The Bonferroni adjustment, or the union bound, is commonly used to study rate optimality properties of statistical methods in high-dimensional problems. However, in practice, the Bonferroni adjustment is overly conservative. The extreme value theory has been proven to provide more accurate multiplicity adjustments in a number of settings, but only on ad hoc basis. Recently, Gaussian approximation has been used to justify bootstrap adjustments in large scale simultaneous inference in some general settings when n ≫ (log p) 7 , where p is the multiplicity of the inference problem and n is the sample size. The thrust of this theory is the validity of the Gaussian approximation for maxima of sums of independent random vectors in high-dimension. In this paper, we reduce the sample size requirement to n ≫ (log p) 5 for the consistency of the empirical bootstrap and the multiplier/wild bootstrap in the Kolmogorov-Smirnov distance, possibly in the regime where the Gaussian approximation is not available. New comparison and anti-concentration theorems, which are of considerable interest in and of themselves, are developed as existing ones interweaved with Gaussian approximation are no longer applicable.

A Lotka-Volterra predator-prey model incorporating predator cannibalism is proposed and studied in this paper. The existence and stability of all possible equilibria of the system are investigated. Our study shows that cannibalism has both positive and negative effect on the stability of the system, it depends on the dynamic behaviors of the original system. If the predator species in the system without cannibalism is extinct, then suitable cannibalism may lead to the coexistence of both species, in this case, cannibalism stabilizes the system. If the cannibalism rate is large enough, the prey species maybe driven to extinction, while the predator species are permanent. If the two species coexist in the stable state in the original system, then predator cannibalism may lead to the extinction of the prey species. In this case, cannibalism has an unstable effect. Numeric simulations support our findings.

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Ethyl carbamate (EC) is a probable carcinogenic compound commonly found in fermented foods and alcoholic beverages and has been classified as a category 2A carcinogen by the International Agency for Research on Cancer (IARC).Alcoholic beverages are one of the main sources of EC intake by humans. Therefore, many countries have introduced a standard EC limit in alcoholic beverages. Wine is the second largest alcoholic beverage in the world after beer and is loved by consumers for its rich taste. However, different survey results showed that the detection rate of EC in wine was almost 100%, while the maximum content was as high as 100 μg/L, necessitating EC content regulation in wine. The existing methods for controlling the EC level in wine mainly include optimizing raw fermentation materials and processes, using genetically engineered strains, and enzymatic methods (urease or urethanase). This review focused on introducing and comparing the advantages, disadvantages, and applicability of methods for controlling EC, and proposes two possible new techniques, that is, changing the fermentation strain and exogenously adding phenolic compounds. In the future, it is hoped that the feasibility of this prospect will be verified by pilot-scale or large-scale application to provide new insight into the regulation of EC during wine production. The formation mechanism and influencing factors of EC in wine were also introduced and the analytical methods of EC were summarized.

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ObjectiveTo measure the rate of the A2063G mutation in the Mycoplasma pneumoniae (M. pneumoniae) 23S rRNA domain V in children with pneumonia and to determine the correlation between radiographic findings and the presence of the A2063G mutation.MethodsPatients who were hospitalized with a confirmed diagnosis of M. pneumoniae pneumonia were enrolled in this study. M. pneumoniae strains were collected for genotype analysis. Chest radiography was performed on all children prior to and following macrolide treatment. Clinical and imaging data were obtained.ResultsOf 211 patients, 195 (92.42%) harboured M. pneumoniae with the A2063G mutation. No significant differences were identified in inflammation score, chest radiography inflammation absorption grade before and after macrolide treatment, or pulmonary complications (atelectasis, hydrothorax, or pleuritis) prior to macrolide treatment when children were stratified based on the presence or absence of the A2063G mutation.ConclusionsA high proportion of children with pneumonia harboured strains of M. pneumoniae with the A2063G mutation in the 23S rRNA domain V. However, no obvious chest radiographic features of M. pneumoniae pneumonia were associated with the A2063G variant.

We consider the problem of constructing pointwise confidence intervals in the multiple isotonic regression model. Recently, Han and Zhang [HZ19] obtained a pointwise limit distribution theory for the block max-min and min-max estimators [FLN17] in this model, but inference remains a difficult problem due to the nuisance parameter in the limit distribution that involves multiple unknown partial derivatives of the true regression function.In this paper, we show that this difficult nuisance parameter can be effectively eliminated by taking advantage of information beyond point estimates in the block max-min and min-max estimators. Formally, let û(x0) (resp. v(x0)) be the maximizing lower-left (resp. minimizing upper-right) vertex in the block max-min (resp. min-max) estimator, and fn be the average of the block max-min and min-max estimators. If all (first-order) partial derivatives of f0 are non-vanishing at x0, then the following pivotal limit distribution theory holds:Here σ is the standard deviation of the errors, and L1 d is a universal limit distribution free of nuisance parameters. This immediately yields confidence intervals for f0(x0) with asymptotically exact confidence level and optimal length. Notably, the construction of the confidence intervals, even new in the univariate setting, requires no more efforts than performing an isotonic regression for once using the block max-min and min-max estimators, and can be easily adapted to other common monotone models including, e.g., (i) monotone density estimation, (ii) interval censoring model with current status data, (iii) counting process model with panel count data, and (iv) generalized linear models. Extensive simulation results demonstrate the accuracy of the coverage probability of the proposed confidence intervals, giving strong support to our theory.