Covariate-assisted ranking and screening for large-scale two-sample inference

Two-sample multiple testing has a wide range of applications. The conventionalpractice first reduces the original observations to a vector of p-values and then chooses a cutoffto adjust for multiplicity. However, this data reduction step could cause significant loss ofinformation and thus lead to suboptimal testing procedures.We introduce a new framework fortwo-sample multiple testing by incorporating a carefully constructed auxiliary variable in inferenceto improve the power. A data-driven multiple-testing procedure is developed by employinga covariate-assisted ranking and screening (CARS) approach that optimally combines the informationfrom both the primary and the auxiliary variables. The proposed CARS procedureis shown to be asymptotically valid and optimal for false discovery rate control. The procedureis implemented in the R package CARS. Numerical results confirm the effectiveness of CARSin false discovery rate control and show that it achieves substantial power gain over existingmethods. CARS is also illustrated through an application to the analysis of a satellite imagingdata set for supernova detection

A-to-I RNA Editing in Human Cells

RNA editing is a means of diversifying the transcriptome and regulating innate immunity. Among the different classes of enzymes that modify RNA, adenosine deaminase acting on RNA (ADAR) is a type that catalyzes adenosine-to-inosine editing on double-stranded RNA molecules to regulate cellular responses to endogenous and exogenous RNA. Of the three ADAR homologs in humans, dysregulation of ADAR1 editing due to inherited mutations leads to disorders such as Aicardi-Goutieres syndrome, an inflammatory disease that manifests in the brain and skin, and dyschromatosis symmetrica hereditaria, a skin pigmentation disorder. ADAR1 is the primary A-to-I editor of RNA in humans, and the majority of edit sites are found in a class of repetitive elements called Alu, many of which are located in introns and 3’ untranslated regions of RNA. The functional consequences of A-to-I editing are varied, although a complete lack of functional ADAR1 is usually not tolerated, as revealed by the MDA5-mediated embryonic lethality in mice lacking functional ADAR1. In human neural progenitor cells, loss of ADAR1 causes spontaneous upregulation of interferon and cell death, although the RNA triggers remain unknown. Given the importance of ADAR1-editing in maintaining homeostasis in various contexts, there is a need to understand in more detail how ADAR1 isoforms are regulated and how they individually contribute to the A-to-I RNA editome. Two ADAR1 protein isoforms, p110 (110 kDa) and p150 (150 kDa), are expressed constitutively and in response to interferon, respectively, but the contribution of each isoform to the editing landscape remains incompletely characterized, largely because of the challenges in expressing p150 without p110. We revealed that the p110 isoform can be expressed from the canonical p150-encoding mRNA due to leaky ribosome scanning downstream of the p150 start codon. Synonymous mutations introduced in the region between the p150 and p110 start codons reduce leaky scanning and usage of the p110 start codon, and cells expressing p150 constructs with these mutations produce significantly reduced levels of p110. With the ability to express p150 with significantly reduced levels of p110, the A-to-I editome can be classified in terms of p150-selective and p110-selective sites, allowing evaluation of the relative contributions of either isoform to global editing levels. Our editing analysis revealed that the majority of ADAR1-edit sites are p150-selective, although a significant proportion of ADAR1-edit sites are also shared between p150 and p110, being not dependent on presence of either isoform for editing to occur. Of the sites that are putatively p110- selective, the majority are located in introns. Finally, the ability of p150 mRNA to give rise to p110 means that p110 is also an interferon-inducible protein alongside the canonical interferon-stimulated ADAR1 isoform: p150. During the interferon response, the transcriptome changes, and many new mRNA structures, perhaps some immunogenic ones, will enter the nucleus and cytoplasm. The distribution of ADAR1 isoforms is such that p110 is mostly present in the nucleus, and p150 mostly in the cytoplasm. We propose that optimal editing in the nucleus and cytoplasm during the interferon response is achieved by the inducibility of p110 and p150, both of which share a large number of target sites

Weighted False Discovery Rate Control in Large-Scale Multiple Testing

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to genome-wide association study is discussed.Comment: Revise

Optimal Screening and Discovery of Sparse Signals with Applications to Multistage High-throughput Studies

A common feature in large-scale scientific studies is that signals are sparse and it is desirable to significantly narrow down the focus to a much smaller subset in a sequential manner. In this paper, we consider two related data screening problems: One is to find the smallest subset such that it virtually contains all signals and another is to find the largest subset such that it essentially contains only signals. These screening problems are closely connected to but distinct from the more conventional signal detection or multiple testing problems. We develop data-driven screening procedures which control the error rates with near optimality properties and study how to design the experiments efficiently to achieve the goals in data screening. A class of new phase diagrams is developed to characterize the fundamental limitations in simultaneous inference. An application to multistage high-throughput studies is given to illustrate the merits of the proposed screening methods

Fill Materials at Integral End Bents

Jointless bridge designs have become increasingly popular due to their low construction and maintenance costs. But this design carries risks. Most notably, integral end bents can be displaced and undergo settlement due to soil movement in embankments and loads carried by the superstructure. In response, the Kentucky Transportation Cabinet (KYTC) devised a novel treatment for end bent and abutment backfills on low- and middle-span concrete bridges in which elasticized geofoam is placed between geosynthetically confined soil and an integral end bent (GCS-IEB). However, this design requires modification where the elasticized geofoam and overlying pavement meet. Using elasticized geofoam is also costly. In response, this study identifies less expensive substitutes for elasticized geofoam that would not be damaged by bridge movements and which would reduce the settlement of integral end bents. Two promising materials were evaluated whose properties are similar to elasticized geofoam but which cost significantly less — shredded tire chips and recycled tire granules. Using a new lab procedure, researchers evaluated the recoverable deformation and maximum resistant stress of different samples, ultimately identifying a recycled tire derivative that is the best low-cost alternative to elasticized geofoam. Step-by-step installation methods are provided to guide the onsite installation of alternative materials. One method applies to recycled tire derivatives delivered in bags, while the other applies to materials that delivered in bulk and placed into baskets onsite

Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain.Peer reviewe