Adaptive Sum Research Articles

Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers.

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism(SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based(aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortiumwhole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.

Robust filtering of ghost and peg-leg from sea floor seismic data

ABSTRACT. The processing of compressional waves from the acoustic components of ocean bottom seismic data is performed decomposing the compressional wavefield on its upgoing an downgoing components, treating the first order multiples, composed by the overlap between the receiver ghost and peg-leg. The separation of these wavefields is achieved through the adaptive summation of the hydrophone and geophone components, usually on a least square sense. This method of separation is known as PZ summation, because it involves an operation between pressure and vertical particle velocity measurements. However, due to the difference in response of the pressure and velocity sensors, the premises assumed on the least square summation can be violated, degrading the results. To overcome these difficulties, a more robust method can be achieved using the L1 norm criterion for the adaptive sum. A comparison was made between the results obtained with the Iterative Reweighted Least Squares and Wiener-Levinson filters. The robustness of the L1 norm sum was demonstrated by applications on PZ summation of Ocean Bottom Cable data from the Jubarte area, in the Campos Basin, Brazil, showing improvements, especially when the multiples are present in the estimation window used to derive the filter coefficients.

Genetic variants associated with ALT elevation from therapeutic acetaminophen

Background Several studies have suggested genetic variants associated with acetaminophen induced liver injury (DILI) following overdose. Genetic variation associated with acetaminophen-induced alanine aminotransferase elevation during therapeutic dosing has not been examined. Methods We performed genetic analyses on patients that ingested therapeutic doses of 4 grams of acetaminophen for up to 16 days. We examined 20 genes previously implicated in the metabolism of acetaminophen or the development of immune-mediated DILI using the Illumina Multi-Ethnic Global Array 2. Autosomes were aligned and imputed using TOPMed. A candidate gene region analysis was performed by testing each gene individually using linkage disequilibrium (LD) pruned variants with the adaptive sum of powered scores (aSPU) test from the aSPU R package. The highest measured ALT during therapy, the maximum ALT, was used as the outcome. Results 192 subjects taking therapeutic APAP were included in the genetic analysis. 136 (70.8%) were female, 133 (69.2%) were Caucasian race, and the median age was 34 years (IQR: 26, 46). Age > 50 years was the only clinical factor associated with maximum ALT increase. Variants in SULT1E1, the gene responsible for Sulfotransferase Family 1E Member 1 enzyme production, were associated with maximum ALT. No single variant drove this association, but rather the association was due to the additive effects of numerous variants within the gene. No other genes were associated with maximum ALT increase in this cohort. Conclusion Acetaminophen induced ALT elevation at therapeutic doses was not associated with variation in most genes associated with acetaminophen metabolism or immune-induced DILI in this cohort. The role of SULT1E1 polymorphism in acetaminophen-induced elevated ALT needs further examination.

An adaptive cumulative sum method for monitoring integer-valued time-series data

In this paper, we propose an adaptive CUSUM monitoring method for detecting step and linear trend changes in count-data time-series. The data is represented using a seasonal INGARCH time series model and an exponential smoother is used to estimate level or trend changes in the data in the cumulative-sum (CUSUM) detector. In a simulation study, the proposed approach is compared to existing CUSUM approaches that are tuned for a specific shift size and the ability of the methods to detect step shifts and linear trends is investigated. The application of the proposed method in public health surveillance is demonstrated using a real infectious disease count data set.

Multi-difference beams adaptive iterative monopulse estimation method for airborne radar

In the process of airborne radar parameters estimation, on the one hand, the adaptive sum and difference beam will be distorted near the main lobe clutter region; on the other hand, there is a coupling between the angle and Doppler frequency parameter estimation, which leads to the parameter estimation performance of the traditional sum and difference pulse estimation method severely degradation. In this paper, an adaptive iterative monopulse estimation method based on multi-difference beams is proposed. Firstly, two virtual difference beams are constructed based on the existing spatial and Doppler difference beams, secondly, the derivative constraint and zero constraint ensure that the formed difference beam is the optimal difference beam, thirdly, the generalized monopulse method is used to estimate parameters, and the mutual coupling problem of multi-parameter estimation is solved. Finally, the estimation accuracy of the target parameter is further improved by adaptive iteration. Simulation results verify the effectiveness of the proposed method.

Integrating brain imaging endophenotypes with GWAS for Alzheimer's disease.

Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer's disease (AD). These susceptibility loci may effect AD indirectly through a combination of physiological brain changes. Many of these neuropathologic features are detectable via magnetic resonance imaging (MRI). In this study, we examine the effects of such brain imaging derived phenotypes (IDPs) with genetic etiology on AD, using and comparing the following methods: two-sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome wide association studies (TWAS) and the adaptive sum of powered score (aSPU) test. These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics. Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project (IGAP) and UK Biobank's (UKBB) brain imaging initiatives, we identify 35 IDPs possibly associated with AD, many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease. Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods, i.e., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables).

A novel hypersonic vehicle-borne multichannel SAR-GMTI scheme based on adaptive sum and difference beams within eigenspace

This paper investigates a novel ground moving target indication (GMTI) scheme for a hypersonic vehicle-borne multichannel synthetic aperture radar (MC-SAR) system with Doppler ambiguity. First, after the range compression and azimuth chirp Fourier transform (CFT), by applying the eigenspace-based adaptive sum and difference beams (EASDB) on the coarse-focused images, the high-precision cross-track velocity (CTV) can be retrieved. Then, utilizing the high-precision CTV to construct an accurate moving target steering vector to extract the moving target energy. With the proposed CFT-two-step method, we have avoided cumbersome CTV search, and simplified the process of range walk correction, resulting in lower computational complexity. Compared to the CFT-center and CFT-two-step methods, the proposed scheme obtains the accurate two-dimension motion parameters of a moving target, and has better performance and robustness in the detection, focusing, and location, preserving the moving target envelope and achieving higher signal-to-clutter-and-noise ratio (SCNR). Finally, using the processing results of simulation and real measured MC-SAR data, we verify the effectiveness and feasibility of our proposed scheme.

Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling.

A central but challenging problem in genetic studies is to test for (usually weak) associations between a complex trait (e.g., a disease status) and sets of multiple genetic variants. Due to the lack of a uniformly most powerful test, data-adaptive tests, such as the adaptive sum of powered score (aSPU) test, are advantageous in maintaining high power against a wide range of alternatives. However, there is often no closed-form to accurately and analytically calculate the p-values of many adaptive tests like aSPU, thus Monte Carlo (MC) simulations are often used, which can be time consuming to achieve a stringent significance level (e.g., 5e-8) used in genome-wide association studies (GWAS). To estimate such a small p-value, we need a huge number of MC simulations (e.g., 1e+10). As an alternative, we propose using importance sampling to speed up such calculations. We develop some theory to motivate a proposed algorithm for the aSPU test, and show that the proposed method is computationally more efficient than the standard MC simulations. Using both simulated and real data, we demonstrate the superior performance of the new method over the standard MC simulations.

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test. We identified 6 novels (CD36, PITX2, EMB, ZNF592, YPEL2, and BC043580) and 87 known loci (adaptive sum of powered score test P<5×10-9). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci. Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

Adaptive Sum of Markov Chains for Modelling 3D Blockage in mmWave V2I Communications

Blockage attenuation is one of the main challenges that face reliable communication at the millimetre-wave (mmWave) band, especially for dynamic vehicle-to-infrastructure (V2I) communication systems. Modelling the dynamics of the blockage is important for evaluating high gain beamforming techniques, which are used to improve the signal strength in the mmWave communication systems. The novel sum of Markov chains (sum of MC) model is designed to do two main tasks successfully; capturing the dynamics of blockers affecting a moving transceiver and computing the arising channel attenuation. The sum of MC model has advantages over existing Markov chains models, which are: that it can adapt to model non-stationary scenarios. The sum of MC model can integrate any attenuation function, including the 3GPP blockage model and any lab measurement attenuation profile. Additionally, it is computationally efficient. The sum of MC model can match well with the performance results from a more complex geometric channel model.

198-LB: Pleiotropy Analysis of Adiposity, Glycemic, and Renal Traits in the Population Architecture Using Genomics in Epidemiology (PAGE) Consortium

A shared genetic susceptibility underlies diabetes, adiposity, and kidney phenotypes, yet few studies have leveraged this common genetic architecture in an attempt to clarify molecular functions, identify mechanistic common denominators, and prioritize variants for functional interrogation. We systematically interrogated evidence of a shared genetic architecture across adiposity (BMI [N=90,813], waist circumference[N=68,389], and waist to hip ratio [WHR, N=63,747]), type 2 diabetes (T2D, [N=98,686, Ncase=26,440], fasting glucose [FG, N=52,211], HbA1c [N=23,357], and fasting insulin [FI, N=48,395]), and kidney (eGFR [N=52,555] and chronic kidney disease [CKD, N=91,382, Ncase=10,883]) phenotypes in an ancestrally diverse study population (28% African American, 31% European ancestry, 28% Hispanic; 69% female; average age 56.9 (range 18-90)) within PAGE. We conducted a cross-trait meta-analysis using Adaptive Sum of Powered Score Tests (aSPU). We have identified 42 loci associated across these traits, (P&amp;lt;5e-8), including one preliminary novel cross-trait association for rs200812701, a nonsynonymous variant in KIF4B p.Ala491Val that was associated with BMI, waist circumference, and HbA1c. rs200812701 is rare (minor allele frequency [MAF]&amp;lt;0.1%) in all populations except Native Hawaiians (MAF=25%) and Asians (MAF=8%). Many of these loci are known for one trait, but the identification of shared genetic susceptibility for these loci is novel. For example, the PEPD locus is established for adiposity measures, namely WHR; however, this cross-trait association is driven more so by FI, T2D and eGFR. Our systematic interrogation of the shared genetic susceptibility underlying diabetes, adiposity, and kidney traits in an ancestrally diverse population has revealed novel loci and evidence of molecular functions and mechanistic common denominators underlying GWAS findings, demonstrating the utility of our approach. Disclosure H.M. Highland: None. C. Downie: None. S.F. Dimos: None. M. Graff: None. L.M. Polfus: None. B.F. Darst: None. A.R.M. Baldassari: None. C. Sitlani: None. A. Howard: None. C.L. Kooperberg: None. R. Loos: None. T. Matise: None. A.K. Mottl: None. K.E. North: None. C.L. Avery: Research Support; Self; Amgen. Funding American Diabetes Association (1-19-PDF-045 to H.M.M.); National Institutes of Health (T32HL007055, T32HL129982, R01HL142825)

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics

BackgroundQuantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.ResultsWe used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.ConclusionThis work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Configuration Optimization and Channel Estimation in Hybrid Beamforming mmWave Systems With Channel Support Side Information

Hybrid beamforming (HBF) is a promising technique in millimeter-wave (mmWave) systems with a limited number of radio frequency (RF) chains. Acquiring channel state information is essential for HBF design. Various compressive-sensing-based channel estimation (CE) schemes that exploit the sparse nature of mmWave channels have been proposed. The design of an analog beamforming matrix plays a key role in providing high CE quality with low pilot overhead. In this paper, a novel and flexible HBF architecture called adaptive sum product network (ASPN) is proposed, which can realize more flexible analog beamforming with less hardwares. Based on the ASPN architecture, a novel variational-Bayesian-inference-based compressive CE algorithm is proposed, which can fully exploit the channel support side information (CSSI) at the base station under a general uncertain measurement matrix induced by the off-grid parameters for power leakage elimination. To optimally configure the degrees of freedom in the ASPN according to the CSSI, a dynamic configuration optimization algorithm is proposed by formulating the CE performance metric in terms of the configuration parameters of ASPN. The importance and effectiveness of the flexibility introduced in ASPN HBF architecture, the compressive CE algorithm as well as the configuration algorithm are illustrated by the superb performance of the proposed scheme compared to the representative state-of-the-art techniques through extensive simulation results.

A powerful and efficient multivariate approach for voxel-level connectome-wide association studies

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR.

Combined Constrained Adaptive Sum and Difference Beamforming in Monopulse Angle Estimation

Motivated by beam distortions in multiple interferences environment, we propose a novel combined constrained method for adaptive sum and difference beamforming in monopulse angle estimation. By optimizing the adaptive weights of sum and difference beams jointly, the restriction on the degree of freedom to determine the adaptive sum or difference weight is evenly distributed to all weights. The constraints of array gain and monopulse ratio curve are utilized to avoid beam distortion while nulling the interferences. Simulations of the proposed algorithm have been provided, which show a significant improvement in beamforming and angle estimation performance compared with the conventional constrained adaptive monopulse algorithm.

Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users’ quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.

Abstract MP61: A Multi-pollutant, Multi-ethnic, and Methylome-wide Association Study Highlights Epigenetic Effects of Exposure to Ambient Air Pollution Mixtures

Background: Ambient air pollution mixtures including fine particulates and gases have been associated with cardiovascular disease (CVD). Although mechanisms underlying the associations remain largely uncharacterized to date, examining epigenetic responses to air pollution mixtures may identify biological systems affected by exposure and provide insight into the pathophysiology and environmental epidemiology of CVD. Therefore, we undertook a multi-pollutant, methylome-wide study of associations between DNA methylation (DNAm) at 485,000 Cytosine-phosphate-Guanine (CpG) sites and pollutants regulated by the U.S. Environmental Protection Agency under the Clean Air Act. Methods: Data from the Atherosclerosis Risk in Communities Study and three sub-studies within the Women’s Health Initiative (WHI) included information on 8,567 participants, 83% of whom were women, 46% African American, and 9% Hispanic/Latino (mean age, 61.3 years). Daily mean, geocoded participant address-specific concentrations of ambient particulate matter with diameter ≤ 10μm (PM 10 ), ≤2.5μm (PM 2.5 ), and 2.5-10μm (PM 2.5-10 ); ozone (O 3 ); nitrogen dioxide (NO 2 ); nitrogen oxides (NO x ); carbon monoxide (CO); and sulfur dioxide (SO 2 ) were averaged over 2, 7, 28, and 365 days before measurement of leukocyte DNAm in whole blood. Methylome-wide, pollutant-DNAm associations were estimated separately at each averaging period in each stratum defined by race/ethnicity and study using multi-level, linear mixed models adjusting for socio-demographic, behavioral, and meteorological characteristics; estimated leukocyte proportions; and technical covariates. Estimates were combined across strata in pollutant- and period-specific, fixed-effects, inverse-variance weighted meta-analyses. Then multi-pollutant, period-specific effects were examined using the adaptive sum of powered scores (U) test (aSPU). CpG sites associated with multi-pollutant significance (aSPU P &lt; 1.0 x 10 -7 ) were characterized in silico to assess their putative function and biological plausibility. Results: Overall analyses identified a significant, 28-day mean, multi-pollutant association at cg15008743 near ZNF83 on chromosome 19 (p = 1x10 -7 ) and a highly-suggestive 28-day mean, multipollutant association at cg00489219 near ZNF621 on chromosome 3 (p = 5x10 -7 ). ZNF83 and ZNF621 encode zinc finger proteins with a potentially broad reach across biological systems given their transcription factor, metal, and DNA binding activities. ZNF621 also harbors rare genetic variants with large effects on blood pressure. Discussion: Methylome-wide associations of DNAm with EPA-regulated ambient air pollutants suggest biologically plausible pathways underpinning the increasingly well-documented ties between multi-pollutant mixtures and cardiovascular disease.

Comparison of common burden tests for genetic association studies of rare variants.

Common burden tests have different statistical performance in genetic association studies of rare variants. Here, we compare the statistical performance of burden tests, such as CMC, WST, SUM and extension methods, using the computer-simulated datasets of rare variants with different parameters of sample sizes, linkage disequilibrium (LD), and different numbers of mixed non-associated variants. The simulation results showed that the type I error for all methods is near 0.05. When the rare variants had the same direction of effect, the higher LD and the less non-associated variants, the higher the power of these method, except the data adaptive SUM test. When the direction was different, the power was significantly reduced for all methods. The methods that consider the direction yielded larger statistical power than those methods without considering the effect direction, except the strong LD condition. And the larger the sample size, the larger the power. The statistical performance of burden tests is affected by a variety of factors, including the sample size, effect direction of variants, non-associated variants, and LD. Therefore, when choosing the method and setting the collection unit and weight, the prior biological information of genetic variation should be integrated to improve study efficiency.

Detecting changes in a multiparameter exponential family by using adaptive CUSUM procedure

ABSTRACTAn adaptive cumulative sum (CUSUM) procedure is proposed to monitor parameter changes in a multiparameter exponential family where the change-point and postchange parameters are estimated adaptively. Approximations for average run lengths are derived. Monitoring changes in both mean and variance in the normal case is considered as an illustration. The conditional biases of the estimations for the change-point and postchange mean and variance is studied by simulation comparison with several other CUSUM procedures. An adaptive dam process by modifying the adaptive CUSUM process is used to detect and identify change points and change segments by using Citibank stock prices from 30 Dow Jones Industry Index.

DFT-Based Method for More Accurate Adsorption Energies: An Adaptive Sum of Energies from RPBE and vdW Density Functionals

In recent years, the popularity of density functional theory with periodic boundary conditions (DFT) has surged for the design and optimization of functional materials. However, no single DFT exchange–correlation functional currently available gives accurate adsorption energies on transition metals both when bonding to the surface is dominated by strong covalent or ionic bonding and when it has strong contributions from van der Waals interactions (i.e., dispersion forces). Here we present a new, simple method for accurately predicting adsorption energies on transition-metal surfaces based on DFT calculations, using an adaptively weighted sum of energies from RPBE and optB86b-vdW (or optB88-vdW) density functionals. This method has been benchmarked against a set of 39 reliable experimental energies for adsorption reactions. Our results show that this method has a mean absolute error and root mean squared error relative to experiments of 13.4 and 19.3 kJ/mol, respectively, compared to 20.4 and 26.4 kJ/mol f...