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Anti-Müllerian hormone (AMH) acts in maintaining orderly cyclic recruitment of early follicles, suggesting that it is a promising candidate for influencing animal reproductive efficiency. This study aimed to elucidate the effect of a missense mutation of Val566Ile on the structure of AMH protein and the genetic association of Val566Ile and AMH expression with egg production in chickens. Structural perturbations of Val566Ile were predicted by homology modeling. The association of the variant with the number of eggs was tested using a quantitative trait transmission disequilibrium test model. AMH expression in granulosa cells in Lueyang black-boned chickens was compared with that in Nick chickens. The Val566 of AMH is a non-conservative amino acid among mammals and birds, but its hydrophobicity is completely conservative. The substitution of Val566 for Ile566 potentially disrupted hydrogen bonds and solvent accessibility of 22 residues and created a short α-helix in the C terminus of AMH. Despite having striking structure-disrupting potential, the variant was not statistically associated with the number of eggs (P>0.05) in the Lueyang black-boned chickens. We did not detect differential expression of AMH between Lueyang black-boned chickens and Nick chickens (P>0.05). These results confirmed the structural impact of Val566Ile, but suggested that Val566Ile and AMH expression might not be the major genetic determinants for egg production in Lueyang black-boned chickens.

Naturally fractured reservoirs make important contributions to global oil and gas reserves and production. The modeling and simulation of naturally fractured reservoirs are different from conventional reservoirs as the existence of natural fractures. To address the development optimization problem of naturally fractured reservoirs, we propose an optimization workflow by coupling the optimization methods with the embedded discrete fracture model (EDFM). Firstly, the effective and superior performance of the workflow is verified based on the conceptual model. The stochastic simplex approximate gradient (StoSAG) algorithm, the ensemble optimization (EnOpt) algorithm, and the particle swarm optimization (PSO) algorithm are implemented for the production optimization of naturally fractured reservoirs based on the improved versions of the Egg model and the PUNQ-S3 model. The results of the two cases demonstrate the effectiveness of this optimization workflow by finding the optimal well controls which yield the maximum net present value (NPV). Compared to the initial well control guess, the final NPV obtained from the production optimization of fractured reservoirs based on all three optimization algorithms is significantly enhanced. Compared with the optimization results of the PSO algorithm, StoSAG and EnOpt have significant advantages in terms of final NPV and computational efficiency. The results also show that fractures have a significant impact on reservoir production. The economic efficiency of fractured reservoir development can be significantly improved by the optimization workflow. [ABSTRACT FROM AUTHOR]

Large-scale multivariate regression is a fundamental statistical tool with a wide range of applications. This study considers the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. The challenge that accompanies a large number of tests is the ubiquitous presence of heavy-tailed and/or highly skewed measurement noise, which is the main reason for the failure of conventional least squares-based methods. For large-scale multivariate regression, we develop a set of robust inference methods to explore data features such as heavy tailedness and skewness, which are not visible to least squares methods. The new testing procedure is based on the data-adaptive Huber regression and a new covariance estimator of regression estimates. Under mild conditions, we show that our methods produce consistent estimates of the false discovery proportion. Extensive numerical experiments and an empirical study on quantitative linguistics demonstrate the advantage of the proposed method over many state-of-the-art methods when the data are generated from heavy-tailed and/or skewed distributions. [ABSTRACT FROM AUTHOR]

The robustification parameter, which balances bias and robustness, plays a critical role in the construction of sub Gaussian estimators for heavy-tailed and/or skewed data. Although the parameter can be tuned using cross-validation, in large-scale statistical problems such as high-dimensional covariance matrix estimation and large-scale multiple testing, the number of robustification parameters increases with the dimensionality causing cross-validation to become computationally prohibitive. We propose a new data-driven principle for choosing the robustification parameter for Huber-type sub Gaussian estimators in three fundamental problems: mean estimation, linear regression, and sparse regression in high dimensions. Our proposal is guided by a nonasymptotic deviation analysis, and is conceptually different from cross-validation, which relies on the mean squared error to assess the fit. Extensive numerical experiments and a real-data analysis further illustrate the efficacy of the proposed methods. [ABSTRACT FROM AUTHOR]

We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed by Fan et al. (2019) for large-scale simultaneous inference on mean effects. We use a multi-factor model to explicitly capture the dependence among a large pool of variables. Three types of factors are considered: observable, latent, and a mixture of observable and latent factors. The non-factor case, which corresponds to standard multiple mean testing under weak dependence, is also included. The library implements a series of adaptive Huber methods integrated with fast data-driven tuning schemes to estimate model parameters and to construct test statistics that are robust against heavy-tailed and asymmetric error distributions. Extensions to two-sample multiple mean testing problems are also discussed. The results of some simulation experiments and a real data analysis are reported. [ABSTRACT FROM AUTHOR]

We consider in this paper the problem of noisy 1-bit matrix completion under a general non-uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max-norm constrained maximum likelihood estimate is introduced and studied. The rate of convergence for the estimate is obtained. Information-theoretical methods are used to establish a minimax lower bound under the general sampling model. The minimax upper and lower bounds together yield the optimal rate of convergence for the Frobenius norm loss. Computational algorithms and numerical performance are also discussed. [ABSTRACT FROM AUTHOR]

We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions. This is challenging due to the non-smooth nature of the quantile regression (QR) loss function, which invalidates the use of existing methodology. The difficulties are resolved through a double-smoothing approach that is applied to the local (at each data source) and global objective functions. Despite the reliance on a delicate combination of local and global smoothing parameters, the quantile regression model is fully parametric, thereby facilitating interpretation. In the low-dimensional regime, we establish a finite-sample theoretical framework for the sequentially defined distributed QR estimators. This reveals a trade-off between the communication cost and statistical error. We further discuss and compare several alternative confidence set constructions, based on inversion of Wald and score-type tests and resampling techniques, detailing an improvement that is effective for more extreme quantile coefficients. In high dimensions, a sparse framework is adopted, where the proposed doubly-smoothed objective function is complemented with an l1-penalty. We show that the corresponding distributed penalized QR estimator achieves the global convergence rate after a near-constant number of communication rounds. A thorough simulation study further elucidates our findings. [ABSTRACT FROM AUTHOR]

Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To address these challenges, in this paper we propose a Factor-Adjusted Robust Multiple Testing ( FarmTest ) procedure for large-scale simultaneous inference with control of the false discovery proportion. We demonstrate that robust factor adjustments are extremely important in both controlling the FDP and improving the power. We identify general conditions under which the proposed method produces consistent estimate of the FDP. As a byproduct that is of independent interest, we establish an exponential-type deviation inequality for a robust U -type covariance estimator under the spectral norm. Extensive numerical experiments demonstrate the advantage of the proposed method over several state-of-the-art methods especially when the data are generated from heavy-tailed distributions. The proposed procedures are implemented in the R-package FarmTest.