On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models

We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting. Such models form a rich class of non-linear models with applications to signal processing, machine learning and statistics. Our estimators leverage the score function based first and second-order Stein's identities and do not require the covariates to satisfy Gaussian or elliptical symmetry assumptions common in the literature. Moreover, to handle score functions and responses that are heavy-tailed, our estimators are constructed via carefully thresholding their empirical counterparts. We show that our estimator achieves near-optimal statistical rate of convergence in several settings. We supplement our theoretical results via simulation experiments that confirm the theory

Resistance to TGFβ suppression and improved anti-tumor responses in CD8+ T cells lacking PTPN22

Transforming growth factor β (TGFβ) is important in maintaining self-tolerance and inhibits T cell reactivity. We show that CD8⁺ T cells that lack the tyrosine phosphatase Ptpn22, a major predisposing gene for autoimmune disease, are resistant to the suppressive effects of TGFβ. Resistance to TGFβ suppression, while disadvantageous in autoimmunity, helps Ptpn22‾/‾ T cells to be intrinsically superior at clearing established tumors that secrete TGFβ. Mechanistically, loss of Ptpn22 increases the capacity of T cells to produce IL-2, which overcomes TGFβ-mediated suppression. These data suggest that a viable strategy to improve anti-tumor adoptive cell therapy may be to engineer tumor-restricted T cells with mutations identified as risk factors for autoimmunity

(a) A 57-year-old women with right breast cancer underwent ¹⁸F–FDG PET/CT. Mild ¹⁸F–FDG uptake (inferior to the liver) resulted in her being classified into the low uptake group. Her BMI was 20.0, and triglyceride level was 45 mg/dL. (b) A 64-year-old women with left breast cancer underwent ¹⁸F–FDG PET/CT. Intense ¹⁸F–FDG uptake along the intestine was classified into the high uptake group. Her BMI was 27.3, and triglyceride level was 393 mg/dL.

<p>(a) A 57-year-old women with right breast cancer underwent ¹⁸F–FDG PET/CT. Mild ¹⁸F–FDG uptake (inferior to the liver) resulted in her being classified into the low uptake group. Her BMI was 20.0, and triglyceride level was 45 mg/dL. (b) A 64-year-old women with left breast cancer underwent ¹⁸F–FDG PET/CT. Intense ¹⁸F–FDG uptake along the intestine was classified into the high uptake group. Her BMI was 27.3, and triglyceride level was 393 mg/dL.</p

Results of univariate and multivariate regression analyses.

<p>HDL, high-density lipoprotein; LDL, low-density lipoprotein</p><p>*<i>p</i><0.05</p><p>Results of univariate and multivariate regression analyses.</p

Association between Personality Traits and Sleep Quality in Young Korean Women

<div><p>Personality is a trait that affects behavior and lifestyle, and sleep quality is an important component of a healthy life. We analyzed the association between personality traits and sleep quality in a cross-section of 1,406 young women (from 18 to 40 years of age) who were not reporting clinically meaningful depression symptoms. Surveys were carried out from December 2011 to February 2012, using the Revised NEO Personality Inventory and the Pittsburgh Sleep Quality Index (PSQI). All analyses were adjusted for demographic and behavioral variables. We considered beta weights, structure coefficients, unique effects, and common effects when evaluating the importance of sleep quality predictors in multiple linear regression models. Neuroticism was the most important contributor to PSQI global scores in the multiple regression models. By contrast, despite being strongly correlated with sleep quality, conscientiousness had a near-zero beta weight in linear regression models, because most variance was shared with other personality traits. However, conscientiousness was the most noteworthy predictor of poor sleep quality status (PSQI≥6) in logistic regression models and individuals high in conscientiousness were least likely to have poor sleep quality, which is consistent with an OR of 0.813, with conscientiousness being protective against poor sleep quality. Personality may be a factor in poor sleep quality and should be considered in sleep interventions targeting young women.</p></div

Correlation coefficients for sleep quality, covariates, and personality traits.

<p>N = 1,406</p><p>^a Pearson’s correlation coefficients</p><p>^b Spearman correlation coefficients</p><p>*<i>p</i><.05</p><p>**<i>p</i><.01</p><p>***<i>p</i><.001</p><p>Correlation coefficients for sleep quality, covariates, and personality traits.</p

Scatter plots of age (a), body mass index (b), triglyceride (c), cholesterol (d), low-density lipoprotein (e), and high-density lipoprotein (f) according TB SUV_max.

<p>Scatter plots of age (a), body mass index (b), triglyceride (c), cholesterol (d), low-density lipoprotein (e), and high-density lipoprotein (f) according TB SUV_max.</p

Logistic regression models predicting poor sleep quality based on personality characteristics.

<p>N = 1,406</p><p><i>Note</i>. CI: Wald Confidence Interval</p><p>^a Odds Ratios (ORs) per 10 <i>T</i>-score increase in a given personality trait, controlling for age, marital status, working status, education, caffeine intake, alcohol use, smoking status, and physical activity.</p><p>^b Model I: logistic regression model including a single domain or facet of personality as an independent variable.</p><p>^c Model II: multiple logistic regression model including all five domains of personality as independent variables. At a facet level, six facets of each domain were included in the model.</p><p>^d Logistic regression analyses of the facet level were performed in neuroticism and conscientiousness.</p><p>*<i>p</i><.05</p><p>**<i>p</i><.01</p><p>***<i>p</i><.001</p><p>Logistic regression models predicting poor sleep quality based on personality characteristics.</p

Discriminant function analysis (DFA) by the stepwise method for the separation between good sleepers and poor sleepers.

<p>N = 1,406</p><p>Discriminant function analysis (DFA) by the stepwise method for the separation between good sleepers and poor sleepers.</p

Sample selection procedure.

<p>Some participants were excluded for multiple criteria.</p