Model‐Based Quality Assessment and Base‐Calling for Second‐Generation Sequencing Data

Summary Second‐generation sequencing (sec‐gen) technology can sequence millions of short fragments of DNA in parallel, making it capable of assembling complex genomes for a small fraction of the price and time of previous technologies. In fact, a recently formed international consortium, the 1000 Genomes Project, plans to fully sequence the genomes of approximately 1200 people. The prospect of comparative analysis at the sequence level of a large number of samples across multiple populations may be achieved within the next five years. These data present unprecedented challenges in statistical analysis. For instance, analysis operates on millions of short nucleotide sequences, or reads—strings of A,C,G, or T's, between 30 and 100 characters long—which are the result of complex processing of noisy continuous fluorescence intensity measurements known as base‐calling. The complexity of the base‐calling discretization process results in reads of widely varying quality within and across sequence samples. This variation in processing quality results in infrequent but systematic errors that we have found to mislead downstream analysis of the discretized sequence read data. For instance, a central goal of the 1000 Genomes Project is to quantify across‐sample variation at the single nucleotide level. At this resolution, small error rates in sequencing prove significant, especially for rare variants. Sec‐gen sequencing is a relatively new technology for which potential biases and sources of obscuring variation are not yet fully understood. Therefore, modeling and quantifying the uncertainty inherent in the generation of sequence reads is of utmost importance. In this article, we present a simple model to capture uncertainty arising in the base‐calling procedure of the Illumina/Solexa GA platform. Model parameters have a straightforward interpretation in terms of the chemistry of base‐calling allowing for informative and easily interpretable metrics that capture the variability in sequencing quality. Our model provides these informative estimates readily usable in quality assessment tools while significantly improving base‐calling performance.     

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

Summary ChIP‐chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein–DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP‐chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP‐chip data through an Ising model with high‐order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios.     

Statistical Modeling of Observational Data With Spatial Dependencies

Abstract I provide a brief introduction to the concept of spatial autocorrelation and its incorporation into regression-type models. Spatial autocorrelation occurs when the response variable is correlated with itself at other locations in the region of interest. The autocorrelation usually takes a specific form where observations close in space are more correlated than those farther apart, and the rate of decay of the correlation is a function of the distance separating 2 locations. I present 2 commonly used models: 1) geostatistical modeling in which data are collected at points in the study region and 2) conditional autoregression (lattice) models in which data are aggregated over small nonoverlapping sub-areas of the study region. I also describe incorporation of explanatory covariates, such as habitat or physico-chemical attributes. I emphasize frequentist methods, but I briefly describe Bayesian approaches. I also provide some advantages, such as obtaining correct standard errors for estimators, and disadvantages, such as requirements for larger sample sizes, of incorporating spatial autocorrelation into the modeling effort. This information can aid researchers in designing and analyzing models of the relationships between species distributions and habitat. As a result, more informative models can be developed which further aid in management of wildlife.     

Nanoarchitectured Array Electrodes for Rechargeable Lithium‐ and Sodium‐Ion Batteries

Rechargeable ion batteries have contributed immensely to shaping the modern world and been seriously considered for the efficient storage and utilization of intermittent renewable energies. To fulfill their potential in the future market, superior battery performance of high capacity, great rate capability, and long lifespan is undoubtedly required. In the past decade, along with discovering new electrode materials, the focus has been shifting more and more toward rational electrode designs because the performance is intimately connected to the electrode architectures, particularly their designs at the nanoscale that can alleviate the reliance on the materials' intrinsic nature. The utilization of nanoarchitectured arrays in the design of electrodes has been proven to significantly improve the battery performance. A comprehensive summary of the structural features and fabrications of the nanoarchitectured array electrodes is provided, and some of the latest achievements in the area of both lithium‐ and sodium‐ion batteries are highlighted. Finally, future challenges and opportunities that would allow further development of such advanced electrode configuration are discussed.     

High‐Quality Biobanks: Pivotal Assets for Reproducibility of OMICS‐Data in Biomedical Translational Research

Human biospecimen samples (HBS) and associated data stored in biobanks (also called “biotrusts,” “biorepositories,” or “biodistributors”) are very critical resources for translational research. As HBS quality is decisive to the reproducibility of research results, biobanks are also key assets for new developments in precision medicine. Biobanks are more than infrastructures providing HBS and associated data. Biobanks have pioneered in identifying and standardizing sources of preanalytical variations in HBS, thus paving the way for the current biospecimen science. To achieve this milestone, biobankers have successively assumed the role of “detective,” and then “architect,” to identify new detrimental impact of preanalytical variables on the tissue integrity. While standardized methods in omics are required to be practiced throughout research communities, the accepted best practices and standards on biospecimen handling are generally not known nor applied by researchers. Therefore, it is mandatory to raise the awareness within omics communities regarding not only the basic concepts of collecting, storing, and utilizing HBS today, but also to suggest insights on biobanking in the cancer omics context.     

Integrated High‐Efficiency Pt/Carbon Nanotube Arrays for PEM Fuel Cells

A facile strategy to deposit Pt nanoparticles with various metal‐loading densities on vertically aligned carbon nanotube (ACNT) arrays as electrocatalysts for proton exchange membrane (PEM) fuel cells is described. The deposition is achieved by electrostatic adsorption of the Pt precursor on the positively charged polyelectrolyte functionalized ACNT arrays and subsequent reduction by L ‐ascorbic acid. The application of the aligned electrocatalysts in fuel cells is realized by transferring from a quartz substrate to nafion membrane using a hot‐press procedure to fabricate the membrane electrode assembly (MEA). It is shown that the MEA with vertically aligned structured electrocatalysts provides better Pt utilization than that with Pt on conventional carbon nanotubes or carbon black, resulting in higher fuel cell performance.     

Order‐restricted Scores Test for the Evaluation of Population‐based Case–control Studies when the Genetic Model is Unknown

The Cochran–Armitage (CA) linear trend test for proportions is often used for genotype‐based analysis of candidate gene association. Depending on the underlying genetic mode of inheritance, the use of model‐specific scores maximises the power. Commonly, the underlying genetic model, i.e. additive, dominant or recessive mode of inheritance, is a priori unknown. Association studies are commonly analysed using permutation tests, where both inference and identification of the underlying mode of inheritance are important. Especially interesting are tests for case–control studies, defined by a maximum over a series of standardised CA tests, because such a procedure has power under all three genetic models. We reformulate the test problem and propose a conditional maximum test of scores‐specific linear‐by‐linear association tests. For maximum‐type, sum and quadratic test statistics the asymptotic expectation and covariance can be derived in a closed form and the limiting distribution is known. Both the limiting distribution and approximations of the exact conditional distribution can easily be computed using standard software packages. In addition to these technical advances, we extend the area of application to stratified designs, studies involving more than two groups and the simultaneous analysis of multiple loci by means of multiplicity‐adjusted p‐values for the underlying multiple CA trend tests. The new test is applied to reanalyse a study investigating genetic components of different subtypes of psoriasis. A new and flexible inference tool for association studies is available both theoretically as well as practically since already available software packages can be easily used to implement the suggested test procedures.     

An Antiaging Electrolyte Additive for High‐Energy‐Density Lithium‐Ion Batteries

High‐capacity Li‐rich layered oxide cathodes along with Si‐incorporated graphite anodes have high reversible capacity, outperforming the electrode materials used in existing commercial products. Hence, they are potential candidates for the development of high‐energy‐density lithium‐ion batteries (LIBs). However, structural degradation induced by loss of interfacial stability is a roadblock to their practical use. Here, the use of malonic acid‐decorated fullerene (MA‐C₆₀) with superoxide dismutase activity and water scavenging capability as an electrolyte additive to overcome the structural instability of high‐capacity electrodes that hampers the battery quality is reported. Deactivation of PF₅ by water scavenging leads to the long‐term stability of the interfacial structures of electrodes. Moreover, an MA‐C₆₀‐added electrolyte deactivates the reactive oxygen species and constructs an electrochemically robust cathode‐electrolyte interface for Li‐rich cathodes. This work paves the way for new possibilities in the design of electrolyte additives by eliminating undesirable reactive substances and tuning the interfacial structures of high‐capacity electrodes in LIBs.     

Robust Selection of the Most Discriminative Variables in the Dichotomous Problem with Application to Some Respiratory Disease Data

A robust method for selection of variables with the greatest discriminatory power is presented in the paper. The method deals with the two groups of data problem. An application of the method to some respiratory disease data and comparisons with classical procedures are given, also.     

Pattern‐Mixture Zero‐Inflated Mixed Models for Longitudinal Unbalanced Count Data with Excessive Zeros

Analysis of longitudinal data with excessive zeros has gained increasing attention in recent years; however, current approaches to the analysis of longitudinal data with excessive zeros have primarily focused on balanced data. Dropouts are common in longitudinal studies; therefore, the analysis of the resulting unbalanced data is complicated by the missing mechanism. Our study is motivated by the analysis of longitudinal skin cancer count data presented by Greenberg, Baron, Stukel, Stevens, Mandel, Spencer, Elias, Lowe, Nierenberg, Bayrd, Vance, Freeman, Clendenning, Kwan, and the Skin Cancer Prevention Study Group[New England Journal of Medicine 323 , 789–795]. The data consist of a large number of zero responses (83% of the observations) as well as a substantial amount of dropout (about 52% of the observations). To account for both excessive zeros and dropout patterns, we propose a pattern‐mixture zero‐inflated model with compound Poisson random effects for the unbalanced longitudinal skin cancer data. We also incorporate an autoregressive of order 1 correlation structure in the model to capture longitudinal correlation of the count responses. A quasi‐likelihood approach has been developed in the estimation of our model. We illustrated the method with analysis of the longitudinal skin cancer data.     

High‐Quality Graphene Microflower Design for High‐Performance Li–S and Al‐Ion Batteries

Poor quality and insufficient productivity are two main obstacles for the practical application of graphene in electrochemical energy storage. Here, high‐quality crumpled graphene microflower (GmF) for high‐performance electrodes is designed. The GmF possesses four advantages simultaneously: highly crystallized defect‐free graphene layers, low stacking degree, sub‐millimeter continuous surface, and large productivity with low cost. When utilized as carbon host for sulfur cathode, the GmF‐sulfur hybrid delivers decent areal capacities of 5.2 mAh cm^?2 at 0.1 C and 3.8 mAh cm^?2 at 0.5 C. When utilized as cathode of Al‐ion battery, the GmF affords a high capacity of 100 mAh g^?1 with 100% capacity retention after 5000 cycles and excellent rate capability from 0.1 to 20 A g^?1. This facile and large‐scale producible GmF represents a meaningful high‐quality graphene powder for practical energy storage technology. Meanwhile, this unique high‐quality graphene design provides an effective route to improve electrochemical properties of graphene‐based electrodes.     

Pairwise Variable Selection for High‐Dimensional Model‐Based Clustering

Summary Variable selection for clustering is an important and challenging problem in high‐dimensional data analysis. Existing variable selection methods for model‐based clustering select informative variables in a “one‐in‐all‐out” manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high‐dimensional model‐based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than alternative approaches that use ?₁ and ?_∞ penalties and offers better interpretation.     

High‐Dimensional Variable Selection in Meta‐Analysis for Censored Data

Summary This article considers the problem of selecting predictors of time to an event from a high‐dimensional set of candidate predictors using data from multiple studies. As an alternative to the current multistage testing approaches, we propose to model the study‐to‐study heterogeneity explicitly using a hierarchical model to borrow strength. Our method incorporates censored data through an accelerated failure time model. Using a carefully formulated prior specification, we develop a fast approach to predictor selection and shrinkage estimation for high‐dimensional predictors. For model fitting, we develop a Monte Carlo expectation maximization (MC‐EM) algorithm to accommodate censored data. The proposed approach, which is related to the relevance vector machine (RVM), relies on maximum a posteriori estimation to rapidly obtain a sparse estimate. As for the typical RVM, there is an intrinsic thresholding property in which unimportant predictors tend to have their coefficients shrunk to zero. We compare our method with some commonly used procedures through simulation studies. We also illustrate the method using the gene expression barcode data from three breast cancer studies.     

High‐Performance Fiber‐Shaped All‐Solid‐State Asymmetric Supercapacitors Based on Ultrathin MnO2 Nanosheet/Carbon Fiber Cathodes for Wearable Electronics

Flexible fiber‐shaped supercapacitors have shown great potential in portable and wearable electronics. However, small specific capacitance and low operating voltage limit the practical application of fiber‐shaped supercapacitors in high energy density devices. Herein, direct growth of ultrathin MnO₂ nanosheet arrays on conductive carbon fibers with robust adhesion is exhibited, which exhibit a high specific capacitance of 634.5 F g^?1 at a current density of 2.5 A g^?1 and possess superior cycle stability. When MnO₂ nanosheet arrays on carbon fibers and graphene on carbon fibers are used as a positive electrode and a negative electrode, respectively, in an all‐solid‐state asymmetric supercapacitor (ASC), the ASC displays a high specific capacitance of 87.1 F g^?1 and an exceptional energy density of 27.2 Wh kg^?1. In addition, its capacitance retention reaches 95.2% over 3000 cycles, representing the excellent cyclic ability. The flexibility and mechanical stability of these ASCs are highlighted by the negligible degradation of their electrochemical performance even under severely bending states. Impressively, as‐prepared fiber‐shaped ASCs could successfully power a photodetector based on CdS nanowires without applying any external bias voltage. The excellent performance of all‐solid‐state ASCs opens up new opportunity for development of wearable and self‐powered nanodevices in near future.     

Bioinspired Surface Layer for the Cathode Material of High‐Energy‐Density Sodium‐Ion Batteries

Cathode materials are usually active in the range of 2–4.3 V, but the decomposition of the electrolytic salt above 4 V versus Na⁺/Na is common. Arguably, the greatest concern is the formation of HF after the reaction of the salts with water molecules, which are present as an impurity in the electrolyte. This HF ceaselessly attacks the active materials and gradually causes the failure of the electrode via electric isolation of the active materials. In this study, a bioinspired β‐NaCaPO₄ nanolayer is reported on a P2‐type layered Na_2/3[Ni_1/3Mn_2/3]O₂ cathode material. The coating layers successfully scavenge HF and H₂O, and excellent capacity retention is achieved with the β‐NaCaPO₄‐coated Na_2/3[Ni_1/3Mn_2/3]O₂ electrode. This retention is possible because a less acidic environment is produced in the Na cells during prolonged cycling. The intrinsic stability of the coating layer also assists in delaying the exothermic decomposition reaction of the desodiated electrodes. Formation and reaction mechanisms are suggested for the coating layers responsible for the excellent electrode performance. The suggested technology is promising for use with cathode materials in rechargeable sodium batteries to mitigate the effects of acidic conditions in Na cells.     

High‐throughput profiling of N‐myristoylation substrate specificity across species including pathogens

One of the most critical modifications affecting the N‐terminus of proteins is N‐myristoylation. This irreversible modification affects the membrane‐binding properties of crucial proteins involved in signal transduction cascades. This cotranslational modification, catalyzed by N‐myristoyl transferase, occurs both in lower and higher eukaryotes and is a validated therapeutic target for several pathologies. However, this lipidation proves very difficult to be evidenced in vivo even with state‐of‐the‐art proteomics approaches or bioinformatics tools. A large part of N‐myristoylated proteins remains to be discovered and the rules of substrate specificity need to be established in each organism. Because the peptide substrate recognition occurs around the first eight residues, short peptides are used for modeling the reaction in vitro. Here, we provide a novel approach including a dedicated peptide array for high‐throughput profiling protein N‐myristoylation specificity. We show that myristoylation predictive tools need to be fine‐tuned to organisms and that their poor accuracy should be significantly enhanced. This should lead to strongly improved knowledge of the number and function of myristoylated proteins occurring in any proteome.