cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7037684.v1
title: Testing Second-Order Dynamics for Autoregressive Processes in Presence of Time-Varying Variance

description: This article considers the volatility modeling for autoregressive univariate time series. A benchmark approach is the stationary autoregressive conditional heteroscedasticity (ARCH) model of Engle. Motivated by real data evidence, processes with nonconstant unconditional variance and ARCH effects have been recently introduced. We take into account this type of nonstationarity in variance and propose simple testing procedures for ARCH effects. Adaptive McLeod and Li’s portmanteau and ARCH-LM tests for checking the presence of such second-order dynamics are provided. The standard versions of these tests, commonly used by practitioners, suppose constant unconditional variance. The failure of these standard tests with time-varying unconditional variance is highlighted. The theoretical results are illustrated by means of simulated and real data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037681.v1
title: Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers

description: We propose a Bayesian generalized low-rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037672
title: Targeted Local Support Vector Machine for Age-Dependent Classification

description: We develop methods to accurately predict whether presymptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example, age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine (SVM) to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing SVM and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest Huntington’s disease (HD) subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037669
title: Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance

description: In disease surveillance applications, the disease events are modeled by spatiotemporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatiotemporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean and covariance functions of the latent process. By performing functional principal component analysis to the latent process, we can better understand the correlation structure in the point process. We also propose an empirical Bayes method to predict the latent spatial random effects, which can help highlight hot areas with unusually high event rates. Under an increasing domain and increasing knots asymptotic framework, we establish the asymptotic distribution for the parametric components in the model and the asymptotic convergence rates for the functional principal component estimators. We illustrate the methodology through a simulation study and an application to the Connecticut Tumor Registry data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037672.v1
title: Targeted Local Support Vector Machine for Age-Dependent Classification

description: We develop methods to accurately predict whether presymptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example, age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine (SVM) to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing SVM and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest Huntington’s disease (HD) subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037669.v1
title: Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance

description: In disease surveillance applications, the disease events are modeled by spatiotemporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatiotemporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean and covariance functions of the latent process. By performing functional principal component analysis to the latent process, we can better understand the correlation structure in the point process. We also propose an empirical Bayes method to predict the latent spatial random effects, which can help highlight hot areas with unusually high event rates. Under an increasing domain and increasing knots asymptotic framework, we establish the asymptotic distribution for the parametric components in the model and the asymptotic convergence rates for the functional principal component estimators. We illustrate the methodology through a simulation study and an application to the Connecticut Tumor Registry data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037654
title: A refinement of the Band spreadsheet for wind turbine collision risk allowing for oblique entry

description: A new spreadsheet is presented, to be used as part of the Band model for estimating potential avian mortality due to wind turbine strike. The spreadsheet extends the Band collision risk spreadsheet by allowing for oblique approach angles and wind speed. The differences in the results between this new spreadsheet and the standard Band spreadsheet are given for two species, the white-tailed eagle <i>Haliaeetus albicilla</i> and the South Island pied oystercatcher <i>Haematopus finschi</i>, chosen for their contrasting sizes and flight characteristics. Under more representative conditions, the true risk for large birds is shown to be substantially greater than that calculated by the Band spreadsheet. Examples of how to use the new spreadsheet with bird survey and wind data are given.
DOI: 10.6084/m9.figshare.7037666
title: Interaction Screening for Ultrahigh-Dimensional Data

description: In ultrahigh-dimensional data analysis, it is extremely challenging to identify important interaction effects, and a top concern in practice is computational feasibility. For a dataset with <i>n</i> observations and <i>p</i> predictors, the augmented design matrix including all linear and order-2 terms is of size <i>n</i> × (<i>p</i><sup>2</sup> + 3<i>p</i>)/2. When <i>p</i> is large, say more than tens of hundreds, the number of interactions is enormous and beyond the capacity of standard machines and software tools for storage and analysis. In theory, the interaction-selection consistency is hard to achieve in high-dimensional settings. Interaction effects have heavier tails and more complex covariance structures than main effects in a random design, making theoretical analysis difficult. In this article, we propose to tackle these issues by forward-selection-based procedures called iFOR, which identify interaction effects in a greedy forward fashion while maintaining the natural hierarchical model structure. Two algorithms, iFORT and iFORM, are studied. Computationally, the iFOR procedures are designed to be simple and fast to implement. No complex optimization tools are needed, since only OLS-type calculations are involved; the iFOR algorithms avoid storing and manipulating the whole augmented matrix, so the memory and CPU requirement is minimal; the computational complexity is <i>linear</i> in <i>p</i> for sparse models, hence feasible for <i>p</i> ≫ <i>n</i>. Theoretically, we prove that they possess sure screening property for ultrahigh-dimensional settings. Numerical examples are used to demonstrate their finite sample performance. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037666.v1
title: Interaction Screening for Ultrahigh-Dimensional Data

description: In ultrahigh-dimensional data analysis, it is extremely challenging to identify important interaction effects, and a top concern in practice is computational feasibility. For a dataset with <i>n</i> observations and <i>p</i> predictors, the augmented design matrix including all linear and order-2 terms is of size <i>n</i> × (<i>p</i><sup>2</sup> + 3<i>p</i>)/2. When <i>p</i> is large, say more than tens of hundreds, the number of interactions is enormous and beyond the capacity of standard machines and software tools for storage and analysis. In theory, the interaction-selection consistency is hard to achieve in high-dimensional settings. Interaction effects have heavier tails and more complex covariance structures than main effects in a random design, making theoretical analysis difficult. In this article, we propose to tackle these issues by forward-selection-based procedures called iFOR, which identify interaction effects in a greedy forward fashion while maintaining the natural hierarchical model structure. Two algorithms, iFORT and iFORM, are studied. Computationally, the iFOR procedures are designed to be simple and fast to implement. No complex optimization tools are needed, since only OLS-type calculations are involved; the iFOR algorithms avoid storing and manipulating the whole augmented matrix, so the memory and CPU requirement is minimal; the computational complexity is <i>linear</i> in <i>p</i> for sparse models, hence feasible for <i>p</i> ≫ <i>n</i>. Theoretically, we prove that they possess sure screening property for ultrahigh-dimensional settings. Numerical examples are used to demonstrate their finite sample performance. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7037654.v1
title: A refinement of the Band spreadsheet for wind turbine collision risk allowing for oblique entry

description: A new spreadsheet is presented, to be used as part of the Band model for estimating potential avian mortality due to wind turbine strike. The spreadsheet extends the Band collision risk spreadsheet by allowing for oblique approach angles and wind speed. The differences in the results between this new spreadsheet and the standard Band spreadsheet are given for two species, the white-tailed eagle <i>Haliaeetus albicilla</i> and the South Island pied oystercatcher <i>Haematopus finschi</i>, chosen for their contrasting sizes and flight characteristics. Under more representative conditions, the true risk for large birds is shown to be substantially greater than that calculated by the Band spreadsheet. Examples of how to use the new spreadsheet with bird survey and wind data are given.
DOI: 10.6084/m9.figshare.7037609
title: Data.csv

description: Seismic hazards assessment based on the database of the NGA project. The data includes the effects of moment magnitude (M ), closest distance to the coseismic rupture plane in kilometers (Rrup ), reverse and reverse-oblique faulting indicator based on the measurements of the average angle of the slip in the plane of rupture between the strikedirection and the slip vector (FRV ), normal and normal-oblique faulting indicator (FN M ), depth to the top of the coseismic rupture plane in kilometers (ZT OR), largerupture dips (δ), the time-averaged shear-wave velocity in the top 30 meters of the siteprofile in meters per seconds (VS30 ), and periods in seconds (T ).
DOI: 10.6084/m9.figshare.7037612
title: Third week training materials

description: E. melliodora GBS data for GBS analysis tutorial<br>
DOI: 10.6084/m9.figshare.7037612.v1
title: Third week training materials

description: E. melliodora GBS data for GBS analysis tutorial<br>
DOI: 10.6084/m9.figshare.7037606
title: all_data_083118.xlsx

description: Data from PLOS ONE manuscript available for download.
DOI: 10.6084/m9.figshare.7037606.v1
title: all_data_083118.xlsx

description: Data from PLOS ONE manuscript available for download.
DOI: 10.6084/m9.figshare.7037567
title: Host species identity in annual Brassicaceae has a limited effect on the assembly of root-endophytic fungal communities

description: Establishing the contribution of host plant genotype to the assembly of root-associated microbial communities can be challenging, since multiple ecological factors may confound the effects purely due to the host, and plant species often do not occur sympatrically. We aimed to measure the relative importance of host plant species on driving the assembly of natural root-endophytic fungal communities, as compared to other habitat conditions that depend on the local environment. Species of annual wild Brassicaceae and the cultivated <i>Brassica napus</i> were collected in two separate locations, where they grew in close proximity. <i>Arrhentherum elatius</i> (Poaceae) was also collected in both sites and used as outgroup. The root-endophytic fungal communities of these plants were characterised using Illumina MiSeq amplicon sequencing and cultivation, and compared between locations and hosts. Plant family and sampling location were the factors driving most variation in fungal communities, affecting both community composition and the proportion of cultivable fungi. Whether Brassicaceae were wild or domesticated had little effect on endophytic communities, although its contribution increased when cultivable fungi were considered. No strong specificity of endophytes towards hosts was found, because a core mycobiome was shared by different species. Plant species is a limited predictor of the structure and composition of fungal endophytic communities in roots of annual Brassicaceae, as compared to geographic location. However, the host effect on endophytic assemblages increases with phylogenetic distance between hosts.
DOI: 10.6084/m9.figshare.7037567.v2
title: Host species identity in annual Brassicaceae has a limited effect on the assembly of root-endophytic fungal communities

description: Establishing the contribution of host plant genotype to the assembly of root-associated microbial communities can be challenging, since multiple ecological factors may confound the effects purely due to the host, and plant species often do not occur sympatrically. We aimed to measure the relative importance of host plant species on driving the assembly of natural root-endophytic fungal communities, as compared to other habitat conditions that depend on the local environment. Species of annual wild Brassicaceae and the cultivated <i>Brassica napus</i> were collected in two separate locations, where they grew in close proximity. <i>Arrhentherum elatius</i> (Poaceae) was also collected in both sites and used as outgroup. The root-endophytic fungal communities of these plants were characterised using Illumina MiSeq amplicon sequencing and cultivation, and compared between locations and hosts. Plant family and sampling location were the factors driving most variation in fungal communities, affecting both community composition and the proportion of cultivable fungi. Whether Brassicaceae were wild or domesticated had little effect on endophytic communities, although its contribution increased when cultivable fungi were considered. No strong specificity of endophytes towards hosts was found, because a core mycobiome was shared by different species. Plant species is a limited predictor of the structure and composition of fungal endophytic communities in roots of annual Brassicaceae, as compared to geographic location. However, the host effect on endophytic assemblages increases with phylogenetic distance between hosts.
DOI: 10.6084/m9.figshare.7037558
title: Local recurrence rate in a national Danish patient cohort after curative treatment for rectal cancer

description: <b>Aim:</b> Several trials have shown that preoperative (chemo)radiotherapy (CRT) reduces local recurrence rates (LRRs) in rectal cancer (RC). The use of CRT varies greatly between countries. It is unknown whether the restrictive use of CRT in Denmark results in a higher LRR relative to other countries. The aim was to evaluate the LRR in a national Danish consecutive cohort of patients with RC. <b>Methods:</b> All data from patients with RC in Denmark in 2009–2010 who were operated on with curative intent were retrieved from the Danish Colorectal Cancer Group database. Patients with metastases at the time of diagnosis, patients with synchronous colon cancer, and patients, in whom only local surgical procedures were performed, were excluded. In total, 1633 patients met the inclusion criteria. Clinical follow-up was at least five years with a cut-off date of 31 December 2015. <b>Results:</b> Clinical follow-up was 5.4 years (median) with an interquartile range of 4.5–6.1 years. Of all included patients, 479 (29%) were treated with preoperative long-course CRT. Local recurrence was found in 68 patients, resulting in an LRR of 4.2%, and 182 (11%) patients developed distant metastases. Five-year overall survival was 74% (95% CI: 71.64–75.91). <b>Conclusions:</b> Five-year follow-up of curatively treated patients with RC in Denmark revealed a low LRR. This figure is identical to those reported in other Nordic countries, despite Denmark’s considerably stricter guidelines for CRT. The obtained results justify the currently adopted restrictive use of preoperative CRT in Denmark.
DOI: 10.6084/m9.figshare.7037531
title: PrISME.Data.xlsx

description: Greenhouse gas fluxes and supporting data
DOI: 10.6084/m9.figshare.7037528
title: New monoclonal antibodies specific for mammalian protamines P1 and P2

description: The expression of protamines and the binding of these small arginine-rich proteins to DNA complete the process of spermatid chromatin reorganization and the global inactivation of the male’s haploid genome that occurs during the final stages of sperm development in mammals. While a number of anti-protamine antibodies have been created during the last 40 years, only a few have proven useful for detecting the presence of the protamines, determining the timing of their expression and deposition in chromatin, and investigating their structure and function in both maturing spermatids and sperm. The aim of this effort was to develop an additional set of monoclonal antibodies (MAbs) that not only recognize new P1 and P2 protamine epitopes but also work well as IHC reagents for detecting and identifying mammalian protamines in testicular tissue and ejaculated sperm. Using a combination of native and synthetic human protamines as antigens, 38 hybridoma clones recognizing human protamine P1 or P2 were generated. Antibodies produced by the 12 best clones were screened for selectivity by enzyme-linked immunosorbent assay, and two were found to recognize only human protamine P1 or P2, while a number of the others bound to both the human and mouse proteins. One MAb recognized every protamine tested. All the antibodies, including one recognizing stallion P1 and another recognizing stallion P2, bound to the native protamines in the chromatin of spermatids or sperm. While the majority labeled only elongating spermatids or sperm, several of the antibodies were found to also bind to the cytoplasm or nuclei of cells that lack protamine, which indicates these MAbs must recognize epitopes present in the protamines that are also found in other proteins. Thirteen overlapping human protamine P1 peptides were synthesized and subsequently used to identify the epitopes recognized by the six best antibodies. <b>Abbreviations</b>: BSA: bovine serum albumin; ELISA: enzyme-linked immunosorbent assay; HCl: hydrochloric acid; IHC: immunohistochemistry; i.p: intraperitoneal; LIS: lithium diiodosalicylate; MAb: monoclonal antibody; PBS: phosphate buffered saline