Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices

This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection. The optimal rate of convergence for estimating the spiked covariance matrix under the spectral norm is established, which requires significantly different techniques from those for estimating other structured covariance matrices such as bandable or sparse covariance matrices. We also establish the minimax rate under the spectral norm for estimating the principal subspace, the primary object of interest in principal component analysis. In addition, the optimal rate for the rank detection boundary is obtained. This result also resolves the gap in a recent paper by Berthet and Rigollet [1] where the special case of rank one is considered

Sparse PCA: Optimal rates and adaptive estimation

Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. This paper considers both minimax and adaptive estimation of the principal subspace in the high dimensional setting. Under mild technical conditions, we first establish the optimal rates of convergence for estimating the principal subspace which are sharp with respect to all the parameters, thus providing a complete characterization of the difficulty of the estimation problem in term of the convergence rate. The lower bound is obtained by calculating the local metric entropy and an application of Fano's lemma. The rate optimal estimator is constructed using aggregation, which, however, might not be computationally feasible. We then introduce an adaptive procedure for estimating the principal subspace which is fully data driven and can be computed efficiently. It is shown that the estimator attains the optimal rates of convergence simultaneously over a large collection of the parameter spaces. A key idea in our construction is a reduction scheme which reduces the sparse PCA problem to a high-dimensional multivariate regression problem. This method is potentially also useful for other related problems.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1178 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Challenges and Prospects of Vehicle OTA Spherical Near-Field Measurement Probes

This Paper Discusses the Issue of Measuring Probe Indicators for Large-Scale Equipment, Such as Automobiles, under Conditions of Offset Configuration. a Simulation of Spherical Near-Field Measurement based on an Offset Configuration is Presented in This Paper. the Measurement Error is Defined According to the Reference Data Calculated by Spherical Wave Expansion Theory. through Comparative Analysis of the Simulation Results, the Main Reason for the Measurement Error is the Insufficient Coverage of the Probe\u27s Beamwidth. by Adjusting the Probe\u27s Radiation Pattern using Simulation Software, an Optimized Probe that Satisfies Near-Field Measurement Requirements under Meter-Level Offset Conditions is Obtained. Finally, based on the Simulation Results, a Set of Recommended Values for the Main Performance of the Optimized Probe is Provided

The effect of nitric oxide on the pressure of the acutely obstructed ureter

Acute ureteral obstruction leads to changes in pressure inside the ureter, interrupting ureter function. The aim of our study is to explore the relationship between nitric oxide (NO) concentration and pressure in the ureter and to observe the effects of nitric oxide on the revival of renal function. We created the animal models by embedding balloons in the lower ureters of anesthetized dogs and expanding them to simulate acute ureteral obstruction. First, the test animals were pre-treated intravenously with different doses of L-NAME (non-selective nitric oxide synthase inhibitor) to inhibit nitric oxide synthase (NOS), and 10 min later, each subject was administered an intravenous dose of isoproterenol (10 μg/kg). We measured ureter pressure (UP), total and peak concentrations of NO (using an NO monitor, model inNO-T) in ureteral urine, and the volume of the urine (UFV) leaking from the balloon edge. After a certain amount of time had elapsed, it became clear that the dose of L-NAME was inversely related to the total and peak concentrations of NO, the rate of change in UP, and the volume of urine produced. We conclude that L-NAME prevents the NOS from inhibiting the release of NO, then inhibits the effect of isoproterenol reducing the pressure of the acute obstructive ureter. Inversely, we think that NO can reduce the pressure of the acute obstructive ureter and make the obstructive ureter recanalization. And when more the concentration of nitric oxide, the more the pressure will be reduced, and more urine will be collected

Leep1 interacts with PIP3 and the Scar/WAVE complex to regulate cell migration and macropinocytosis

Polarity is essential for diverse functions in many cell types. Establishing polarity requires targeting a network of specific signaling and cytoskeleton molecules to different subregions of the cell, yet the full complement of polarity regulators and how their activities are integrated over space and time to form morphologically and functionally distinct domains remain to be uncovered. Here, by using the model system Dictyostelium and exploiting the characteristic chemoattractant-stimulated translocation of polarly distributed molecules, we developed a proteomic screening approach, through which we identified a leucine-rich repeat domain–containing protein we named Leep1 as a novel polarity regulator. We combined imaging, biochemical, and phenotypic analyses to demonstrate that Leep1 localizes selectively at the leading edge of cells by binding to PIP3, where it modulates pseudopod and macropinocytic cup dynamics by negatively regulating the Scar/WAVE complex. The spatiotemporal coordination of PIP3 signaling, Leep1, and the Scar/WAVE complex provides a cellular mechanism for organizing protrusive structures at the leading edge

High Thyroid Stimulating Hormone Level Is Associated With Hyperandrogenism in Euthyroid Polycystic Ovary Syndrome (PCOS) Women, Independent of Age, BMI, and Thyroid Autoimmunity: A Cross-Sectional Analysis

Background: Infertility and dyslipidemia are frequently present in both women with polycystic ovary syndrome (PCOS) and subjects with thyroid dysfunction. Limited study regarding the association between thyroid stimulating hormone (TSH) level and phenotypes in euthyroid PCOS women. We aimed to determine whether the variation of TSH level associates with phenotypes in euthyroid PCOS patients.Methods: Cross-sectional study including 600 PCOS and 200 age, body mass index (BMI), and thyroid autoimmunity-matched Chinese women from Renji hospital, Shanghai Jiaotong university during January 2010 and August 2018. The anthropometric and serum biochemical parameters related to TSH, thyroid autoimmunity, lipid profiles, and sex steroids were detected.Results: The TSH level is higher in (2.29 ± 1.24 vs. 1.86 ± 0.90 mu/L, p < 0.001) in PCOS than controls. In euthyroid PCOS patients, TSH, TG, TC, LDL-c, and apoB level increased from non-hyperandrogenism (nonHA) to HA group (all p < 0.05). TSH level is positively associated with TG, apoB, free T, FAI, and negatively associated with apoA (all p < 0.05). The percentage of HA increased from TSH level (57.93% in TSH < = 2.5 group vs. 69.46% in TSH > 2.5 mU/L group, p = 0.006). HA phenotype is increased with TSH level independently of age, BMI, WC, LDL-C. Besides, in multivariate logistic regression analysis TSH and TG significantly associated with HA phenotype.Conclusions: Higher TSH level is associated with increased prevalence of HA phenotype independent of age, BMI and thyroid autoimmunity in euthyroid PCOS