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The official air-quality statistic reported that Beijing had a 9.9% decline in the annual concentration of PM2.5 in 2016. While this statistic offered some relief for the inhabitants of the capital, we present several analyses based on Beijing's PM2.5 data of the past 4 years at 36 monitoring sites along with meteorological data of the past 7 years. The analyses reveal the air pollution situation in 2016 was not as rosy as the 9.9% decline would convey, and improvement if any was rather uncertain. The paper also provides an assessment on the city's PM2.5 situation in the past 4 years.

This paper considers distributed statistical inference for general symmetric statistics in the context of massive data with efficient computation. Estimation efficiency and asymptotic distributions of the distributed statistics are provided, which reveal different results between the nondegenerate and degenerate cases, and show the number of the data subsets plays an important role. Two distributed bootstrap methods are proposed and analyzed to approximation the underlying distribution of the distributed statistics with improved computation efficiency over existing methods. The accuracy of the distributional approximation by the bootstrap are studied theoretically. One of the methods, the pseudo-distributed bootstrap, is particularly attractive if the number of datasets is large as it directly resamples the subset-based statistics, assumes less stringent conditions and its performance can be improved by studentization.

This study aimed to investigate the effect of lipoprotein(a) (Lp(a)) on major adverse cardiovascular events (MACEs) among individuals with chronic coronary syndrome (CCS) according to ABO blood groups. Two independent cohorts of patients with CCS were included consecutively. Blood groups and Lp(a) levels were measured. Patients with the AB group were excluded due to the small sample size. In the exploratory cohort (n = 7611), 560 MACEs were recorded over a mean follow‐up of 54.80 months. Stratification analysis revealed that the relationship of elevated Lp(a) levels with prognosis was more pronounced in patients with blood group A or B. Patients with blood group A or B plus medium Lp(a) (HR, 1.93, 95% CI: 1.24–3.01) or high Lp(a) (HR, 2.06, 95% CI: 1.32–3.24) concentrations had a significantly higher risk of MACEs compared to those with blood group O and low Lp(a) levels. Similar results were obtained in the confirmatory cohort (n = 7916). In conclusion, our data demonstrated for the first time a more prominent association between Lp(a) and adverse outcomes in CCS patients with non‐O blood group compared to those with blood group O, suggesting that ABO blood group measurement may be clinically useful for decision‐making in Lp(a) intervention. The present study firstly revealed that the predictive value of Lp(a) for worse outcomes in chronic coronary syndrome patients with A or B blood group was stronger than that in the O group, suggesting that ABO blood group measurement may be clinically helpful for decision‐making in Lp(a) intervention.

Approximately 70% patients with acute myocardial infarction (AMI) presented without ST-segment elevation on electrocardiogram. Patients with non-ST segment elevation myocardial infarction (NSTEMI) often presented with atypical symptoms, which may be related to pre-hospital delay and increased risk of mortality. However, up to date few studies reported detailed symptomatology of NSTEMI, particularly among Asian patients. The objective of this study was to describe and compare symptoms and presenting characteristics of NSTEMI vs. STEMI patients. We enrolled 21,994 patients diagnosed with AMI from China Acute Myocardial Infarction (CAMI) Registry between January 2013 and September 2014. Patients were divided into 2 groups according to ST-segment elevation: ST-segment elevation (STEMI) group and NSTEMI group. We extracted data on patients' characteristics and detailed symptomatology and compared these variables between two groups. Compared with patients with STEMI (N = 16,315), those with NSTEMI (N = 5679) were older, more often females and more often have comorbidities. Patients with NSTEMI were less likely to present with persistent chest pain (54.3% vs. 71.4%), diaphoresis (48.6% vs. 70.0%), radiation pain (26.4% vs. 33.8%), and more likely to have chest distress (42.4% vs. 38.3%) than STEMI patients (all P < 0.0001). Patients with NSTEMI were also had longer time to hospital. In multivariable analysis, NSTEMI was independent predictor of presentation without chest pain (odds ratio: 1.974, 95% confidence interval: 1.849-2.107). Patients with NSTEMI were more likely to present with chest distress and pre-hospital patient delay compared with patients with STEMI. It is necessary for both clinicians and patients to learn more about atypical symptoms of NSTEMI in order to rapidly recognize myocardial infarction. www.clinicaltrials.gov (No. NCT01874691).This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0.

Patients with ST-segment elevation myocardial infarction (STEMI) who present without typical chest pain are associated with a poor outcome. However, whether angiographic characteristics are related to a higher risk of mortality in this population is unclear. This study aimed to investigate whether the higher mortality risk in patients with STEMI without chest pain could be explained by their \"high-risk\" angiographic characteristics. We used data of 12,145 patients with STEMI who was registered in China Acute Myocardial Infarction registry from January 2013 to September 2014. We compared the infarct-related artery (IRA), thrombolysis in myocardial infarction (TIMI) flow grade in the IRA, and other angiographic characteristics between patients without and those with chest pain. Multivariable logistic regression model was used to identify independent risk factor of in-hospital mortality. The 2922 (24.1%) patients with STEMI presented without typical chest pain. These patients had a higher TIMI flow grade (mean TIMI flow grade: 1.00 vs. 0.94, P = 0.02) and a lower rate of IRA disease of the left anterior descending artery (44.6% vs. 51.2%, χ = 35.63, P < 0.01) than did those with typical chest pain. Patients without chest pain were older, more likely to have diabetes, longer time to hospital and higher Killip classification, and less likely to receive optimal medication treatment and primary percutaneous coronary intervention and higher In-hospital mortality (3.3% vs. 2.2%, χ = 10.57, P < 0.01). After adjusting for multi-variables, presentation without chest pain was still an independent predictor of in-hospital death among patients with STEMI (adjusted odds ratio: 1.36, 95% confidence interval: 1.02-1.83). Presentation without chest pain is common and associated with a higher in-hospital mortality risk in patients with acute myocardial infarction. Our results indicate that their poor prognosis is associated with baseline patient characteristics and delayed treatment, but not angiographic lesion characteristics. NCT01874691, https://clinicaltrials.gov.

We propose tests for sphericity and identity of high-dimensional covariance matrices. The tests are nonparametric without assuming a specific parametric distribution for the data. They can accommodate situations where the data dimension is much larger than the sample size, namely the \"large p, small n\" situations. We demonstrate by both theoretical and empirical studies that the tests have good properties for a wide range of dimensions and sample sizes. We applied the proposed test on a microarray dataset on Yorkshire Gilts and tested for the covariance structure for the expression levels for sets of genes.

We construct an autoregressive moving average (ARMA) model consisting of the history and random effects for the streamwise velocity fluctuation in boundary-layer turbulence. The distance to the wall and the boundary-layer thickness determine the time step and the order of the ARMA model, respectively. Based on the autocorrelation's analytical expression of the ARMA model, we obtain a global analytical expression for the second-order structure function, which asymptotically captures the inertial, dynamic and large-scale ranges. Specifically, the exponential autocorrelation of the ARMA model arises from the autoregressive coefficients and is modified to logarithmic behaviour by the moving-average coefficients. The asymptotic expressions enable us to determine model coefficients by existing parameters, such as the Kolmogorov and the Townsend–Perry constants. A consequent double-log expression for the characteristic length scale is derived and is justified by direct numerical simulation data with $Re_\\tau \\approx 5200$ and field-measured neutral atmospheric surface layer data with $Re_\\tau \\sim O(10^6)$ from the Qingtu Lake Observation Array site. This relation is robust because it applies to $Re_\\tau$ from $O(10^4)$ to $O(10^6)$, and even when the statistics of natural ASL deviate from those of canonical boundary-layer turbulence, e.g. in the case of imbalance in energy production and dissipation, and when the Townsend–Perry constant deviates from traditional values.

We propose a two-sample test for the means of high-dimensional data when the data dimension is much larger than the sample size. Hotelling's classical T² test does not work for this \"large p, small n\" situation. The proposed test does not require explicit conditions in the relationship between the data dimension and sample size. This offers much flexibility in analyzing high-dimensional data. An application of the proposed test is in testing significance for sets of genes which we demonstrate in an empirical study on a leukemia data set.

This paper proposes a nonparametric simultaneous test for parametric specification of the conditional mean and variance functions in a time series regression model. The test is based on an empirical likelihood (EL) statistic that measures the goodness of fit between the parametric estimates and the nonparametric kernel estimates of the mean and variance functions. A unique feature of the test is its ability to distribute natural weights automatically between the mean and the variance components of the goodness-of-fit measure. To reduce the dependence of the test on a single pair of smoothing bandwidths, we construct an adaptive test by maximizing a standardized version of the empirical likelihood test statistic over a set of smoothing bandwidths. The test procedure is based on a bootstrap calibration to the distribution of the empirical likelihood test statistic. We demonstrate that the empirical likelihood test is able to distinguish local alternatives that are different from the null hypothesis at an optimal rate.

We study epidemiological characteristics of 25 early COVID-19 outbreak countries, which emphasizes on the reproduction of infection and effects of government control measures. The study is based on a vSIADR model which allows asymptomatic and pre-diagnosis infections to reflect COVID-19 clinical realities, and a linear mixed-effect model to analyse the association between each country’s control measures and the effective reproduction number Rt . It finds significant effects of higher stringency measures in lowering the reproduction, and a significant shortening effect on the time to the epidemic turning point by applying stronger early counter measures. Epidemic projections under scenarios of the counter measures (China and Korea, the USA and the UK) show substantial reduction in the epidemic size and death by taking earlier and forceful actions. The governments’ response before and after the start of the second wave epidemics were alarmingly weak, which made the average duration of the second wave more than doubled that of the first wave. We identify countries which urgently need to restore to at least the maximum stringency measures implemented so far in the pandemic in order to avoid even higher infection size and death.