cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13637944.v1
title: False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data

description: There are a number of ways to test for the absence/presence of a spatial signal in a completely observed fine-resolution image. One of these is a powerful nonparametric procedure called enhanced false discovery rate (EFDR). A drawback of EFDR is that it requires the data to be defined on regular pixels in a rectangular spatial domain. Here, we develop an EFDR procedure for possibly incomplete data defined on irregular small areas. Motivated by statistical learning, we use conditional simulation (CS) to condition on the available data and simulate the full rectangular image at its finest resolution many times (<i>M</i>, say). EFDR is then applied to each of these simulations resulting in <i>M</i> estimates of the signal and <i>M</i> statistically dependent <i>p</i>-values. Averaging over these estimates yields a single, combined estimate of a possible signal, but inference is needed to determine whether there really is a signal present. We test the original null hypothesis of no signal by combining the M p-values into a single <i>p</i>-value using copulas and a composite likelihood. If the null hypothesis of no signal is rejected, we use the combined estimate. We call this new procedure EFDR-CS and, to demonstrate its effectiveness, we show results from a simulation study; an experiment where we introduce aggregation and incompleteness into temperature-change data in the Asia-Pacific; and an application to total-column carbon dioxide from satellite remote sensing data over a region of the Middle East, Afghanistan, and the western part of Pakistan. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12903889
title: Quantum Annealing via Path-Integral Monte Carlo With Data Augmentation

description: This article considers quantum annealing in the Ising framework for solving combinatorial optimization problems. The path-integral Monte Carlo simulation approach is often used to approximate quantum annealing and implement the approximation by classical computers, which refers to simulated quantum annealing (SQA). In this article, we introduce a data augmentation scheme into SQA and develop a new algorithm for its implementation. The proposed algorithm reveals new insights on the sampling behaviors in SQA. Theoretical analyses are established to justify the algorithm, and numerical studies are conducted to check its performance and to confirm the theoretical findings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12903889.v2
title: Quantum Annealing via Path-Integral Monte Carlo With Data Augmentation

description: This article considers quantum annealing in the Ising framework for solving combinatorial optimization problems. The path-integral Monte Carlo simulation approach is often used to approximate quantum annealing and implement the approximation by classical computers, which refers to simulated quantum annealing (SQA). In this article, we introduce a data augmentation scheme into SQA and develop a new algorithm for its implementation. The proposed algorithm reveals new insights on the sampling behaviors in SQA. Theoretical analyses are established to justify the algorithm, and numerical studies are conducted to check its performance and to confirm the theoretical findings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12903889.v1
title: Quantum Annealing via Path-Integral Monte Carlo with Data Augmentation

description: This paper considers quantum annealing in the Ising framework for solving combinatorial optimization problems. The path-integral Monte Carlo simulation approach is often used to approximate quantum annealing and implement the approximation by classical computers, which refers to simulated quantum annealing. In this paper we introduce a data augmentation scheme into simulated quantum annealing and develop a new algorithm for its implementation. The proposed algorithm reveals new insights on the sampling behaviors in simulated quantum annealing. Theoretical analyses are established to justify the algorithm, and numerical studies are conducted to check its performance and to confirm the theoretical findings.
DOI: 10.6084/m9.figshare.13611276
title: Predictive Distribution Modeling Using Transformation Forests

description: Regression models for supervised learning problems with a continuous response are commonly understood as models for the conditional mean of the response given predictors. This notion is simple and therefore appealing for interpretation and visualization. Information about the whole underlying conditional distribution is, however, not available from these models. A more general understanding of regression models as models for conditional distributions allows much broader inference, for example, the computation of prediction intervals or probabilistic predictions for exceeding certain thresholds. Several random forest-type algorithms aim at estimating conditional distributions, most prominently quantile regression forests. We propose a novel approach based on a parametric family of distributions characterized by their transformation function. A dedicated novel “transformation tree” algorithm able to detect distributional changes is developed. Based on these transformation trees, we introduce “transformation forests” as an adaptive local likelihood estimator of conditional distribution functions. The resulting predictive distributions are fully parametric yet very general and allow inference procedures, such as likelihood-based variable importances, to be applied in a straightforward way. Supplemental files for this article are available online.
DOI: 10.6084/m9.figshare.13611276.v2
title: Predictive Distribution Modeling Using Transformation Forests

description: Regression models for supervised learning problems with a continuous response are commonly understood as models for the conditional mean of the response given predictors. This notion is simple and therefore appealing for interpretation and visualization. Information about the whole underlying conditional distribution is, however, not available from these models. A more general understanding of regression models as models for conditional distributions allows much broader inference, for example, the computation of prediction intervals or probabilistic predictions for exceeding certain thresholds. Several random forest-type algorithms aim at estimating conditional distributions, most prominently quantile regression forests. We propose a novel approach based on a parametric family of distributions characterized by their transformation function. A dedicated novel “transformation tree” algorithm able to detect distributional changes is developed. Based on these transformation trees, we introduce “transformation forests” as an adaptive local likelihood estimator of conditional distribution functions. The resulting predictive distributions are fully parametric yet very general and allow inference procedures, such as likelihood-based variable importances, to be applied in a straightforward way. Supplemental files for this article are available online.
DOI: 10.6084/m9.figshare.13611276.v1
title: Predictive Distribution Modelling Using Transformation Forests

description: Regression models for supervised learning problems with a continuous response are commonly understood as models for the conditional mean of the response given predictors. This notion is simple and therefore appealing for interpretation and visualisation. Information about the whole underlying conditional distribution is, however, not available from these models. A more general understanding of regression models as models for conditional distributions allows much broader inference, for example the computation of prediction intervals or probabilistic predictions for exceeding certain thresholds. Several random forest-type algorithms aim at estimating conditional distributions, most prominently quantile regression forests (Meinshausen, 2006, JMLR). We propose a novel approach based on a parametric family of distributions characterised by their transformation function. A dedicated novel “transformation tree” algorithm able to detect distributional changes is developed. Based on these transformation trees, we introduce “transformation forests” as an adaptive local likelihood estimator of conditional distribution functions. The resulting predictive distributions are fully parametric yet very general and allow inference procedures, such as likelihood-based variable importances, to be applied in a straightforward way.
DOI: 10.6084/m9.figshare.13577937.v1
title: Model selection with Lasso-Zero: adding straw to the haystack to better find needles

description: The high-dimensional linear model y=Xβ0+ϵ is considered and the focus is put on the problem of recovering the support <i>S</i> <sup>0</sup> of the sparse vector β0. We introduce a new ℓ1-based estimator, called <i>Lasso-Zero</i>, whose novelty resides in the repeated use of noise dictionaries concatenated to <i>X</i> for overfitting the response. Lasso-Zero is an extension of Thresholded Basis Pursuit, for which we prove sign consistency for correlated Gaussian designs. Both theoretical and empirical results motivate the use of noise dictionaries to improve Thresholded Basis Pursuit when the coefficients’ amplitude is low. To select the threshold of Lasso-Zero, we suggest to employ a pivotal version of the Quantile Universal Threshold (QUT) that exploits a byproduct of Lasso-Zero in order to avoid the need to estimate the noise level. Numerical simulations show that Lasso-Zero tuned by QUT performs well in terms of support recovery and provides an excellent trade-off between high power and few false discoveries compared to competitors.
DOI: 10.6084/m9.figshare.13519115.v1
title: Adaptive Bayesian Spectral Analysis of High-dimensional Nonstationary Time Series

description: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk towards zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo (SAMC) techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13143714.v1
title: Particle MCMC with Poisson Resampling: Parallelization and Continuous Time Models

description: We introduce a new version of particle filter in which the number of “children” of a particle at a given time has a Poisson distribution. As a result, the number of particles is random and varies with time. An advantage of this scheme is that descendants of different particles can evolve independently. It makes easy to parallelize computations. Moreover, particle filter with Poisson resampling is readily adapted to the case when a hidden process is a continuous time, piecewise deterministic semi-Markov process. We show that the basic techniques of particle MCMC, namely particle independent Metropolis-Hastings, particle Gibbs Sampler and its version with ancestor sampling, work under our Poisson resampling scheme. Our version of particle Gibbs Sampler is uniformly ergodic under the same assumptions as its standard counterpart. We present simulation results which indicate that our algorithms can compete with the existing methods.
DOI: 10.6084/m9.figshare.12974817
title: Assessing and Visualizing Simultaneous Simulation Error

description: Monte Carlo experiments produce samples to estimate features such as means and quantiles of a given distribution. However, simultaneous estimation of means and quantiles has received little attention. In this setting, we establish a multivariate central limit theorem for any finite combination of sample means and quantiles under the assumption of a strongly mixing process, which includes the standard Monte Carlo and Markov chain Monte Carlo settings. We build on this to provide a fast algorithm for constructing hyperrectangular confidence regions having the desired simultaneous coverage probability and a convenient marginal interpretation. The methods are incorporated into standard ways of visualizing the results of Monte Carlo experiments enabling the practitioner to more easily assess the reliability of the results. We demonstrate the utility of this approach in various Monte Carlo settings including simulation studies based on independent and identically distributed samples and Bayesian analyses using Markov chain Monte Carlo sampling. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12974817.v1
title: Assessing and Visualizing Simultaneous Simulation Error

description: Monte Carlo experiments produce samples to estimate features such as means and quantiles of a given distribution. However, simultaneous estimation of means and quantiles has received little attention. In this setting, we establish a multivariate central limit theorem for any finite combination of sample means and quantiles under the assumption of a strongly mixing process, which includes the standard Monte Carlo and Markov chain Monte Carlo settings. We build on this to provide a fast algorithm for constructing hyperrectangular confidence regions having the desired simultaneous coverage probability and a convenient marginal interpretation. The methods are incorporated into standard ways of visualizing the results of Monte Carlo experiments enabling the practitioner to more easily assess the reliability of the results. We demonstrate the utility of this approach in various Monte Carlo settings including simulation studies based on independent and identically distributed samples and Bayesian analyses using Markov chain Monte Carlo sampling. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.4784939
title: Supplementary information - Assembling Genomes With Complex Genomic Repeats

description: Update (v2.1.1-mtReads) - Added results from albacore 1.1.0 and guppy 5.0.7: gci_graph_2021-May-25.zip qualmean.png gci_violin.png Update (v2.0.0-mtReads) - Added mitochondrial sequence raw read files (as split and combined fast5 files), and summary statistics (gap-compressed identity, and mean estimated basecaller accuracy): Nbra_CFED_mtDNA_reads.tar gci_graph.zip These data complement the poster and lightning talk to be given by David Eccles at the London Calling conference in 2017: MIMR_EC3_ONTDECAF_t1.fasta.gz -- initial attempt at a genome assembly with Canu v1.4 (3x error corrected) NbL5_ONTDECAF_t1.contigs.flipped.gfa -- assembly graph associated with the corrected genome tangled_web_300.png -- background image of the poster (generated from the Canu GFA)
DOI: 10.5281/zenodo.798710
title: Supplementary information - Assembling Genomes With Complex Genomic Repeats

description: Update (v2.1.1-mtReads) - Added results from albacore 1.1.0 and guppy 5.0.7: gci_graph_2021-May-25.zip qualmean.png gci_violin.png Update (v2.0.0-mtReads) - Added mitochondrial sequence raw read files (as split and combined fast5 files), and summary statistics (gap-compressed identity, and mean estimated basecaller accuracy): Nbra_CFED_mtDNA_reads.tar gci_graph.zip These data complement the poster and lightning talk to be given by David Eccles at the London Calling conference in 2017: MIMR_EC3_ONTDECAF_t1.fasta.gz -- initial attempt at a genome assembly with Canu v1.4 (3x error corrected) NbL5_ONTDECAF_t1.contigs.flipped.gfa -- assembly graph associated with the corrected genome tangled_web_300.png -- background image of the poster (generated from the Canu GFA)
DOI: 10.6084/m9.figshare.12848877
title: Risk factors for severe opioid-induced respiratory depression in hospitalized adults: A case–control study

description: <b>Background</b>: Opioids are commonly prescribed to hospitalized adults to promote adequate pain relief, yet they can cause potentially fatal respiratory depression. <b>Aim</b>: The aim of this study was to examine the risk factors for the development of severe opioid-induced respiratory depression (OIRD) in hospitalized adults to ensure adequate monitoring of high-risk patients. <b>Methods</b>: A retrospective case–control study was conducted using data from the medical records of a university-affiliated hospital in Canada. Cases were eligible if they were adults (≥18 years old) and received opioid analgesia within 24 h of naloxone administration for respiratory depression. Controls had the same eligibility criteria, except for respiratory depression and naloxone administration. The case–control ratio was 1:1, and they were matched based on sex, type of unit, opioid molecule and the presence/absence of medication errors. <b>Results</b>: A total of 133 cases and 133 controls were included. Following cumulative risk factor analysis, renal failure (odds ratio [OR] = 2.176, 95% confidence interval [CI], 1.021–4.640, <i>P</i> = 0.044), the first 24 h of opioid administration (OR = 1.899, 95% CI, 1.090–3.309, <i>P</i> = 0.024), concomitant central nervous system (CNS) depressants (OR = 1.785, 95% CI, 1.023–3.113, <i>P</i> = 0.041), and increasing age (OR = 1.019, 95% CI, 1.002–1.035, <i>P</i> = 0.028) were positively associated with severe OIRD. <b>Conclusions</b>: Some adult hospitalized patients were at higher risk of experiencing severe OIRD, such as those with renal failure, those in their first 24 h of opioid administration, those receiving CNS depressants in addition to opioids, and those with an advanced age. These results will assist with the screening of patients at higher risk for severe OIRD, which is key to implementing appropriate monitoring and enhancing the safety of opioid use in hospital settings.
DOI: 10.6084/m9.figshare.12848877.v1
title: Risk factors for severe opioid-induced respiratory depression in hospitalized adults: A case–control study

description: <b>Background</b>: Opioids are commonly prescribed to hospitalized adults to promote adequate pain relief, yet they can cause potentially fatal respiratory depression. <b>Aim</b>: The aim of this study was to examine the risk factors for the development of severe opioid-induced respiratory depression (OIRD) in hospitalized adults to ensure adequate monitoring of high-risk patients. <b>Methods</b>: A retrospective case–control study was conducted using data from the medical records of a university-affiliated hospital in Canada. Cases were eligible if they were adults (≥18 years old) and received opioid analgesia within 24 h of naloxone administration for respiratory depression. Controls had the same eligibility criteria, except for respiratory depression and naloxone administration. The case–control ratio was 1:1, and they were matched based on sex, type of unit, opioid molecule and the presence/absence of medication errors. <b>Results</b>: A total of 133 cases and 133 controls were included. Following cumulative risk factor analysis, renal failure (odds ratio [OR] = 2.176, 95% confidence interval [CI], 1.021–4.640, <i>P</i> = 0.044), the first 24 h of opioid administration (OR = 1.899, 95% CI, 1.090–3.309, <i>P</i> = 0.024), concomitant central nervous system (CNS) depressants (OR = 1.785, 95% CI, 1.023–3.113, <i>P</i> = 0.041), and increasing age (OR = 1.019, 95% CI, 1.002–1.035, <i>P</i> = 0.028) were positively associated with severe OIRD. <b>Conclusions</b>: Some adult hospitalized patients were at higher risk of experiencing severe OIRD, such as those with renal failure, those in their first 24 h of opioid administration, those receiving CNS depressants in addition to opioids, and those with an advanced age. These results will assist with the screening of patients at higher risk for severe OIRD, which is key to implementing appropriate monitoring and enhancing the safety of opioid use in hospital settings.
DOI: 10.6084/m9.figshare.12958492
title: A scoping review to identify and map the multidimensional domains of pain in adults with advanced liver disease

description: <b>Background</b>: Pain is a significant problem in adults living with advanced liver disease, having limited guidance available for its clinical management. While pain is considered a multidimensional experience, there have been limited reviews of the pain literature in advanced liver disease conducted with a multidimensional framework. <b>Aims:</b> The goal of this scoping review was to identify and map the multidimensional domains of pain in adults with advanced liver disease using the biopsychosocial model. <b>Methods:</b> We used Arksey and O’Malley’s scoping framework. A search was conducted in MEDLINE, Embase, AMED, and CINAHL databases and the gray literature using specific eligibility criteria (1990–2019). Citation selection and data extraction were performed by two independent reviewers and in duplicate. <b>Results:</b> Of the 43 studies that met inclusion criteria, 51% were from North America and 93% utilized quantitative methods. The combined studies reported on 168,110 participants with ages ranging between 23 to 87 years. Only 9% reported an objective scoring system for liver disease severity. Few studies reported pain classification (9%) and intensity (16%). Pain prevalence ranged between 18% and 100%, with pain locations including joint, abdomen, back, head/neck, and upper/lower extremities. We identified and mapped 115 pain factors to the biopsychosocial model: physical (81%), psychological (65%), and sociocultural (5%). Only 9% measured pain using validated multidimensional tools. Pharmacological intervention (92%) prevailed among pain treatments. <b>Conclusions:</b> Pain is not well understood in patients with advanced liver disease, having limited multidimensional pain assessment and treatment approaches. There is a need to systematically examine the multidimensional nature of pain in this population.