Statistical Uncertainty Research Articles

Calculating linear and nonlinear multi-ensemble slow collective variables for protein folding

Traditional molecular dynamics simulation of biomolecules suffers from the conformational sampling problem. It is often difficult to produce enough valid data for post analysis such as free energy calculation and transition path construction. To improve the sampling, one practical solution is putting an adaptive bias potential on some predefined collective variables. The quality of collective variables strongly affects the sampling ability of a molecule in the simulation. In the past, collective variables were built with the sampling data at a constant temperature. This is insufficient because of the same sampling problem. In this work, we apply the standard weighted histogram analysis method to calculate the multi-ensemble averages of pairs of time-lagged features for the construction of both linear and nonlinear slow collective variables. Compared to previous single-ensemble methods, the presented method produces averages with much smaller statistical uncertainties. The generated collective variables help a peptide and a miniprotein fold to their near-native states in a short simulation time period. By using the method, enhanced sampling simulations could be more effective and productive.

Stealth dark matter spectrum using Laplacian Heaviside smearing and irreducible representations

We present nonperturbative lattice calculations in the quenched approximation of the low-lying meson and baryon spectrum of the SU(4) gauge theory with fundamental fermion constituents. This theory is one instance of stealth dark matter, a class of strongly coupled theories, where the lowest mass stable baryon is the dark matter candidate. This work constitutes the first milestone in the program to study stealth dark matter self-interactions. Here, we focus on reducing excited state contamination in the single-baryon channel by applying the Laplacian Heaviside method, as well as projecting our baryon operators onto the irreducible representations of the octahedral group. We compare our resulting spectrum to previous work involving Gaussian smeared nonprojected operators and find good agreement with reduced statistical uncertainties. We also present the spectrum of the low-lying odd-parity baryons for the first time. Published by the American Physical Society 2024

The Arizona Molecular ISM Survey with the SMT: Survey Overview and Public Data Release

The CO(1–0) line has been carefully calibrated as a tracer of molecular gas mass. However, recent studies often favor higher J transitions of the CO molecule, which are brighter and accessible for redshift ranges where CO(1–0) is not. These lines are not perfect analogs for CO(1–0), owing to their more stringent excitation conditions, and must be calibrated for use as molecular gas tracers. Here, we introduce the Arizona Molecular ISM Survey with the SMT, a multi-CO line survey of z ∼ 0 galaxies conducted to calibrate the CO(2–1) and CO(3–2) lines. The final survey includes CO(2–1) spectra of 176 galaxies and CO(3–2) spectra for a subset of 45. We supplement these with archival CO(1–0) spectra from xCOLD GASS for all sources and additional CO(1–0) observations with the Kitt Peak 12 m Telescope. Targets were selected to be representative of the 109 M ⊙ ≤ M * ≤ 1011.5 M ⊙ galaxy population. Our project emphasized careful characterization of statistical and systematic uncertainties to enable studies of trends in CO line ratios. We show that optical and CO disk sizes are on average equal, for both the CO(1–0) and CO(2–1) line. We measure the distribution of CO line luminosity ratios, finding medians (16th–84th percentile) of 0.71 (0.51–0.96) for the CO(2–1)-to-CO(1–0) ratio, 0.39 (0.24–0.53) for the CO(3–2)-to-CO(1–0) ratio, and 0.53 (0.41–0.74) for the CO(3–2)-to-CO(2–1) ratio. A companion paper presents our study of CO(2–1)'s applicability as a molecular gas mass tracer and search for trends in the CO(2–1)-to-CO(1–0) ratio. Our catalog of CO line luminosities is publicly available.

New Prediction Of Cracks Propagation In Repaired Steel Plate With Bonded Composite Patch At Cyclic Loading

Abstract This study presents a numerical prediction of the fatigue life of steel panels repaired by a composite patch. The effect of length cracks, the stress ratio R and properties of the patch is presented. The obtained results show that the bonded composite repair significantly reduces the stress intensity factors at the tip of repaired cracks. The results are in a good agreement with those in the literature. The Monte Carlo method is used to predict the distribution function governing crack propagation in fatigue analysis. In computing the failure probability of the structure, we consider the statistical uncertainty associated with key variables, along with the previously discussed model uncertainty. The results obtained highlight the considerable impact of variations in crack length and stress ratio on the distribution function. Notably, uncertainty in these parameters significantly amplifies the probability of structural failure in plates, thereby diminishing overall structural durability.

Measurement of the branching fraction of the decay J/ψ→pp¯η

A high-precision measurement of the branching fraction of the decay J/ψ→pp¯η is performed using (10 087±44)×106 J/ψ events recorded by the BESIII detector at the BEPCII storage ring. The branching fractions of the two decays J/ψ→pp¯η(η→γγ) and J/ψ→pp¯η(η→π+π−π0) are measured individually to be B(J/ψ→pp¯η(η→γγ))=(1.480±0.001±0.024)×10−3 and B(J/ψ→pp¯η(η→π+π−π0))=(1.557±0.003±0.038)×10−3, where the first uncertainties are statistical and the second systematic. Both results are compatible within their uncorrelated systematic uncertainties. The combined result is B(J/ψ→pp¯η)=(1.495±0.001±0.023)×10−3, where the first uncertainty is the combined statistical uncertainty and the second one the combined systematic uncertainty of both analyses, incorporating correlations between them. In addition, the pp¯ threshold region is investigated for a potential threshold enhancement, and no evidence for one is observed. Published by the American Physical Society 2024

The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 < z < 1.13, which is the largest sample of supernovae from any single survey and increases the number of known z > 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are σΩM,stat+sysΛCDM= 0.017 in a flat ΛCDM model, and (σΩM,σw)stat+syswCDM = (0.082, 0.152) in a flat wCDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.

Modeling the oxygen effect in DNA strand break induced by gamma-rays with TOPAS-nBio

Objective.To present and validate a method to simulate from first principles the effect of oxygen on radiation-induced double-strand breaks (DSBs) using the Monte Carlo Track-structure code TOPAS-nBio.Approach.Two chemical models based on the oxygen fixation hypothesis (OFH) were developed in TOPAS-nBio by considering an oxygen adduct state of DNA and creating a competition kinetic mechanism between oxygen and the radioprotective molecule WR-1065. We named these models 'simple' and 'detailed' due to the way they handle the hydrogen abstraction pathways. We used the simple model to obtain additional information for the •OH-DNA hydrogen abstraction pathway probability for the detailed model. These models were calibrated and compared with published experimental data of linear and supercoiling fractions obtained with R6K plasmids, suspended in dioxane as a hydroxyl scavenger, and irradiated with137Cs gamma-rays. The reaction rates for WR-1065 and O2with DNA were taken from experimental works. Single-Strand Breaks (SSBs) and DSBs as a function of the dose for a range of oxygen concentrations [O2] (0.021%-21%) were obtained. Finally, the hypoxia reduction factor (HRF) was obtained from DSBs.Main Results.Validation results followed the trend of the experimental within 12% for the supercoiled and linear plasmid fractions for both models. The HRF agreed with measurements obtained with137Cs and 200-280 kVp x-ray within experimental uncertainties. However, the HRF at an oxygen concentration of 2.1% overestimated experimental results by a factor of 1.7 ± 0.1. Increasing the concentration of WR-1065 from 1 mM to 10-100 mM resulted in a HRF difference of 0.01, within the 8% statistical uncertainty between TOPAS-nBio and experimental data. This highlights the possibility of using these chemical models to recreate experimental HRF results.Significance.Results support the OFH as a leading cause of oxygen radio-sensitization effects given a competition between oxygen and chemical DNA repair molecules like WR-1065.

Context-Dependent Criteria for Dirichlet Process in Sequential Decision-Making Problems

In models with insufficient initial information, parameter estimation can be subject to statistical uncertainty, potentially resulting in suboptimal decision-making; however, delaying implementation to gather more information can also incur costs. This paper examines an extension of information-theoretic approaches designed to address this classical dilemma, focusing on balancing the expected profits and the information needed to be obtained about all of the possible outcomes. Initially utilized in binary outcome scenarios, these methods leverage information measures to harmonize competing objectives efficiently. Building upon the foundations laid by existing research, this methodology is expanded to encompass experiments with multiple outcome categories using Dirichlet processes. The core of our approach is centered around weighted entropy measures, particularly in scenarios dictated by Dirichlet distributions, which have not been extensively explored previously. We innovatively adapt the technique initially applied to binary case to Dirichlet distributions/processes. The primary contribution of our work is the formulation of a sequential minimization strategy for the main term of an asymptotic expansion of differential entropy, which scales with sample size, for non-binary outcomes. This paper provides a theoretical grounding, extended empirical applications, and comprehensive proofs, setting a robust framework for further interdisciplinary applications of information-theoretic paradigms in sequential decision-making.

A comprehensive inventory of Australia's critical minerals: Simplifying the complexity of critical resources

National inventories of mineral resources invariably exclude critical minerals produced as smelter-refinery by-products due to insufficient data. This study addresses this gap through the development of a comprehensive database of Australia's critical minerals resources in mines and mineral deposits combined with extensive geochemical data analysis. We provide a description of how such a database can be constructed for any country using publicly available information and consider a range of uncertainties arising from the use of proxy data to estimate critical mineral grades. A detailed analysis of Australian critical mineral resource endowments is presented alongside a review and discussion of reporting mechanisms and transparency. Our results show that despite statistical uncertainties, data complexities and limited prior accounting, estimated Australian endowments of a range of critical metals which currently remain unreported are likely to be substantial (e.g., an additional 4.2 Mt Co). This Australian case study suggests that the global development of similar databases is likely to reveal substantially greater endowments of critical metals than previously recognised, providing confidence in the ability to simplify the previously complex assessment of critical minerals required for the modern world's technological needs.

Search for X(3872)→π0π0χc1,2

Using 10.1 fb−1 of e+e− collision data collected by the BESIII detector with center-of-mass energies between 4.15 GeV and 4.30 GeV, we search for the decays X(3872)→π0π0χc1,2, where the X(3872) is produced in e+e−→γX(3872). No evidence above 3σ is found for either decay. Upper limits at the 90% CL on the branching fractions of X(3872)→π0π0χc1,2 normalized to the branching fraction of X(3872)→π+π−J/ψ are set to be B(X(3872)→π0π0χc1)/B(X(3872)→π+π−J/ψ)&lt;1.1 and B(X(3872)→π0π0χc2)/B(X(3872)→π+π−J/ψ)&lt;0.5, taking into account both statistical and systematic uncertainties. Published by the American Physical Society 2024

Methodical evaluation of Boyle temperatures using Mayer sampling Monte Carlo with application to polymers in implicit solvent.

The Boyle temperature, TB, for an n-segment polymer in solution is the temperature where the second osmotic virial coefficient, A2, is zero. This characteristic is of interest for its connection to the polymer condensation critical temperature, particularly for n → ∞. TB can be measured experimentally or computed for a given model macromolecule. For the latter, we present and examine two approaches, both based on the Mayer-sampling Monte Carlo (MSMC) method, to calculate Boyle temperatures as a function of model parameters. In one approach, we use MSMC calculations to search for TB, as guided by the evaluation of temperature derivatives of A2. The second approach involves numerical integration of an ordinary differential equation describing how TB varies with a model parameter, starting from a known TB. Unlike general MSMC calculations, these adaptations are appealing because they neither invoke a reference for the calculation nor use special averages needed to avoid bias when computing A2 directly. We demonstrate these methods by computing TB lines for off-lattice linear Lennard-Jones polymers as a function of chain stiffness, considering chains of length n ranging from 2 to 512 monomers. We additionally perform calculations of single-molecule radius of gyration Rg and determine the temperatures Tθ, where linear scaling of Rg2 with n is observed, as if the polymers were long random-walk chains. We find that Tθ and TB seem to differ by 6% in the n → ∞ limit, which is beyond the statistical uncertainties of our computational methodology. However, we cannot rule out systematic error relating to our extrapolation procedure as being the source of this discrepancy.

HIGHLY ADAPTIVE LASSO: MACHINE LEARNING THAT PROVIDES VALID NONPARAMETRIC INFERENCE IN REALISTIC MODELS.

Understanding treatment effects on health-related outcomes using real-world data requires defining a causal parameter and imposing relevant identification assumptions to translate it into a statistical estimand. Semiparametric methods, like the targeted maximum likelihood estimator (TMLE), have been developed to construct asymptotically linear estimators of these parameters. To further establish the asymptotic efficiency of these estimators, two conditions must be met: 1) the relevant components of the data likelihood must fall within a Donsker class, and 2) the estimates of nuisance parameters must converge to their true values at a rate faster than . The Highly Adaptive LASSO (HAL) satisfies these criteria by acting as an empirical risk minimizer within a class of càdlàg functions with a bounded sectional variation norm, which is known to be Donsker. HAL achieves the desired rate of convergence, thereby guaranteeing the estimators' asymptotic efficiency. The function class over which HAL minimizes its risk is flexible enough to capture realistic functions while maintaining the conditions for establishing efficiency. Additionally, HAL enables robust inference for non-pathwise differentiable parameters, such as the conditional average treatment effect (CATE) and causal dose-response curve, which are important in precision health. While these parameters are often considered in machine learning literature, these applications typically lack proper statistical inference. HAL addresses this gap by providing reliable statistical uncertainty quantification that is essential for informed decision-making in health research.

Quantifying measurement uncertainty in renal transplant biopsy assessment.

Renal transplant biopsies provide insights into graft health and support decision making. The current evidence on links between biopsy scores and transplant outcomes suggests there may be numerous factors affecting biopsy scores. Here we adopt measurement science approach to investigate the sources of uncertainty in biopsy assessment and suggest techniques to improve its robustness. Histological assessments, Remuzzi scores, biopsy processing and clinical variables are obtained from 144 repeat biopsies originating from 16 deceased-donor kidneys. We conducted sensitivity analysis to find the morphometric features with highest discriminating power and studied the dependencies of these features on biopsy and stain type. The analysis results formed a basis for recommendations on reducing the assessment variability. Most morphometric variables are influenced by the biopsy and stain types. The variables with the highest discriminatory power are sclerotic glomeruli counts, healthy glomeruli counts per unit area, percentages of interstitial fibrosis and tubular atrophy as well as diameter and lumen of the worst artery. A revised glomeruli adequacy score is proposed to improve the robustness of the glomeruli statistics, whereby a minimum of 104 µm2 of cortex tissue is recommended to keep type 1 and type 2 error probabilities below 0.15 and 0.2. The findings are transferable to several biopsy scoring systems. We hope that this work will help practitioners to understand the sources of statistical uncertainty and improve the utility of renal biopsy.

Cross Section Evaluation for Exclusive Channels of K+Λ and K+Σ0 Electroproduction off Protons using CLAS Detector Data

In this work, a method for evaluating the cross sections of electroproduction of K+Λ and K+Σ 0 off protons in the region of invariant masses of final hadrons MK + MY &lt; W &lt; 2.65 GeV (MK and MY being the masses of the kaon and hyperon, respectively) and squares of four-momentum transfers of virtual photons, i.e., photon virtualities 0 &lt; Q2 &lt; 5 GeV2 , is developed based on experimental data of these exclusive channels’ cross sections measured by the CLAS detector in Hall B at Jefferson Lab. A set of algorithms has been implemented to evaluate the differential cross sections of these channels, along with their statistical and systematic uncertainties. A program was developed for the evaluation of differential cross sections and structure functions using C++ and Python libraries. An interactive website was created for working with the program, enabling the analysis of one-dimensional and two-dimensional dependences of structure functions and differential cross sections. The evaluation of differential cross sections for the K+Λ and K+Σ 0 electroproduction channels is necessary for extracting the structure function σLT ′ from the data on the polarization asymmetry of electroproduction reactions of these final states with longitudinally polarized electrons. The obtained results are also important for the development of realistic Monte Carlo event generators in planning future experiments and for evaluating the efficiency of detecting final particles when extracting reaction cross sections from experimental data

An interpretable machine learning-based model for shear resistance prediction of CFRP-strengthened RC beams using experimental and synthetic dataset

Existing analytical models for predicting the shear resistance of RC beams strengthened with externally bonded CFRP reinforcements exhibit deficient performance due to their inability to accurately capture the complex resisting mechanisms. Combined with significant statistical uncertainties in shear failure, driven by its brittle nature, this further undermines the reliability of these models. To address these limitations, this study leverages Machine Learning (ML) to develop more robust and reliable predictive tool. A rigorous feature-selection process identified eight predictors as the most influential. Subsequently, nine ML-algorithms were trained on a refined experimental dataset comprising 239 beams, with XGBoost emerging as the top performer. This model also outperformed established models likefib Bulletin-90 and ACI 2023 models. However, the limited scope of the experimental dataset constrained the model’s predictive performance especially when separately evaluated on beams strengthened with U-wraps, full wraps or side-bonded FRP configurations. Therefore, to achieve a more reliable model a synthetic dataset was generated using Tabular Variational Auto-Encoder. The XGBoost model trained with the synthetic dataset significantly improved the performance of the former model and exhibited better predictions for all strengthening configurations. Finally, to ensure the physical consistency of predictions, values obtained from the SHapley Additive exPlanations method were analysed.

System size effects on the free energy landscapes from molecular dynamics of phase-separating bilayers.

The "lipid raft" hypothesis proposes that cell membranes contain distinct domains of varying lipid compositions, where "rafts" of ordered lipids and cholesterol coexist with disordered lipid regions. Experimental and theoretical phase diagrams of model membranes have revealed multiple coexisting phases. Molecular dynamics (MD) simulations can also capture spontaneous phase separation of bilayers. However, these methods merely determine the signof the free energy change upon phase separation-whether or not it is favorable-but not the amplitude. Recently, we developed a workflow to compute the free energy of phase separation from MD simulations using the weighted ensemble method. However, while theoretical treatments generally focus on infinite systems and experimental measurements on mesoscopic to macroscopic systems, MD simulations are comparatively small. Therefore, if we are to put the results of these calculations into the appropriate context, we need to understand the effects the finite size of the simulation has on the computed free energy landscapes. In this study, we investigate this phenomenon by computing free energy profiles for a model phase-separating system as a function of system size, ranging from 324 to 10 110 lipids. The results suggest that, within the limits of statistical uncertainty, bulk-like behavior emerges once the systems contain roughly 4000 lipids.

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations

Abstract An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and m-mode analysis. In this paper, we propose an alternative Bayesian method to infer the coefficients of a full-sky spherical harmonic basis for a drift-scan telescope with potentially thousands of baselines, that can precisely encode the uncertainties and correlations between the parameters used to build the recovered image. We use Gaussian Constrained Realisations (GCR) to efficiently draw samples of the spherical harmonic coefficients, despite the very large parameter space and extensive sky-regions of missing data. Each GCR solution provides a complete, statistically-consistent gap-free realisation of a full-sky map conditioned on the available data, even when the interferometer’s field of view is small. Many realisations can be generated and used for further analysis and robust propagation of statistical uncertainties. In this paper, we present the mathematical formalism of the spherical harmonic GCR-method for radio interferometers. We focus on the recovery of diffuse emission as a use case, along with validation of the method against simulations with a known diffuse emission component.

Validation of the OpenMC Code for Fusion Applications: The FNG-Streaming Benchmark Case

In this work, we benchmark OpenMC against the FNG-ITER streaming experiment. FNG-ITER streaming, a high-quality experiment carried out at the ENEA laboratories in Frascati, Italy, was initially included in SINBAD (Shielding Integral Benchmark Archive and Database). More recently, the benchmark was included in the Compilation of Nuclear Data Experiments for Radiation Characterization as well. It consists of a neutron shielding experiment with a rather complex geometry that constitutes an appropriate validation study for the use of weight windows within OpenMC. Measurements include flux detection via four different types of activation foils divided into three batches and a set of thermoluminescent detectors for nuclear heating. The OpenMC results are in very good agreement with those of MCNP and the experimental measurements, with the majority of the discrepancies within the combined statistical error and experimental uncertainty (less than 10% computed measured discrepancy).

Evaluation of aerial thermography for measuring the thermal transmittance (U-value) of a building façade

In efforts to mitigate greenhouse gas emissions in the construction sector, policies promoting energy efficiency improvements in existing buildings are being implemented. Accurately assessing the impact of energy retrofit strategies requires precise determination of thermal envelope performance through energy audits. Aerial thermography, employing unmanned aerial vehicles (UAVs) equipped with infrared thermal cameras (IRT), is an underused method overcoming limitations in energy audits of large buildings or complexes. This approach offers an elevated perspective, providing an overview of the building’s thermal patterns, aiding in the identification of thermal envelope defects. By measuring temperature distribution, even in inaccessible envelope areas, the severity of identified defects can be estimated. Beyond detecting thermal bridges, air leaks, and moisture, aerial thermography is valid for estimating thermal transmittance (U-value) of enclosures. The aim of this research is to evaluate this method to calculate the U-value of the façade of an university building located in a Subtropical Mediterranean climate, during a typical full day in different seasons of the year. Concurrently with aerial thermography, the thermometric method (THM) was applied to compare results and refine the operational conditions during U-value measurement using different statistical indices and uncertainty analysis. The results demonstrated the validation of airborne thermography as a method for U-value calculation under specific meteorological and operational conditions, overcoming the common physical limitations of traditional building audit methods.

Modeling movements improves capture–recapture estimates for mobile species with sparse data: Polar bears (Ursus maritimus) in Viscount Melville sound

AbstractWildlife management requires estimates of demographic parameters that are difficult to obtain for mobile species at low densities. Biased parameter estimates often result from capture–recapture (CR) studies due to small sample sizes and unequal recapture probabilities, the latter of which can be caused by animal movements with respect to the sampling area. We developed a multistate CR model designed to minimize biases by including multiple data types (capture, harvest, natural mortality, and telemetry) and accounting for temporary emigration. We applied the model to data collected intensively from 2012 to 2014, and intermittently since the 1970s, for the Viscount Melville (VM) subpopulation of polar bears (Ursus maritimus) in the Canadian Arctic. The number of bears within the VM subpopulation boundary likely increased from an average of 145 (Bayesian 95% credible interval [CRI] [109, 221]) in 1989–1992 to 235 (95% CRI [148, 569]) in 2012–2014. Survival probability increased for all sex and age classes except adult females, for which estimates declined due to unknown reasons. Polar bear movements exhibited Markovian dependence with approximately 28% of the subpopulation located outside of the sampling area each spring. This contributed to inaccurate parameter estimates when using a simpler, single‐state CR model that only included capture data. Although the interpretation of demographic status was complicated by statistical uncertainty and changes in study design, our findings suggest that—as of 2014—the VM polar bear subpopulation had likely recovered from an earlier period of overharvest, was stable, and had not exhibited detectable negative effects of climate warming.