Semiparametric estimation exploiting covariate independence in two-phase randomized trials.

Recent results for case-control sampling suggest when the covariate distribution is constrained by gene-environment independence, semiparametric estimation exploiting such independence yields a great deal of efficiency gain. We consider the efficient estimation of the treatment-biomarker interaction in two-phase sampling nested within randomized clinical trials, incorporating the independence between a randomized treatment and the baseline markers. We develop a Newton-Raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate (SPMLE). Our algorithm accommodates both continuous phase-one outcomes and continuous phase-two biomarkers. The profile information matrix is computed explicitly via numerical differentiation. In certain situations where computing the SPMLE is slow, we propose a maximum estimated likelihood estimator (MELE), which is also capable of incorporating the covariate independence. This estimated likelihood approach uses a one-step empirical covariate distribution, thus is straightforward to maximize. It offers a closed-form variance estimate with limited increase in variance relative to the fully efficient SPMLE. Our results suggest exploiting the covariate independence in two-phase sampling increases the efficiency substantially, particularly for estimating treatment-biomarker interactions

Comparison of Haplotype-based and Tree-based SNP Imputation in Association Studies

Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease association. In this article, we compare two haplotype-based imputation approaches and one regression tree-based imputation approach for association studies. The goal is to assess the imputation accuracy, and to evaluate the impact of imputation on parameter estimation. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case-control study on developmental dyslexia. Our results suggest that imputation generally improves over the standard practice of ignoring missing data in terms of bias and efficiency. The haplotype-based approaches slightly outperform the tree-based approach when there are a small number of SNPs in linkage disequilibrium (LD), but the latter has a computational advantage. Finally, we demonstrate that utilizing the disease status in imputation helps to reduce the bias in the subsequent parameter estimation

DMseg: a Python algorithm for de novo detection of differentially or variably methylated regions

Detecting and assessing statistical significance of differentially methylated regions (DMRs) is a fundamental task in methylome association studies. While the average differential methylation in different phenotype groups has been the inferential focus, methylation changes in chromosomal regions may also present as differential variability, i.e., variably methylated regions (VMRs). Testing statistical significance of regional differential methylation is a challenging problem, and existing algorithms do not provide accurate type I error control for genome-wide DMR or VMR analysis. No algorithm has been publicly available for detecting VMRs. We propose DMseg, a Python algorithm with efficient DMR/VMR detection and significance assessment for array-based methylome data, and compare its performance to Bumphunter, a popular existing algorithm. Operationally, DMseg searches for DMRs or VMRs within CpG clusters that are adaptively determined by both gap distance and correlation between contiguous CpG sites in a microarray. Levene test was implemented for assessing differential variability of individual CpGs. A likelihood ratio statistic is proposed to test for a constant difference within CpGs in a DMR or VMR to summarize the evidence of regional difference. Using a stratified permutation scheme and pooling null distributions of LRTs from clusters with similar numbers of CpGs, DMseg provides accurate control of the type I error rate. In simulation experiments, DMseg shows superior power than Bumphunter to detect DMRs. Application to methylome data of Barrett's esophagus and esophageal adenocarcinoma reveals a number of DMRs and VMRs of biological interest

Neural Networks with Recurrent Generative Feedback

Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that human brains use an internal generative model to update the posterior beliefs of the sensory input. This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment. Inspired by such hypothesis, we enforce self-consistency in neural networks by incorporating generative recurrent feedback. We instantiate this design on convolutional neural networks (CNNs). The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables to existing CNN architectures, where consistent predictions are made through alternating MAP inference under a Bayesian framework. In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks.Comment: NeurIPS 202

Neural Networks with Recurrent Generative Feedback

Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that human brains use an internal generative model to update the posterior beliefs of the sensory input. This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment. Inspired by such hypothesis, we enforce self-consistency in neural networks by incorporating generative recurrent feedback. We instantiate this design on convolutional neural networks (CNNs). The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables to existing CNN architectures, where consistent predictions are made through alternating MAP inference under a Bayesian framework. In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks

Critical Structure Sparing in Stereotactic Ablative Radiotherapy for Central Lung Lesions: Helical Tomotherapy vs. Volumetric Modulated Arc Therapy

Background Helical tomotherapy (HT) and volumetric modulated arc therapy (VMAT) are both advanced techniques of delivering intensity-modulated radiotherapy (IMRT). Here, we conduct a study to compare HT and partial-arc VMAT in their ability to spare organs at risk (OARs) when stereotactic ablative radiotherapy (SABR) is delivered to treat centrally located early stage non-small-cell lung cancer or lung metastases. Methods 12 patients with centrally located lung lesions were randomly chosen. HT, 2 & 8 arc (Smart Arc, Pinnacle v9.0) plans were generated to deliver 70 Gy in 10 fractions to the planning target volume (PTV). Target and OAR dose parameters were compared. Each technique’s ability to meet dose constraints was further investigated. Results HT and VMAT plans generated essentially equivalent PTV coverage and dose conformality indices, while a trend for improved dose homogeneity by increasing from 2 to 8 arcs was observed with VMAT. Increasing the number of arcs with VMAT also led to some improvement in OAR sparing. After normalizing to OAR dose constraints, HT was found to be superior to 2 or 8-arc VMAT for optimal OAR sparing (meeting all the dose constraints) (p = 0.0004). All dose constraints were met in HT plans. Increasing from 2 to 8 arcs could not help achieve optimal OAR sparing for 4 patients. 2/4 of them had 3 immediately adjacent structures. Conclusion HT appears to be superior to VMAT in OAR sparing mainly in cases which require conformal dose avoidance of multiple immediately adjacent OARs. For such cases, increasing the number of arcs in VMAT cannot significantly improve OAR sparing

Lyman Continuum Escape Fraction from Low-mass Starbursts at z = 1.3

We present a new constraint on the Lyman continuum (LyC) escape fraction at . We obtain deep, high sensitivity far-UV imaging with the Advanced Camera for Surveys Solar Blind Channel on the Hubble Space Telescope, targeting 11 star-forming galaxies at 1.2 190 Å, low stellar mass (M⋆ 3) in the individual galaxies or in the stack in the far-UV images. We place 3σ limits on the relative escape fraction of individual galaxies to be f_(esc,rel) < [0.10-0.22] and a stacked 3σ limit of f_(esc,rel) < 0.07. Measuring various galaxy properties, including stellar mass, dust attenuation, and star formation rate, we show that our measured values fall within the broad range of values covered by the confirmed LyC emitters from the literature. In particular, we compare the distribution of Hα and [O III] EWs of confirmed LyC emitters and non-detections, including the galaxies in this study. Finally, we discuss if a dichotomy seen in the distribution of Hα EWs can perhaps distinguish the LyC emitters from the non-detections

Genetic variants in the MRPS30 region and postmenopausal breast cancer risk

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Observation of TeV gamma rays from the Cygnus region with the ARGO-YBJ experiment

We report the observation of TeV gamma-rays from the Cygnus region using the ARGO-YBJ data collected from 2007 November to 2011 August. Several TeV sources are located in this region including the two bright extended MGRO J2019+37 and MGRO J2031+41. According to the Milagro data set, at 20 TeV MGRO J2019+37 is the most significant source apart from the Crab Nebula. No signal from MGRO J2019+37 is detected by the ARGO-YBJ experiment, and the derived flux upper limits at 90% confidence level for all the events above 600 GeV with medium energy of 3 TeV are lower than the Milagro flux, implying that the source might be variable and hard to be identified as a pulsar wind nebula. The only statistically significant (6.4 standard deviations) gamma-ray signal is found from MGRO J2031+41, with a flux consistent with the measurement by Milagro.Comment: 14 pages, 4 figure