Summary
A hallmark of obesity is chronic low‐grade inflammation, which plays a major role in the process of atherosclerotic cardiovascular disease (ACVD). Gut microbiota is one of the factors ...influencing systemic immune responses, and profound changes have been found in its composition and metabolic function in individuals with obesity. This systematic review assesses the association between the gut microbiota and markers of low‐grade inflammation in humans. We identified 14 studies which were mostly observational and relatively small (n = 10 to 471). The way in which the microbiome is analysed differed extensively between these studies. Lower gut microbial diversity was associated with higher white blood cell counts and high sensitivity C‐reactive protein (hsCRP) levels. The abundance of Bifidobacterium, Faecalibacterium, Ruminococcus and Prevotella were inversely related to different markers of low‐grade inflammation such as hsCRP and interleukin (IL)‐6. In addition, this review speculates on possible mechanisms through which the gut microbiota can affect low‐grade inflammation and thereby ACVD. We discuss the associations between the microbiome and the inflammasome, the innate immune system, bile acids, gut permeability, the endocannabinoid system and TMAO. These data reinforce the importance of human research into the gut microbiota as potential diagnostic and therapeutic strategy to prevent ACVD.
Regional trans-boundary air pollution has become an important issue in the field of air pollution modeling. This paper presents the results of the implementation of the MM5-CMAQ modeling system in ...the Yangtze River Delta (YRD) for the months of January and July of 2004. The meteorological parameters are obtained by using the MM5 model. A new regional emission inventory with spatial and temporal allocations based on local statistical data has been developed to provide input emissions data to the MM5-CMAQ modeling system. The pollutant concentrations obtained from the MM5-CMAQ modeling system have been compared with observational data from the national air pollution monitoring network. It is found that air quality in winter in the YRD is generally worse than in summer, due mainly to unfavorable meteorological dispersion conditions. In winter, the pollution transport from Northern China to the YRD reinforces the pollution caused by large local emissions. The monthly average concentration of SO2 in the YRD is 0.026 ± 0.011 mg m−3 in January and 0.017 ± 0.009 mg m−3 in July. Monthly average concentrations of NO2 in the YRD in January and July are 0.021 ± 0.009 mg m−3, and 0.014 ± 0.008 mg m−3, respectively. The monthly average concentration of PM10 in the YRD is 0.080 ± 0.028 mg m−3 in January and 0.025 ± 0.015 mg m−3 in July. Visibility is also a problem, with average deciview values of 26.4 ± 2.95 dcv in winter and 17.6 ± 3.3 dcv in summer. The ozone concentration in the downtown area of a city like Zhoushan can be very high, with the highest simulated value reaching 0.24 mg m−3. In January, the monthly average concentration of O3 in the YRD is 0.052 ± 0.011 mg m−3, and 0.054 ± 0.008 mg m−3 in July. Our results show that ozone and haze have become extremely important issues in the regional air quality. Thus, regional air pollution control is urgently needed to improve air quality in the YRD.
The Chinese Spring Festival is one of the most important traditional festivals in China. The peak transport in the Spring Festival season (spring travel rush) provides a unique opportunity for ...investigating the impact of human activity on air quality in the Chinese megacities. Emission sources are varied and fluctuate greatly before, during and after the Festival. Increased vehicular emissions during the "spring travel rush" before the 2009 Festival resulted in high level pollutants of NOx (270 μg m−3), CO (2572 μg m−3), black carbon (BC) (8.5 μg m−3) and extremely low single scattering albedo of 0.76 in Shanghai, indicating strong, fresh combustion. Organics contributed most to PM2.5, followed by NO3−, NH4+, and SO42−. During the Chinese Lunar New Year's Eve and Day, widespread usage of fireworks caused heavy pollution of extremely high aerosol concentration, scattering coefficient, SO2, and NOx. Due to the "spring travel rush" after the festival, anthropogenic emissions gradually climbed and mirrored corresponding increases in the aerosol components and gaseous pollutants. Secondary inorganic aerosol (SO42−, NO3−, and NH4+) accounted for a dominant fraction of 74% in PM2.5 due to an increase in human activity. There was a greater demand for energy as vast numbers of people using public transportation or driving their own vehicles returned home after the Festival. Factories and constructions sites were operating again. The potential source contribution function (PSCF) analysis illustrated the possible source areas for air pollutants of Shanghai. The effects of regional and long-range transport were both revealed. Five major sources, i.e. natural sources, vehicular emissions, burning of fireworks, industrial and metallurgical emissions, and coal burning were identified using the principle component analysis. The average visibility during the whole study period was less than 6 km. It had been estimated that 50% of the total light extinction was due to the high water vapor in the atmosphere. This study demonstrates that organic aerosol was the largest contributor to aerosol extinction at 47%, followed by sulfate ammonium, nitrate ammonium, and EC at 22%, 14%, and 12%, respectively. Our results indicated the dominant role of traffic-related aerosol species (i.e. organic aerosol, nitrate and EC) on the formation of air pollution, and suggested the importance of controlling vehicle numbers and emissions in mega-cities of China as its population and economy continue to grow.
Dynamical downscaling was applied in this study to link the global climate-chemistry model Community Atmosphere Model (CAM-Chem) with the regional models Weather Research and Forecasting (WRF) Model ...and Community Multi-scale Air Quality (CMAQ). Two representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) were used to evaluate the climate impact on ozone concentrations in the 2050s. From the CAM-Chem global simulation results, ozone concentrations in the lower to mid-troposphere (surface to ~300 hPa), from mid- to high latitudes in the Northern Hemisphere, decreases by the end of the 2050s (2057–2059) in RCP 4.5 compared to present (2001–2004), with the largest decrease of 4–10 ppbv occurring in the summer and the fall; and an increase as high as 10 ppbv in RCP 8.5 resulting from the increased methane emissions. From the regional model CMAQ simulation results, under the RCP 4.5 scenario (2057–2059), in the summer when photochemical reactions are the most active, the large ozone precursor emissions reduction leads to the greatest decrease of downscaled surface ozone concentrations compared to present (2001–2004), ranging from 6 to 10 ppbv. However, a few major cities show ozone increases of 3 to 7 ppbv due to weakened NO titration. Under the RCP 8.5 scenario, in winter, downscaled ozone concentrations increase across nearly the entire continental US in winter, ranging from 3 to 10 ppbv due to increased methane emissions. More intense heat waves are projected to occur by the end of the 2050s in RCP 8.5, leading to a 0.3 ppbv to 2.0 ppbv increase (statistically significant except in the Southeast) of the mean maximum daily 8 h daily average (MDA8) ozone in nine climate regions in the US. Moreover, the upper 95% limit of MDA8 increase reaches 0.4 ppbv to 1.5 ppbv in RCP 4.5 and 0.6 ppbv to 3.2 ppbv in RCP 8.5. The magnitude differences of increase between RCP 4.5 and 8.5 also reflect that the increase of methane emissions may favor or strengthen the effect of heat waves.
An intensive aerosol and gases campaign was performed at Shanghai in the Yangtze River Delta region over Eastern China from late March to early June 2009. This study provided a complementary picture ...of typical haze types and the formation mechanisms in megacities over China by using a synergy of ground-based monitoring, satellite and lidar observations. During the whole study period, several extreme low visibility periods were observed with distinct characteristics, and three typical haze types were identified, i.e. secondary inorganic pollution, dust, and biomass burning. Sulfate, nitrate and ammonium accounted for a major part of PM2.5 mass during the secondary inorganic pollution, and the good correlation between SO2/NOx/CO and PM2.5 indicated that coal burning and vehicle emission were the major sources. Large-scale regions with high AOD (aerosol optical depths) and low Ångström exponent were detected by remote-sensing observation during the dust pollution episode, and this episode corresponded to coarse particles rich in mineral components such as Al and Ca contributing 76.8% to TSP. The relatively low Ca/Al ratio of 0.75 along with the air mass backward trajectory analysis suggested the dust source was from Gobi Desert. Typical tracers for biomass burning from satellite observation (column CO and HCHO) and from ground measurement (CO, particulate K+, OC, and EC) were greatly enhanced during the biomass burning pollution episode. The exclusive linear correlation between CO and PM2.5 corroborated that organic aerosol dominated aerosol chemistry during biomass burning, and the high concentration and enrichment degree of arsenic (As) could be also partly derived from biomass burning. Aerosol optical profile observed by lidar demonstrated that aerosol was mainly constrained below the boundary layer and comprised of spheric aerosol (depolarization ratio <5%) during the secondary inorganic and biomass burning episodes, while thick dust layer distributed at altitudes from near surface to 1.4 km (average depolarization ratio = 0.122 0.023) with dust accounting for 44–55% of the total aerosol extinction coefficient during the dust episode. This study portrayed a good picture of the typical haze types and proposed that identification of the complicated emission sources is important for the air quality improvement in megacities in China.
Vehicle emissions are a major source of urban air pollution. In recent decade, the Chinese government has introduced a range of policies to reduce vehicle emissions. In order to understand the ...chemical characteristics of PM2.5 from on-road vehicle emissions in the Pearl River Delta (PRD) region and to evaluate the effectiveness of control policies on vehicle emissions, the emission factors of PM2.5 mass, elemental carbon (EC), organic carbon (OC), water-soluble organic carbon (WSOC), water-soluble inorganic ions (WSII), metal elements, organic compounds and stable carbon isotopic composition were measured in the Zhujiang tunnel of Guangzhou, in the PRD region of China in 2013. Emission factors of PM2.5 mass, OC, EC and WSOC were 92.4, 16.7, 16.4 and 1.31 mg vehicle-1 km-1 respectively. Emission factors of WSI delta were 0.016 (F-) similar to 4.17 (Cl-) mg vehicle-1 km-1, contributing about 9.8% to the PM2.5 emissions. The sum of 27 measured metal elements accounted for 15.2% of PM2.5 emissions. Fe was the most abundant metal element, with an emission factor of 3.91 mg vehicle-1 km-1. Emission factors of organic compounds including n-alkanes, polycyclic aromatic hydrocarbons, hopanes and steranes were 91.9, 5.02, 32.0 and 7.59 mu g vehicle-1 km-1, respectively. Stable carbon isotopic composition delta 13C value was -25.0ppt on average. An isotopic fractionation of 3.2ppt was found during fuel combustion. Compared to a previous study in Zhujiang tunnel in 2004, emission factors of PM2.5mass, EC, OC, WSI delta except Cl- and organic compounds decreased by 16.0 similar to 93.4%, which could be attributed to emission control policy from 2004 to 2013. However, emission factors of most of the metal elements increased significantly, which could be partially attributed to the changes in motor oil additives and vehicle conditions. There are no mandatory national standards to limit metal content from vehicle emissions, which should be a concern of the government. A snapshot of the 2013 characteristic emissions of PM2.5 and its constituents from the on-road vehicular fleet in the PRD region retrieved from our study would be helpful for the assessment of past and future implementations of vehicle emission control policy.
The South Pole Telescope (SPT) has systematically identified 81 high-redshift, strongly gravitationally lensed, dusty star-forming galaxies (DSFGs) in a 2500 square degree cosmological ...millimeter-wave survey. We present the final spectroscopic redshift survey of this flux-limited (S870 m > 25 mJy) sample, initially selected at 1.4 mm. The redshift survey was conducted with the Atacama Large Millimeter/submillimeter Array across the 3 mm spectral window, targeting carbon monoxide line emission. By combining these measurements with ancillary data, the SPT sample is now spectroscopically complete, with redshifts spanning 1.9 < z < 6.9 and a median of . We present the millimeter through far-infrared photometry and spectral energy density fits for all sources, along with their inferred intrinsic properties. Comparing the properties of the SPT sources to the unlensed DSFG population, we demonstrate that the SPT-selected DSFGs represent the most extreme infrared-luminous galaxies, even after accounting for strong gravitational lensing. The SPT sources have a median star formation rate of and a median dust mass of . However, the inferred gas depletion timescales of the SPT sources are comparable to those of unlensed DSFGs, once redshift is taken into account. This SPT sample contains roughly half of the known spectroscopically confirmed DSFGs at z > 5, making this the largest sample of high-redshift DSFGs to date, and enabling the "high-redshift tail" of extremely luminous DSFGs to be measured. Though galaxy formation models struggle to account for the SPT redshift distribution, the larger sample statistics from this complete and well-defined survey will help inform future theoretical efforts.
Abstract
We present analysis of a new pulsating helium-atmosphere (DB) white dwarf, EPIC 228782059, discovered from 55.1 days of K2 photometry. The long-duration, high-quality light curves reveal 11 ...independent dipole and quadruple modes, from which we derive a rotational period of 34.1 ± 0.4 hr for the star. An optimal model is obtained from a series of grids constructed using the White Dwarf Evolution Code, which returns
M
*
= 0.685 ± 0.003
M
⊙
,
T
eff
= 21,910 ± 23 K, and
log
g
=
8.14
±
0.01
dex. These values are comparable to those derived from spectroscopy by Koester & Kepler (20,860 ± 160 K, and 7.94 ± 0.03 dex). If these values are confirmed or better constrained by other independent works, it would make EPIC 228782059 one of the coolest pulsating DB white dwarf stars known, and would be helpful for testing different physical treatments of convection, and to further investigate the theoretical instability strip of DB white dwarf stars.
The gut microbiome is associated with diverse diseases
, but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, ...exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.
Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative ...determination method is not accurate enough.
We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis.
The DLRN showed good discrimination of the number of LNM on all cohorts overall C-indexes (95% confidence interval): 0.821 (0.785–0.858) in the primary cohort, 0.797 (0.771–0.823) in the external validation cohorts, and 0.822 (0.756–0.887) in the international validation cohort. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n = 271).
A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.
•Evaluation of the lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC).•Deep leaning radiomic nomogram (DLRN) based on CT images can preoperatively determine the number of LNM in LAGC.•DLRN is significantly superior to the routinely used clinical N stages, tumor size, and clinical model.•DLRN is significantly associated with the overall survival of LAGC.