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The random batch method provides an efficient algorithm for computing statistical properties of a canonical ensemble of interacting particles. In this work, we study the error estimates of the fully discrete random batch method, especially in terms of approximating the invariant distribution. The triangle inequality framework employed in this paper is a convenient approach to estimate the long-time sampling error of the numerical methods. Using the triangle inequality framework, we show that the long-time error of the discrete random batch method is |$O(\sqrt {\tau } + e^{-\lambda t})$|⁠ , where |$\tau $| is the time step and |$\lambda $| is the convergence rate, which does not depend on the time step |$\tau $| or the number of particles |$N$|⁠. Our results also apply to the McKean–Vlasov process, which is the mean-field limit of the interacting particle system as the number of particles |$N\rightarrow \infty $|⁠. [ABSTRACT FROM AUTHOR]

When starting an N -body simulation of an isolated galaxy, it is desirable to select particles from a distribution function to ensure that the model is in equilibrium. Random sampling from a DF is widely used, but results in a set of particles that differs by shot noise from that intended. This paper presents a method to reduce sampling noise that has been developed by the author in a many collaborations over a number of years. The technique has been partly described in past papers, though the ideas have not previously been gathered together, nor have its advantages been clearly demonstrated in past work. Of course, sampling errors can also be reduced by a brute force increase in the number of particles, but methods to achieve the same effect with fewer particles have obvious advantages. Here we not only describe the method, but also present three sets of simulations to illustrate the practical advantages of reducing sampling error. The improvements are not dramatic, but are clearly worth having. [ABSTRACT FROM AUTHOR]

Populations suffer two types of stochasticity: demographic stochasticity, from sampling error in offspring number, and environmental stochasticity, from temporal variation in the growth rate. By modelling evolution through phenotypic selection following an abrupt environmental change, we investigate how genetic and demographic dynamics, as well as effects on population survival of the genetic variance and of the strength of stabilizing selection, differ under the two types of stochasticity. We show that population survival probability declines sharply with stronger stabilizing selection under demographic stochasticity, but declines more continuously when environmental stochasticity is strengthened. However, the genetic variance that confers the highest population survival probability differs little under demographic and environmental stochasticity. Since the influence of demographic stochasticity is stronger when population size is smaller, a slow initial decline of genetic variance, which allows quicker evolution, is important for population persistence. In contrast, the influence of environmental stochasticity is population‐size‐independent, so higher initial fitness becomes important for survival under strong environmental stochasticity. The two types of stochasticity interact in a more than multiplicative way in reducing the population survival probability. Our work suggests the importance of explicitly distinguishing and measuring the forms of stochasticity during evolutionary rescue. [ABSTRACT FROM AUTHOR]

Motivation Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. Results We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. Availability and implementation All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire. [ABSTRACT FROM AUTHOR]

We present a generalized estimator for the autocorrelation function, S-ACF, which is an extended version of the standard estimator of the autocorrelation function (ACF). S-ACF is a versatile definition that can robustly and efficiently extract periodicity and signal shape information from a time series, independent of the time sampling and with minimal assumptions about the underlying process. Calculating the autocorrelation of irregularly sampled time series becomes possible by generalizing the lag of the standard estimator of the ACF to a real parameter and introducing the notion of selection and weight functions. We show that the S-ACF reduces to the standard ACF estimator for regularly sampled time series. Using a large number of synthetic time series, we demonstrate that the performance of the S-ACF is as good or better than commonly used Gaussian and rectangular kernel estimators, and is comparable to a combination of interpolation and the standard estimator. We apply the S-ACF to astrophysical data by extracting rotation periods for the spotted star KIC 5110407, and compare our results to Gaussian process (GP) regression and Lomb–Scargle (LS) periodograms. We find that the S-ACF periods typically agree better with those from GP regression than from LS periodograms, especially in cases where there is evolution in the signal shape. The S-ACF has a wide range of potential applications and should be useful in quantitative science disciplines where irregularly sampled time series occur. A python implementation of the S-ACF is available under the MIT license. [ABSTRACT FROM AUTHOR]

Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling's expression using information theory match the leading terms in Keeton's expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. While intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound. [ABSTRACT FROM AUTHOR]

In sample surveys that collect information on skewed variables, it is often desirable to assess the influence of sample units on the sampling error of survey-weighted estimators of finite population parameters. The conditional bias is an attractive measure of influence that accounts for the sampling design and the estimation method. It is defined as the design expectation of the sampling error conditional on a given unit being selected in the sample. The estimation of the conditional bias is relatively straightforward for simple sampling designs and estimators. However, for complex designs or complex estimators, it may be tedious to derive an explicit expression for the conditional bias. In those complex surveys, variance estimation is often achieved through replication methods such as the bootstrap. Bootstrap methods of variance estimation are typically implemented by producing a set of bootstrap weights that is provided to users along with the survey data. In this article, we show how to use these bootstrap weights to obtain an estimator of the conditional bias. Our bootstrap estimator is evaluated in a simulation study and illustrated using data from the Canadian Survey of Household Spending. [ABSTRACT FROM AUTHOR]

Background: Laboratory sampling is a significant source of error in feed testing. Proficiency testing programs such as the Association of American Feed Control Officials Proficiency Testing Program are an effectivemeans of assessing error in and among analyticalmethods. However, all proficiency test items are comminuted and blended to control variability among items, effectivelyminimizing sampling error. Currently there is nomechanismformonitoring sampling error among laboratories. Objective: The objective of this work was to investigate the feasibility of a proficiency testing program for laboratory sampling methods and provide insight into a program to advance the performance of sampling in laboratories. Methods: The study involved the fabrication of identical feed test items from feed ingredients and shipping the uncomminuted materials to volunteer laboratories. The volunteer laboratories followed in-house procedures for selecting test portions for routine feed tests. Tests on all the test portions for a single analyte were performed by a single laboratory, so that the variability in test results could be attributed to laboratory sampling processes to select test portions. Results: The average RSD, %, for Item A and Item B, respectively, were as follows: protein, 5.08 and 5.23; non-protein nitrogen, 8.90 and 16.6; crude fat, 3.45 and 5.67; vitamin A, 33.9 and 26.9; calcium, 21.9 and 23.6; zinc, 17.9 and 27.9; and copper, 17.4 and 27.9. Conclusion: This study suggests that a proficiency testing program for laboratory sampling is feasible with manual manufacture of the test items, and data can be used to monitor laboratory sampling proficiency and also to compare the performance of different laboratory sampling methods. Highlights: The data illustrates that each analyte has unique distributional and compositional heterogeneity, thus unique sampling error, even when multiple analytes are determined from a single test portion. [ABSTRACT FROM AUTHOR]

Failure of individuals to voluntarily observe regulations and recommendations around mitigating COVID-19 (e.g. social distancing; frequent handwashing) is often cited as a reason why some countries struggled to curtail the spread of the virus. Understanding the factors that are associated with people's willingness to comply with COVID-19 regulations and recommendations is an important step in helping policy makers and health officials reduce the impact of this (and future) pandemics. In the current study we examined this question in one of the countries hardest hit by the pandemic: Spain. A large, representative survey (N = 2100) revealed that several factors were positively associated with willingness to comply to COVID-19 regulations and recommendations. In decreasing order of predictive value, these were: (i) perceptions of whether friends and family were complying (i.e. norms), (ii) trust in science as a basis for lawmaking, (iii) perceived effectiveness of regulations and (iv) perception of risk of infection. These results point to the importance of influencing social norms as the primary way to improve adherence to the health regulations of COVID-19; more important than intrapsychic considerations such as efficacy and risk. [ABSTRACT FROM AUTHOR]

In the search for life in the Universe, exoplanets represent numerous natural experiments in planet formation, evolution, and the emergence of life. This raises the fascinating prospect of evaluating cosmic life on a statistical basis. One key statistic is the occurrence rate of life-bearing worlds, f L, the 'frequency of life' term in the famous Drake Equation. Measuring f L would give profound insight into how common life is and may help us to constrain origin-of-life theories. I propose f L as the goal for the DRAKE mission (Dedicated Research for Advancing Knowledge of Exobiology): a transit spectroscopy survey of M-dwarf habitable zone terrestrial planets. I investigate how the uncertainty on the observed value of f L scales with sample size. I determine that sampling error dominates over observational error and that the uncertainty is a function of the observed f L value. I show that even small sample sizes can provide significant constraints on f L, boding well for the transit spectroscopy approach. I perform a feasibility study of the DRAKE mission using a nominal instrument design and mission plan. Due to low observing efficiencies, DRAKE may need to be incorporated into a wider-ranging deep-space or lunar observatory. A 50-planet survey could constrain f L to ≤ 0.06 (at 95 per cent confidence) if the sample f L  = 0, or 0.03–0.2 if the sample f L  = 0.1. This can be achieved (on average) in 10 yr using a 17-m telescope with an unrestricted field-of-regard. DRAKE is a viable approach to attempting the first experimental measurement of f L. [ABSTRACT FROM AUTHOR]

Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, research on properly quantifying the uncertainty of the (noisy) sample estimate regarding the true value in the population, is currently still limited. This article proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. Instead of computing a differentially private point estimate and deriving its uncertainty, we directly estimate the interval bounds and discuss why this approach is superior if ensuring privacy is important. We also illustrate that addressing both sources of uncertainty—the error from sampling and the error from protecting the output—simultaneously should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion. We evaluate the performance of the different algorithms under various parameter settings in extensive simulation studies and demonstrate how the findings could be applied in practical settings using data from the 1940 Decennial Census. [ABSTRACT FROM AUTHOR]

Secondary contact between species often results in the formation of a hybrid zone, with the eventual fates of the hybridizing species dependent on evolutionary and ecological forces. We examine this process in the Amazon Basin by conducting the first genomic and phenotypic characterization of the hybrid zone formed after secondary contact between two obligate army‐ant‐followers: the White‐breasted Antbird (Rhegmatorhina hoffmannsi) and the Harlequin Antbird (Rhegmatorhina berlepschi). We found a major geographic displacement (∼120 km) between the mitochondrial and nuclear clines, and we explore potential hypotheses for the displacement, including sampling error, genetic drift, and asymmetric cytonuclear incompatibilities. We cannot exclude roles for sampling error and genetic drift in contributing to the discordance; however, the data suggest expansion and unidirectional introgression of hoffmannsi into the distribution of berlepschi. [ABSTRACT FROM AUTHOR]

The adoption of a multivariate perspective of selection implies the existence of multivariate adaptive peaks and pervasive correlational selection that promotes co‐adaptation between traits. However, to test for the ubiquity of correlational selection in nature, we must first have a sense of how well can we estimate multivariate nonlinear selection (i.e., the γ‐matrix) in the face of sampling error. To explore the sampling properties of estimated γ‐matrices, we simulated inidividual traits and fitness under a wide range of sample sizes, using different strengths of correlational selection and of stabilizing selection, combined with different number of traits under selection, different amounts of residual variance in fitness, and distinct patterns of selection. We then ran nonlinear regressions with these simulated datasets to simulate γ‐matrices after adding random error to individual fitness. To test how well could we detect the imposed pattern of correlational selection at different sample sizes, we measured the similarity between simulated and imposed γ‐matrices. We show that detection of the pattern of correlational selection is highly dependent on the total strength of selection on traits and on the amount of residual variance in fitness. Minimum sample size needs to be at least 500 to precisely estimate the pattern of correlational selection. Furthermore, a pattern of selection in which different sets of traits contribute to different functions is the easiest to diagnose, even when using a large number of traits (10 traits), but with sample sizes in the order of 1000 individuals. Consequently, we recommend working with sets of traits from distinct functional complexes and fitness proxies less prone to effects of environmental and demographic stochasticity to test for correlational selection with lower sample sizes. [ABSTRACT FROM AUTHOR]

Background: Vitamin A test results have historically been notorious for poor repeatability and reproducibility. This problem has been discussed at length in Association of American Feed Control Officials Laboratory Methods and Services Committee meetings. Objective: The objective of this work was to assess the effect of test portion mass on the repeatability of vitamin A test results. Methods: The study was conducted in two parts. In Part I, fundamental sampling error (FSE) was determined experimentally through replicated (n = 16) vitamin A testing of three animal feed materials. The testing followed rigorous test portion selection for 10 g and 100 g test portions. In Part II, FSE calculations were made (1) using theoretical equations based on vitamin A as a liberated analyte and (2) on representing the particles in feed materials. Particle size characterization of vitamin A ingredients was estimated by microscopy and further evaluated by particle size analysis. Results: RSDs, % for vitamin A determinations ranged from 10.5-24.7, and 2.26-10.7 for 10 g and 100 g test portions, respectively. FSE calculated for Ingredient A ranged from 18.3-101% and 5.79-32.0% for 10 g and 100 g test portions, respectively, and for Ingredient B, ranged from 10.2-56.2% and 3.21-17.8% for 10 g and 100 g test portions, respectively. Conclusion: Test portion mass has a substantial impact on FSE and is an important factor in controlling the random error in vitamin A testing. FSE equations are useful to approximate minimum test portion mass. Highlights: Vitamin A method development should use theoretical predictions and experimental verification to guide test portion mass. Strategies to deal with the larger test portion masses will be key to validating new methods. [ABSTRACT FROM AUTHOR]

We introduce Bilby-MCMC , a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. Bilby-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate that learning proposals can produce over a 10-fold improvement in efficiency by reducing the autocorrelation time. Using a variety of standard and problem-specific tests, we validate the ability of the Bilby-MCMC sampler to produce independent posterior samples and estimate the Bayesian evidence. Compared to the widely used Dynesty nested sampling algorithm, Bilby-MCMC is less efficient in producing independent posterior samples and less accurate in its estimation of the evidence. However, we find that posterior samples drawn from the Bilby-MCMC sampler are more robust: never failing to pass our validation tests. Meanwhile, the Dynesty sampler fails the difficult-to-sample Rosenbrock likelihood test, over constraining the posterior. For CBC problems, this highlights the importance of cross-sampler comparisons to ensure results are robust to sampling error. Finally, Bilby-MCMC can be embarrassingly and asynchronously parallelized making it highly suitable for reducing the analysis wall-time using a High Throughput Computing environment. Bilby-MCMC may be a useful tool for the rapid and robust analysis of gravitational-wave signals during the advanced detector era and we expect it to have utility throughout astrophysics. [ABSTRACT FROM AUTHOR]

Nested sampling is an important tool for conducting Bayesian analysis in Astronomy and other fields, both for sampling complicated posterior distributions for parameter inference, and for computing marginal likelihoods for model comparison. One technical obstacle to using nested sampling in practice is the requirement (for most common implementations) that prior distributions be provided in the form of transformations from the unit hyper-cube to the target prior density. For many applications – particularly when using the posterior from one experiment as the prior for another – such a transformation is not readily available. In this letter, we show that parametric bijectors trained on samples from a desired prior density provide a general purpose method for constructing transformations from the uniform base density to a target prior, enabling the practical use of nested sampling under arbitrary priors. We demonstrate the use of trained bijectors in conjunction with nested sampling on a number of examples from cosmology. [ABSTRACT FROM AUTHOR]

Background: Pesticide residues are analyzed in thousands of samples yearly by national authorities and private laboratories. Intensive research is ongoing to develop new methods or improve existing ones concentrating on extraction, cleanup, and detection techniques. Little attention has been paid to the contribution of prior steps in the determination process to overall laboratory sampling errors, though several publications demonstrate their practical importance. Consequently, the repeatability and reproducibility of the results are often reported based on the recovery tests alone. A few previous publications are cited in this paper which illustrate the magnitude of random errors derived from subsampling, comminution of analytical samples, and selection of small test portions. Objectives: We aim to call attention to the importance of considering all steps of laboratory sampling and analysis processes in calculating the combined uncertainty of results and realistic performance assessments of methods including their longterm intermediate precision. Method: Validation of laboratory sampling of large fruits is used to illustrate the recommended procedures, determination of their random error, and long-term method performance. Results: The results indicate that subsampling, comminution, and selection of test portions can be major contributors to the combined uncertainty of results. Conclusions: All these steps should be considered in estimation of random variation (uncertainty) of measured residues. Highlights: Random error of laboratory sampling for pesticide residues. Mass reduction of large crop units. Internal quality control of laboratory operations. [ABSTRACT FROM AUTHOR]

The National Marine Fisheries Service is the agency charged with estimating the number of fish removed from US oceans by recreational anglers. Two surveys produce data for these estimates; one measures number of angler trips and the other fish caught per trip by species, geography, and time period. Both surveys collect data from people and have the usual sources of nonsampling error afflicting demographic surveys. Due to lack of accessibility to fishing sites, they also have additional sources such as undercoverage. The rare incidence of saltwater fishing among the general population makes the current method costly and time-consuming. Consequently, new ways of obtaining information to supplement or replace the current method are of interest. One is the electronic logbook (ELB); this approach allows anglers to self-report their catch using cellphones or other communication devices. Estimation of catch from these data are possible with estimators using capture-recapture methods, but new sources of nonsampling error are rising. In this article, we examine three sources of nonsampling error in estimators of catch and approximate their biasing effect. We illustrate the method by comparing the effects using data from an ELB study in the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Sampling error during screening and surveillance endoscopy is a well-recognized problem. Wide-area transepithelial sampling with three-dimensional computer-assisted analysis (WATS3D), used adjunctively to forceps biopsy (FB), has been shown to increase the detection of Barrett's esophagus (BE) and BE-associated neoplasia. We evaluated the clinical utility of WATS3D and its impact on the management of patients with BE and dysplasia. Between 2013 and 2018, 432 consecutive patients who had a WATS3D positive and an accompanying FB negative result were identified. Physicians were contacted to determine if the WATS3D result impacted their decision to enroll patients in surveillance or increase the frequency of surveillance, recommend ablation, and/or initiate or increase the dose of proton pump inhibitors (PPIs). WATS3D directly impacted the management of 97.8% of 317 BE patients; 96.2% were enrolled in surveillance and 60.2% were started on PPIs or their dose was increased. WATS3D impacted the management of 94.9% and 94.1% of the 98 low-grade dysplasia and 17 high-grade dysplasia patients, respectively. As a result of WATS3D, 33.7% of low-grade dysplasia and 70.6% of high-grade dysplasia patients underwent endoscopic therapy. More than 37% of all dysplasia patients were enrolled in a surveillance program, and nearly 30% were scheduled to be surveilled more frequently. PPIs were either initiated, or the dose was increased in more than 54% of all dysplasia patients. We demonstrate that WATS3D has high clinical utility. By prompting physicians to change their clinical management in patients with negative FB results, WATS3D, used adjunctively to FB, directly impacts patient management, and improves patient outcomes. [ABSTRACT FROM AUTHOR]

Cosmological analyses of galaxy surveys rely on knowledge of the redshift distribution of their galaxy sample. This is usually derived from a spectroscopic and/or many-band photometric calibrator survey of a small patch of sky. The uncertainties in the redshift distribution of the calibrator sample include a contribution from shot noise, or Poisson sampling errors, but, given the small volume they probe, they are dominated by sample variance introduced by large-scale structures. Redshift uncertainties have been shown to constitute one of the leading contributions to systematic uncertainties in cosmological inferences from weak lensing and galaxy clustering, and hence they must be propagated through the analyses. In this work, we study the effects of sample variance on small-area redshift surveys, from theory to simulations to the COSMOS2015 data set. We present a three-step Dirichlet method of resampling a given survey-based redshift calibration distribution to enable the propagation of both shot noise and sample variance uncertainties. The method can accommodate different levels of prior confidence on different redshift sources. This method can be applied to any calibration sample with known redshifts and phenotypes (i.e. cells in a self-organizing map , or some other way of discretizing photometric space), and provides a simple way of propagating prior redshift uncertainties into cosmological analyses. As a worked example, we apply the full scheme to the COSMOS2015 data set, for which we also present a new, principled SOM algorithm designed to handle noisy photometric data. We make available a catalogue of the resulting resamplings of the COSMOS2015 galaxies. [ABSTRACT FROM AUTHOR]

In the current era of Gaia and large, high signal-to-noise stellar spectroscopic surveys, there is an unmet need for a reliable library of fundamentally calibrated stellar effective temperatures based on accurate stellar diameters. Here, we present a set of precision diameters and temperatures for a sample of 6 dwarf, 5 sub-giant, and 5 giant stars observed with the PIONIER beam combiner at the VLTI. Science targets were observed in at least two sequences with five unique calibration stars each for accurate visibility calibration and to reduce the impact of bad calibrators. We use the standard PIONIER data reduction pipeline, but bootstrap over interferograms, in addition to employing a Monte Carlo approach to account for correlated errors by sampling stellar parameters, limb darkening coefficients, and fluxes, as well as predicted calibrator angular diameters. The resulting diameters were then combined with bolometric fluxes derived from broad-band Hipparcos–Tycho photometry and MARCS model bolometric corrections, plus parallaxes from Gaia to produce effective temperatures, physical radii, and luminosities for each star observed. Our stars have mean angular diameter and temperatures uncertainties of 0.8 per cent and 0.9 per cent, respectively, with our sample including diameters for 10 stars with no pre-existing interferometric measurements. The remaining stars are consistent with previous measurements, with the exception of a single star which we observe here with PIONIER at both higher resolution and greater sensitivity than was achieved in earlier work. [ABSTRACT FROM AUTHOR]

This essay attempts to describe the apples and oranges problem in meta-analyses. Essentially, some meta-analyses combine original studies of various variables that are not the same pairs of variables. Metaphorically, they meta-analyze the effects of fruit when they should conduct separate meta-analyses of apples and oranges. This practice is inconsistent with the assumptions behind the meta-analytic formulae concerning sampling error and makes meta-analytic estimates difficult to interpret. Meta-analysis teams are advised to justify their choices and types of evidence are described to assist researchers and reviewers in assessing and justifying when constructs can and cannot be combined together in a meta-analysis. [ABSTRACT FROM AUTHOR]

In the analysis of survey data, it is of interest to estimate and quantify uncertainty about means or totals for each of several nonoverlapping subpopulations or areas. When the sample size for a given area is small, standard confidence intervals based on data only from that area can be unacceptably wide. In order to reduce interval width, practitioners often utilize multilevel models in order to borrow information across areas, resulting in intervals centered around shrinkage estimators. However, such intervals only have the nominal coverage rate on average across areas under the assumed model for across-area heterogeneity. The coverage rate for a given area depends on the actual value of the area mean and can be nearly zero for areas with means that are far from the across-group average. As such, the use of uncertainty intervals centered around shrinkage estimators are inappropriate when area-specific coverage rates are desired. In this article, we propose an alternative confidence interval procedure for area means and totals under normally distributed sampling errors. This procedure not only has constant 1 − α frequentist coverage for all values of the target quantity but also uses auxiliary information to borrow information across areas. Because of this, the corresponding intervals have shorter expected lengths than standard confidence intervals centered on the unbiased direct estimator. Importantly, the coverage of the procedure does not depend on the assumed model for across-area heterogeneity. Rather, improvements to the model for across-area heterogeneity result in reduced expected interval width. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to review and illustrate the sometimes diagnostically challenging features of cardiac sarcoidosis. We emphasize variable phenotypes presented at explant and biopsy evaluation and review literature regarding ancillary clinical and pathologic studies to enhance diagnostic accuracy.Methods: A literature review was performed and two cardiac sarcoidosis cases were illustrated.Results: Our cases and literature review demonstrate the pathologic spectrum of cardiac sarcoidosis. Irregular left ventricular free wall involvement is most common, followed by the interventricular septum and right ventricle. Although granulomas are often composed of tight epithelioid macrophage aggregates, early granulomas comprise loosely associated macrophages with lymphocyte predominance. Chronic disease leads to fibrosis and end-stage heart failure. Sampling errors and variable histology cause low endomyocardial biopsy sensitivity.Conclusions: Current guidelines use clinical, radiologic, and immunohistologic criteria for diagnosing cardiac sarcoidosis. Knowledge of these guidelines will assist pathologists in making accurate diagnosis of this disease. [ABSTRACT FROM AUTHOR]

Linked data are increasingly being used for epidemiological research, to enhance primary research, and in planning, monitoring and evaluating public policy and services. Linkage error (missed links between records that relate to the same person or false links between unrelated records) can manifest in many ways: as missing data, measurement error and misclassification, unrepresentative sampling, or as a special combination of these that is specific to analysis of linked data: the merging and splitting of people that can occur when two hospital admission records are counted as one person admitted twice if linked and two people admitted once if not. Through these mechanisms, linkage error can ultimately lead to information bias and selection bias; so identifying relevant mechanisms is key in quantitative bias analysis. In this article we introduce five key concepts and a study classification system for identifying which mechanisms are relevant to any given analysis. We provide examples and discuss options for estimating parameters for bias analysis. This conceptual framework provides the 'links' between linkage error, information bias and selection bias, and lays the groundwork for quantitative bias analysis for linkage error. [ABSTRACT FROM AUTHOR]

Spectral induced polarization (SIP), describing the measurement of the frequency domain electrical impedance magnitude and phase of porous materials, has been widely used to characterize subsurface hydrological/biogeochemical properties and processes. SIP data collected at frequencies higher than 100 Hz are expected to describe the polarization of small particles providing insights into the physicochemical properties of clays, nanoparticles and microorganisms. However, the phase measurements at these high frequencies are often contaminated by errors due to the parasitic capacitive coupling of the SIP instrument, especially for lower conductivity samples. We developed a model showing the measured phase is the sum of the true sample phase and an error term ωC in Z x, where ω is the angular frequency; C in is the instrument input capacitance and Z x is a measurable impedance function related to the sample holder properties and the reference resistor. Based on this model, a new phase correction method is proposed that results in highly accurate SIP data up to 20 kHz as well as the determination of C in. We tested the correction method using electric circuits, NaCl fluids and three unconsolidated samples (sand, sand-clay and sand-pyrite mixtures). The corrected phase for the circuit and NaCl fluid experiments showed excellent agreement with the theoretical phase response across the studied frequency range (errors <1 mrad). For unconsolidated samples, removal of errors results in phase spectra more consistent with expected polarization mechanisms, as based on phase peaks recorded for small pyrite and clay particles at high frequencies. These phase peaks could not be identified in the uncorrected data. Our approach can substantially enhance the value of the SIP method for the characterization of fine-grained sediments and rocks. [ABSTRACT FROM AUTHOR]

Motivation Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains. Results We evaluate our method both on simulated data with varying degrees of distribution mismatch and on real data, considering the problem of age prediction based on DNA methylation data across multiple tissues. Compared with a non-adaptive standard model, our approach substantially reduces errors on samples with a mismatched distribution. On real data, we achieve far lower errors on cerebellum samples, a tissue which is not part of the training data and poorly predicted by standard models. Our results demonstrate that unsupervised domain adaptation is possible for applications in computational biology, even with many more features than samples. Availability and implementation Source code is available at https://github.com/PfeiferLabTue/wenda. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques. [ABSTRACT FROM AUTHOR]

Directed acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph's structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially when a local or sequential approach is used to enumerate edge directions by testing or optimizing a criterion locally, as a local method may break down even for moderately sized graphs. We propose a novel approach to simultaneously identifying all estimable directed edges and model parameters, using constrained maximum likelihood with nonconvex constraints. We develop a constraint reduction method that constructs a set of active constraints from super-exponentially many constraints. This, coupled with an alternating direction method of multipliers and a difference convex method, permits efficient computation for large-graph learning. We show that the proposed method consistently reconstructs identifiable directions of the true graph and achieves the optimal performance in terms of parameter estimation. Numerically, the method compares favourably with competitors. A protein network is analysed to demonstrate that the proposed method can make a difference in identifying the network's structure. [ABSTRACT FROM AUTHOR]

The article offers a reply to a response by James G. Webster and Harsh Taneja to the authors' issue article "Networks of Audience Overlap in the Consumption of Digital News." Topics include the use of network methods to analyze audience data, the use of statistical modeling in relation to sampling error, and the implications of audience overlap for cultural aspects of mass media audiences.

The branching order of major angiosperm lineages is a challenging phylogenetic question that has received substantial attention in recent years. Two main competing hypotheses place the New Caledonian Amborella as either sister to all other extant angiosperms (Amborella-sister) or to thewater lilies (AmborellaþNymphaeales). Here, we revisit this question by expanding a transcriptomic data set of 310 genes previously assembled to include data from seven species comprising two major lineages of flowering plants that were poorly represented or missing from the original study. We also applied gene genealogy interrogation, a recent approach based on constrained tree searches in combination with topology tests, to account for gene tree estimation error and its downstream effects in coalescent analyses. In addition to gene genealogy interrogation, we conducted a large number of multilocus analyses, including concatenation and coalescent approaches (using both unconstrained and constrained gene trees), and based on different data sets (original and expanded) and data types (nucleotide and amino acid sequences). We show that the majority of gene trees favor Amborella-sister topology, and all multilocus analyses conducted (concatenation and coalescent) provide overwhelming support for this hypothesis regardless of data type. Beyond resolving the evolutionary root of angiosperms with confidence, our results highlight the importance of both broadening taxonomic sampling in phylogenomics and addressing the effects of gene tree error in summary coalescent inferences. [ABSTRACT FROM AUTHOR]

Location awareness has become one of the most popular areas for mobile internet applications. Normally, the method of maximum likelihood estimation (MLE) can be used to estimate the position of the unknown nodes. However, when the number of anchor nodes is small within the communication range, the traditional methods cannot work very well. This paper provides a novel location approach to estimate the position by selecting valuable neighbors (both anchor and regular nodes) with Minimized Error Propagation Location method based on Error estimation (MEPLE). The simulation results show that the MEPLE outperforms the MLE overall, especially when the number of anchor nodes is relatively small such as between 10 and 20, MEPLE can reduce 50-80% estimation error on average. [ABSTRACT FROM AUTHOR]

Although random routes are an important face-to-face sampling method, there is very little research about the procedure. The idea that random route sampling leads to a uniform selection of households has never been tested. This study challenges this notion in a simulation and uses registration office data to verify the impact of selection errors on survey results. All tested routes strongly violate the equal probability assumption and lead to biased expected values in multiple variables. The strongest errors were found in variables that were related to the spatial location of households. Furthermore, errors were related not only to random route instructions but also to the selection of starting points for these routes. This study is of relevance for scientists using random route data for their research as well as for surveys applying random route procedures. Data analysts can use the results to gain an impression about the degree of bias, whereas researchers that use random route samples should view these findings as inducement to improve the random route methodology and develop alternatives. [ABSTRACT FROM AUTHOR]

Compatible discrete operator schemes preserve basic properties of the continuous model at the discrete level. They combine discrete differential operators that discretize exactly topological laws and discrete Hodge operators that approximate constitutive relations. We devise and analyse two families of such schemes for the Stokes equations in curl formulation, with the pressure degrees of freedom located at either mesh vertices or cells. The schemes ensure local mass and momentum conservation. We prove discrete stability by establishing novel discrete Poincaré inequalities. Using commutators related to the consistency error, we derive error estimates with first-order convergence rates for smooth solutions. We analyse two strategies for discretizing the external load, so as to deliver tight error estimates when the external load has a large curl-free or divergence-free part. Finally, numerical results are presented on three-dimensional polyhedral meshes. [ABSTRACT FROM AUTHOR]

We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance-covariance matrices ( G). Large-sample theory shows that maximum-likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G. This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G, and of functions of G. We refer to this as the REML- MVN method. This has been implemented in the mixed-model program WOMBAT. Estimates of sampling variances from REML- MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo ( MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20-dimensional data set for Drosophila wings. REML- MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best-estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML- MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest. [ABSTRACT FROM AUTHOR]

The estimation of species trees using multiple loci has become increasingly common. Because different loci can have different phylogenetic histories (reflected in different gene tree topologies) for multiple biological causes, new approaches to species tree estimation have been developed that take gene tree heterogeneity into account. Among these multiple causes, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is potentially the most common cause of gene tree heterogeneity, and much of the focus of the recent literature has been on how to estimate species trees in the presence of ILS. Despite progress in developing statistically consistent techniques for estimating species trees when gene trees can differ due to ILS, there is substantial controversy in the systematics community as to whether to use the new coalescent-based methods or the traditional concatenation methods. One of the key issues that has been raised is understanding the impact of gene tree estimation error on coalescent-based methods that operate by combining gene trees. Herewe explore the mathematical guarantees of coalescent-based methods when analyzing estimated rather than true gene trees. Our results provide some insight into the differences between promise of coalescent-based methods in theory and their performance in practice. [ABSTRACT FROM AUTHOR]

Motivation: It is commonly assumed in pattern recognition that cross-validation error estimation is ‘almost unbiased’ as long as the number of folds is not too small. While this is true for random sampling, it is not true with separate sampling, where the populations are independently sampled, which is a common situation in bioinformatics.Results: We demonstrate, via analytical and numerical methods, that classical cross-validation can have strong bias under separate sampling, depending on the difference between the sampling ratios and the true population probabilities. We propose a new separate-sampling cross-validation error estimator, and prove that it satisfies an ‘almost unbiased’ theorem similar to that of random-sampling cross-validation. We present two case studies with previously published data, which show that the results can change drastically if the correct form of cross-validation is used.Availability and implementation: The source code in C++, along with the Supplementary Materials, is available at: http://gsp.tamu.edu/Publications/supplementary/zollanvari13/.Contact: ulisses@ece.tamu.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms by which nutritional and pharmaceutical factors can manipulate adipose tissue growth and development in production animals has direct and indirect effects in the profitability of an enterprise. Adipocyte cellularity (number and size) is a key biological response that is commonly measured in animal science research. The variability and sampling of adipocyte cellularity within a muscle has been addressed in previous studies, but no attempt to critically investigate these issues has been proposed in the literature. The present study evaluated 2 sampling techniques (random and systematic) in an attempt to minimize sampling bias and to determine the minimum number of samples from 1 to 15 needed to represent the overall adipose tissue in the muscle. Both sampling procedures were applied on adipose tissue samples dissected from 30 longissimus muscles from cattle finished either on grass or grain. Briefly, adipose tissue samples were fixed with osmium tetroxide, and size and number of adipocytes were determined by a Coulter Counter. These results were then fit in a finite mixture model to obtain distribution parameters of each sample. To evaluate the benefits of increasing number of samples and the advantage of the new sampling technique, the concept of acceptance ratio was used; simply stated, the higher the acceptance ratio, the better the representation of the overall population. As expected, a great improvement on the estimation of the overall adipocyte cellularity parameters was observed using both sampling techniques when sample size number increased from 1 to 15 samples, considering both techniques' acceptance ratio increased from approximately 3 to 25%. When comparing sampling techniques, the systematic procedure slightly improved parameters estimation. The results suggest that more detailed research using other sampling techniques may provide better estimates for minimum sampling. [ABSTRACT FROM AUTHOR]

A set of accurate quadrature rules applicable to a class of integrable functions with isolated singularities is designed and analysed theoretically in one and two dimensions. These quadrature rules are based on the trapezoidal rule with corrected quadrature weights for points in the vicinity of the singularity. To compute the correction weights, small-size ill-conditioned systems have to be solved. The convergence of the correction weights is accelerated by the use of compactly supported functions that annihilate boundary errors. Convergence proofs with error estimates for the resulting quadrature rules are given in both one and two dimensions. The tabulated weights are specific for the singularities under consideration, but the methodology extends to a large class of functions with integrable isolated singularities. Furthermore, in one dimension we have obtained a closed form expression based on which the modified weights can be computed directly. [ABSTRACT FROM AUTHOR]

We present a new approach to reduce a sparse, linear system of equations associated with tomographic inverse problems. We begin by making a modification to the commonly used compressed sparse-row format, whereby our format is tailored to the sparse structure of finite-frequency (volume) sensitivity kernels in seismic tomography. Next, we cluster the sparse matrix rows to divide a large matrix into smaller subsets representing ray paths that are geographically close. Singular value decomposition of each subset allows us to project the data onto a subspace associated with the largest eigenvalues of the subset. After projection we reject those data that have a signal-to-noise ratio (SNR) below a chosen threshold. Clustering in this way assures that the sparse nature of the system is minimally affected by the projection. Moreover, our approach allows for a precise estimation of the noise affecting the data while also giving us the ability to identify outliers. We illustrate the method by reducing large matrices computed for global tomographic systems with cross-correlation body wave delays, as well as with surface wave phase velocity anomalies. For a massive matrix computed for 3.7 million Rayleigh wave phase velocity measurements, imposing a threshold of 1 for the SNR, we condensed the matrix size from 1103 to 63 Gbyte. For a global data set of multiple-frequency P wave delays from 60 well-distributed deep earthquakes we obtain a reduction to 5.9 per cent. This type of reduction allows one to avoid loss of information due to underparametrizing models. Alternatively, if data have to be rejected to fit the system into computer memory, it assures that the most important data are preserved. [ABSTRACT FROM PUBLISHER]

Motivation: The determination of absolute quantities of proteins in biological samples is necessary for multiple types of scientific inquiry. While relative quantification has been commonly used in proteomics, few proteomic datasets measuring absolute protein quantities have been reported to date. Various technologies have been applied using different types of input data, e.g. ion intensities or spectral counts, as well as different absolute normalization strategies. To date, a user-friendly and transparent software supporting large-scale absolute protein quantification has been lacking.Results: We present a bioinformatics tool, termed aLFQ, which supports the commonly used absolute label-free protein abundance estimation methods (TopN, iBAQ, APEX, NSAF and SCAMPI) for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation.Availability and implementation: aLFQ is written in R and freely available under the GPLv3 from CRAN (http://www.cran.r-project.org). Instructions and example data are provided in the R-package. The raw data can be obtained from the PeptideAtlas raw data repository (PASS00321).Contact: lars.malmstroem@imsb.biol.ethz.chSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

An editorial is presented on the diagnostic confirmation tests for Buruli ulcer, a skin disease caused by Mycobacterium ulcerans. It references the study conducted by M. Eddyani and colleagues which examines the accuracy of diagnostic tests and approaches including the use of polymerase chain reaction (PCR).

Background Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases. Results Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis -associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis. Conclusions Our results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis. [ABSTRACT FROM AUTHOR]

Mark A. Jones, Stefan H. Steiner. Assessing the effect of estimation error on risk-adjusted CUSUM chart performance. Int J Qual Health Care (2012) 24(2): 176–181 doi: 10.1093/intqhc/mzr082. The authors would like to correct an error identified in the above paper. Table 5 included incorrect information. The correct table has been reprinted below. Furthermore, in the discussion on p. 180 of this paper, one of the incorrect numbers in Table 5 was quoted. This section is reproduced below with the correct numbers. In the case of homogeneous patients where adverse event risk was assumed to be constant at 6.6% the estimated level of estimation error: SD (ARL0) = 85.9 was less than the equivalent risk-adjusted scenario where SD (ARL0) = 89.2 but only by around 4%. [ABSTRACT FROM AUTHOR]