Imbalances in VSMCs phenotypic switching can result in a variety of cardiovascular diseases, including atherosclerosis, in-stent restenosis, aortic aneurysms, and vascular calcification. It is very important to identify the molecular mechanisms regulating VSMCs phenotypic switching to prevent and treat cardiovascular diseases with high morbidity and mortality. However, the key molecular mechanisms and signaling pathways participating in VSMCs phenotypic switching have still not been fully elucidated despite long-term efforts by cardiovascular researchers. In this review, we provide an updated summary of the recent studies and systematic knowledge of VSMCs phenotypic switching in atherosclerosis, in-stent restenosis, aortic aneurysms, and vascular calcification, which may help guide future research and provide novel insights into the prevention and treatment of related diseases.

Shear-Induced ITGB4 Promotes Endothelial Cell Inflammation and Atherosclerosis.

The local heterogeneity in the distribution of atherosclerotic lesions is caused by local flow patterns. The integrin family plays crucial regulatory roles in diverse biological processes, but knowledge of integrin ß4 (ITGB4) in shear stress-induced atherosclerosis is limited. This study clarified that low shear stress (LSS) regulates the generation of ITGB4 in endothelial cells with atheroprone phenotype to identify ITGB4's role in atherosclerosis. We found that LSS led to an increase in ITGB4 protein expression both in vitro and in vivo. ITGB4 knockdown attenuated inflammation and ROS generation in human umbilical vein endothelial cells (HUVECs) and reduced atherosclerotic lesion areas in ApoE-/- mice fed with HFD, largely independent of effects on the lipid profile. Mechanistically, ITGB4 knockdown altered the phosphorylation levels of SRC, FAK, and NFκB in HUVECs under LSS conditions. In addition, the knockdown of NFκB inhibited the production of ITGB4 and SRC phosphorylation, and the knockdown of SRC downregulated ITGB4 protein expression and NFκB activation. These data demonstrate a critical role of ITGB4 in atherosclerosis via modulation of endothelial cell inflammation, and ITGB4/SRC/NFκB might form a positive feedback loop in the regulation of endothelial cell inflammation.

Rapid Mouse Follicle Stimulating Hormone Quantification and Estrus Cycle Analysis Using an Automated Microfluidic Chemiluminescent ELISA System.

Follicle stimulating hormone (FSH) plays a critical role in female reproductive development and homeostasis. The blood/serum concentration of FSH is an important marker for reporting multiple endocrinal functions. The standardized method for mouse FSH (mFSH) quantification based on radioimmunoassay (RIA) suffers from long assay time (∼2 days), relatively low sensitivity, larger sample volume (60 µL), and small dynamic range (2-60 ng/mL); thus, it is insufficient for monitoring fast developing events with relatively small mFSH fluctuations (e.g., estrous cycles of mammals). Here, we developed an automated microfluidic chemiluminescent ELISA device along with the disposal sensor array and the corresponding detection protocol for rapid and quantitative analysis of mFSH from mouse tail serum samples. With this technology, highly sensitive quantification of mFSH can be accomplished within 30 min using only 8 µL of the serum sample. It is further shown that our technique is able to generate results comparable to RIA but has a significantly improved dynamic range that covers 0.5-250 ng/mL. The performance of this technology was evaluated with blood samples collected from ovariectomized animals and animals with reimplanted ovarian tissues, which restored ovarian endocrine function and correlated with estrus cycle analysis study.