arXiv subjects

Amir Shahmoradi

Publications and source records attributed to Amir Shahmoradi.

18 recordsLinked to original sources

arXiv2024

Associations between exposure to OPEs and rheumatoid arthritis risk among adults in NHANES, 2011-2018

Rheumatoid arthritis (RA) has an intricate etiology that includes environmental factors as well as genetics. Organophosphate esters (OPEs) are frequently used as chemical additives in many personal care products and household items. However, there has been limited research on their potential effects on rheumatoid arthritis (RA). The specific associations between OPEs and RA remain largely unexplored. This study investigates any potential associations between adult rheumatoid arthritis risk and exposure to OPEs. We investigated data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 among participants over 20 years old. In two models, multivariable logistic regression was utilized to investigate the relationship between exposure to OPEs and RA. Furthermore, subgroup analyses stratified by age, gender, and dose exposure response were evaluated. Generalized additive models and smooth curve fits were used to characterize the nonlinear relationship between RA and OPEs. In conclusion, 5490 individuals (RA: 319, Non-RA: 5171) were analyzed. Higher quantiles (Q4) of DPHP and DBUP showed a higher prevalence of RA than the lowest quantiles. Our findings show that adult RA prevalence is higher in those who have been exposed to OPEs (DPHP, DBUP). These correlations seem to be stronger among women, the elderly, those with higher BMIs, and those who have diabetes. The dose-response curve for DPHP and DBUP demonstrated an upward-sloping trend. In contrast, BCEP and BCPP showed a U-shaped relationship and an inverted U-shaped relationship with the probability of RA. BDCPP demonstrates a complex relationship with a peak at lower concentrations followed by a decrease. Our study concludes that exposure to OPEs plays a crucial role in the pathogenesis of RA.

q-bio.QM↗

arXiv2024

Infrared-Radio-follow-up Observations for Detection of the Magnetic Radio Emission of Extra-Solar Planets: A New Window to Detect Exoplanets

There are several methods for indirectly detecting exoplanets, such as transit, radial velocity, astrometry, and the conventional gravitational microlensing approach. These methods rely on observing the effects of exoplanets on the emission or motion of observed stars. All these techniques have focused on the optical or infrared domains. However, an alternative method for exoplanet detection via microlensing events involves planets orbiting the source star, creating a binary source system. In this study, we explore a novel approach to detecting and studying exoplanets exclusively through their radio emissions resulting from magnetospheric processes. We propose utilizing the Roman telescope as a survey observer to detect microlensing events. Subsequently, we investigate the potential for detecting planetary radio signals through follow-up observations of these microlensing events in the radio band using the SKA telescope. This method is viable due to the comparable radio emission levels of exoplanets and their parent stars, unlike optical and infrared emissions. We conduct a Monte Carlo simulation to replicate the observations by the Nancy Roman Telescope, followed by a follow-up observation in radio frequencies using the SKA telescope. We determine that approximately 1317 exoplanets exhibit detectable signals by the SKA telescope during the 7-season observations by the Nancy Roman Telescope. This result indicates that such a method cannot only facilitate the direct detection of exoplanets but also enable the measurement of their magnetic field strength through analysis of their radio emissions.

astro-ph.EP↗

arXiv2024

An alternative statistical interpretation for the apparent plateaus in the duration distributions of GRBs

The existence of a plateau in the short-duration tail of the observed distribution of cosmological Long-soft Gamma Ray Bursts (LGRBs) has been argued as the first direct evidence of Collapsars. A similar plateau in the short-duration tail of the observed duration distribution of Short-hard Gamma Ray Bursts (SGRBs) has been suggested as evidence of compact binary mergers. We present an equally plausible alternative interpretation for this evidence, which is purely statistical. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs can similarly result from a combination of sample incompleteness and inhomogeneous binning of data. The observed plateau in the short-duration tail of the observed distributions of SGRBs can similarly result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of Gamma-Ray Bursts (GRBs) through extensive numerical Monte Carlo simulations. Our presented analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs without invoking the physics of Collapsars and jet-propagation through the stellar envelope.

astro-ph.HE↗

arXiv2020

Are there radio-loud and radio-quiet Gamma-Ray Bursts?

The potential existence of two separate classes of Long-duration Gamma-Ray Bursts (LGRBs) with and without radio afterglow emission, corresponding to radio-bright/loud and radio-dark/quiet populations, has been recently argued and favored in the GRB literature. The radio-quiet LGRBs have been found to have, on average, lower total isotropic gamma-ray emissions (Eiso) and shorter intrinsic prompt gamma-ray duration (e.g., T90z). In addition, a redshift-T90z anti-correlation has been discovered among the radio-loud LGRBs, which is reportedly missing in the radio-quiet class. Here we discuss the significance of the differences between the energetics and temporal properties of the two proposed classes of radio-loud and radio-quiet LGRBs. We show that much of the proposed evidence in support of the two distinct radio populations of LGRBs can be explained away in terms of selection effects and sample incompleteness. Our arguments are based on the recent discovery of the relatively-strong highly-significant positive correlation between the total isotropic emission (Eiso) and the intrinsic prompt duration (T90z) that is present in both populations of short-hard and long-soft GRBs, predicted, quantified, and reported for the first time by Shahmoradi (2013) and Shahmoradi and Nemiroff (2015).

astro-ph.HE↗

arXiv2020

MatDRAM: A pure-MATLAB Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler

Markov Chain Monte Carlo (MCMC) algorithms are widely used for stochastic optimization, sampling, and integration of mathematical objective functions, in particular, in the context of Bayesian inverse problems and parameter estimation. For decades, the algorithm of choice in MCMC simulations has been the Metropolis-Hastings (MH) algorithm. An advancement over the traditional MH-MCMC sampler is the Delayed-Rejection Adaptive Metropolis (DRAM). In this paper, we present MatDRAM, a stochastic optimization, sampling, and Monte Carlo integration toolbox in MATLAB which implements a variant of the DRAM algorithm for exploring the mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference. The design goals of MatDRAM include nearly-full automation of MCMC simulations, user-friendliness, fully-deterministic reproducibility, and the restart functionality of simulations. We also discuss the implementation details of a technique to automatically monitor and ensure the diminishing adaptation of the proposal distribution of the DRAM algorithm and a method of efficiently storing the resulting simulated Markov chains. The MatDRAM library is open-source, MIT-licensed, and permanently located and maintained as part of the ParaMonte library at https://github.com/cdslaborg/paramonte.

physics.data-an↗

arXiv2020

How unbiased statistical methods lead to biased scientific discoveries: A case study of the Efron-Petrosian statistic applied to the luminosity-redshift evolution of Gamma-Ray Bursts

Statistical methods are frequently built upon assumptions that limit their applicability to certain problems and conditions. Failure to recognize these limitations can lead to conclusions that may be inaccurate or biased. An example of such methods is the non-parametric Efron-Petrosian test statistic used in the studies of truncated data. We argue and show how the inappropriate use of this statistical method can lead to biased conclusions when the assumptions under which the method is valid do not hold. We do so by reinvestigating the evidence recently provided by multiple independent reports on the evolution of the luminosity/energetics distribution of cosmological Long-duration Gamma-Ray Bursts (LGRBs) with redshift. We show that the effects of detection threshold has been likely significantly underestimated in the majority of previous studies. This underestimation of detection threshold leads to severely-incomplete LGRB samples that exhibit strong apparent luminosity-redshift or energetics-redshift correlations. We further confirm our findings by performing extensive Monte Carlo simulations of the cosmic rates and the luminosity/energy distributions of LGRBs and their detection process.

astro-ph.HE↗

arXiv2020

Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library

ParaMonte::Python (standing for Parallel Monte Carlo in Python) is a serial and MPI-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for sampling mathematical objective functions, in particular, the posterior distributions of parameters in Bayesian modeling and analysis in data science, Machine Learning, and scientific inference in general. In addition to providing access to fast high-performance serial/parallel Monte Carlo and MCMC sampling routines, the ParaMonte::Python library provides extensive post-processing and visualization tools that aim to automate and streamline the process of model calibration and uncertainty quantification in Bayesian data analysis. Furthermore, the automatically-enabled restart functionality of ParaMonte::Python samplers ensure seamless fully-deterministic into-the-future restart of Monte Carlo simulations, should any interruptions happen. The ParaMonte::Python library is MIT-licensed and is permanently maintained on GitHub at https://github.com/cdslaborg/paramonte/tree/master/src/interface/Python.

cs.MS↗

arXiv2020

ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran

ParaMonte (standing for Parallel Monte Carlo) is a serial and MPI/Coarray-parallelized library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference. The ParaMonte library has been developed with the design goal of unifying the **automation**, **accessibility**, **high-performance**, **scalability**, and **reproducibility** of Monte Carlo simulations. The current implementation of the library includes **ParaDRAM**, a **Para**llel **D**elyaed-**R**ejection **A**daptive **M**etropolis Markov Chain Monte Carlo sampler, accessible from a wide range of programming languages including C, C++, Fortran, with a unified Application Programming Interface and simulation environment across all supported programming languages. The ParaMonte library is MIT-licensed and is permanently located and maintained at [https://github.com/cdslaborg/paramonte](https://github.com/cdslaborg/paramonte).

cs.MS↗

arXiv2020

ParaDRAM: A Cross-Language Toolbox for Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo Simulations

We present ParaDRAM, a high-performance Parallel Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo software for optimization, sampling, and integration of mathematical objective functions encountered in scientific inference. ParaDRAM is currently accessible from several popular programming languages including C/C++, Fortran, MATLAB, Python and is part of the ParaMonte open-source project with the following principal design goals: 1. full automation of Monte Carlo simulations, 2. interoperability of the core library with as many programming languages as possible, thus, providing a unified Application Programming Interface and Monte Carlo simulation environment across all programming languages, 3. high-performance 4. parallelizability and scalability of simulations from personal laptops to supercomputers, 5. virtually zero-dependence on external libraries, 6. fully-deterministic reproducibility of simulations, 7. automatic comprehensive reporting and post-processing of the simulation results. We present and discuss several novel techniques implemented in ParaDRAM to automatically and dynamically ensure the good-mixing and the diminishing-adaptation of the resulting pseudo-Markov chains from ParaDRAM. We also discuss the implementation of an efficient data storage method used in ParaDRAM that reduces the average memory and storage requirements of the algorithm by, a factor of 4 for simple simulation problems, to an order of magnitude and more for sampling complex high-dimensional mathematical objective functions. Finally, we discuss how the design goals of ParaDRAM can help users readily and efficiently solve a variety of machine learning and scientific inference problems on a wide range of computing platforms.

cs.CE↗

arXiv2020

A Multilevel Empirical Bayesian Approach to Estimating the Unknown Redshifts of 1366 BATSE Catalog Long-Duration Gamma-Ray Bursts

We present a catalog of the probabilistic redshift estimates and for 1366 individual Long-duration Gamma-Ray Bursts (LGRBs) detected by the Burst And Transient Source Experiment (BATSE). This result is based on a careful selection and modeling of the population distribution of 1366 BATSE LGRBs in the 5-dimensional space of redshift and the four intrinsic prompt gamma-ray emission properties: the isotropic 1024ms peak luminosity, the total isotropic emission, the spectral peak energy, as well as the intrinsic duration, while carefully taking into account the effects of sample incompleteness and the LGRB-detection mechanism of BATSE. Two fundamental plausible assumptions underlie our purely-probabilistic approach: 1. LGRBs trace, either exactly or closely, the Cosmic Star Formation Rate and 2. the joint 4-dimensional distribution of the aforementioned prompt gamma-ray emission properties is well-described by a multivariate log-normal distribution. Our modeling approach enables us to constrain the redshifts of individual BATSE LGRBs to within $0.36$ and $0.96$ average uncertainty ranges at $50\%$ and $90\%$ confidence levels, respectively. Our redshift predictions are completely at odds with the previous redshift estimates of BATSE LGRBs that were computed via the proposed phenomenological high-energy relations, specifically, the apparently-strong correlation of LGRBs' peak luminosity with the spectral peak energy, lightcurve variability, and the spectral lag. The observed discrepancies between our predictions and the previous works can be explained by the strong influence of detector threshold and sample-incompleteness in shaping these phenomenologically-proposed high-energy correlations in the literature.

astro-ph.HE↗

arXiv2019

A Catalog of Redshift Estimates for 1366 BATSE Long-Duration Gamma-Ray Bursts: Evidence for Strong Selection Effects on the Phenomenological Prompt Gamma-Ray Correlations

We present a catalog of the redshift estimates and probability distributions for 1366 individual Long-duration Gamma-Ray Bursts (LGRBs) detected by the Burst And Transient Source Experiment (BATSE). This result is based on a careful classification and modeling of the population distribution of BATSE LGRBs in the 5-dimensional space of redshift as well as intrinsic prompt gamma-ray emission properties: peak luminosity, total isotropic emission, the spectral peak energy, and the intrinsic duration, while taking into account the detection mechanism of BATSE and sample incompleteness. The underlying assumption in our modeling approach is that LGRBs trace the Cosmic Star Formation Rate and that the joint 4-dimensional distribution of the aforementioned prompt gamma-ray emission properties follows a multivariate log-normal distribution. Our modeling approach enables us to constrain the redshifts of BATSE LGRBs to average uncertainty ranges of $0.7$ and $1.7$ at $50\%$ and $90\%$ confidence levels, respectively. Our predictions are almost entirely at odds with the previous estimates of BATSE redshifts based on the phenomenological high-energy correlations, in particular with the estimates derived from the lag-luminosity and the variability-luminosity relations. There is, however, a weak but significant correlation of strength $\sim0.26$ between our predicted redshift estimates and those derived from the hardness-brightness relations. The discrepancies between the estimates can be explained by the strong influence of sample incompleteness in shaping the phenomenologically proposed high-energy correlations in the literature. The presented catalog here can be useful for demographic studies of LGRBs and studies of individual BATSE events.

astro-ph.HE↗

arXiv2017

Multilevel Bayesian Parameter Estimation in the Presence of Model Inadequacy and Data Uncertainty

Model inadequacy and measurement uncertainty are two of the most confounding aspects of inference and prediction in quantitative sciences. The process of scientific inference (the inverse problem) and prediction (the forward problem) involve multiple steps of data analysis, hypothesis formation, model construction, parameter estimation, model validation, and finally, the prediction of the quantity of interest. This article seeks to clarify the concepts of model inadequacy and bias, measurement uncertainty, and the two traditional classes of uncertainty: aleatoric versus epistemic, as well as their relationships with each other in the process of scientific inference. Starting from basic principles of probability, we build and explain a hierarchical Bayesian framework to quantitatively deal with model inadequacy and noise in data. The methodology can be readily applied to many common inference and prediction problems in science, engineering, and statistics.

physics.data-an↗

arXiv2017

Dissecting the roles of local packing density and longer-range effects in protein sequence evolution

What are the structural determinants of protein sequence evolution? A number of site-specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site-specific sequence variability in proteins. We show here that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as the contact number and the weighted contact number, represent by definition the combined effects of local packing density and longer-range effects. As an alternative, we here propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino-acid side chains, behaves nearly identically to the relative solvent accessibility, and both can explain, on average, approximately 34\% of the site-specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10\% of variation can be explained by non-local effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined action of the immediate amino-acid neighbors of that site and of effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize the relative importance of immediate contacts and longer-range effects on evolutionary variation.

physics.bio-ph↗

arXiv2015

Short vs. Long Gamma-Ray Bursts: A Comprehensive Study of Energetics and Prompt Gamma-Ray Correlations

We present the results of a comprehensive study of the luminosity function, energetics, prompt gamma-ray correlations, and classification methodology of short-hard and long-soft GRBs (SGRBs and LGRBs), based on observational data in the largest catalog of GRBs available to this date: BATSE catalog of 2702 GRBs. We find that: 1. The least-biased classification method of GRBs into short and long, solely based on prompt-emission properties, appears to be the ratio of the observed spectral peak energy to the observed duration ($R=E_p/T_{90}$) with the dividing line at $R\simeq50[keV~s^{-1}]$. 2. Once data is carefully corrected for the effects of the detection threshold of gamma-ray instruments, the population distribution of SGRBs and LGRBs can be individually well described as multivariate log-normal distribution in the $4$--dimensional space of the isotropic peak gamma-ray luminosity, total isotropic gamma-ray emission, the intrinsic spectral peak energy, and the intrinsic duration. 3. Relatively large fractions of SGRBs and LGRBs with moderate-to-low spectral peak energies have been missed by BATSE detectors. 4. Relatively strong and highly significant intrinsic hardness--brightness and duration--brightness correlations likely exist in both populations of SGRBs and LGRBs, once data is corrected for selection effects. The strengths of these correlations are very similar in both populations, implying similar mechanisms at work in both GRB classes, leading to the emergence of these prompt gamma-ray correlations.

astro-ph.HE↗

arXiv2014

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

physics.bio-ph↗

arXiv2013

Gamma-Ray bursts: Energetics and Prompt Correlations

A model is presented here that is capable of simultaneously describing the luminosity function and the underlying joint population distribution of the prompt spectral and temporal parameters of Gamma-Ray Bursts (GRBs), subject to the detection threshold of gamma-ray instruments--in particular, BATSE and Fermi. Based on 2130 GRB prompt emission data in the BATSE catalog, I show that the population properties of the two classes of GRBs -- Long and Short durations -- bear striking similarities in the 4-dimensional space of prompt parameters: peak luminosity (Liso), total isotropic emission (Eiso), time-integrated spectral peak energy (Ep,z) and the prompt duration (T90,z). The two well-known Amati (Eiso-Ep,z) and Yonetoku (Liso-Ep,z) relations are shown to be highly affected by selection effects, undermining the legitimacy of their frequent uses in the studies of Dark Energy's equation of state and different cosmological models. In particular, I show that the slope of the Amati relation is likely in the range 0.2-0.3, corresponding to a Pearson's correlation strength of rho=0.58\pm0.04. This predicted slope is significantly (>16sigma) less than the value currently perceived by the GRB community (~0.56). I argue that similar Eiso-Ep,z and Liso-Ep,z relations with approximately same strength and significance should exist in the population of Short GRBs. Also predicted by the model is the strong positive correlation of the prompt emission duration (e.g., T90,z) with Eiso and Liso in both classes of Short and Long GRBs.

astro-ph.HE↗

arXiv2013

A Multivariate Fit Luminosity Function and World Model for Long GRBs

It is proposed that the luminosity function, the rest-frame spectral correlations and distributions of cosmological Long-duration (Type-II) Gamma-Ray Bursts (LGRBs) may be very well described as multivariate log-normal distribution. This result is based on careful selection, analysis and modeling of LGRBs' temporal and spectral variables in the largest catalog of Gamma-Ray Bursts available to date: 2130 BATSE GRBs, while taking into account the detection threshold and possible selection effects. Constraints on the joint rest-frame distribution of the isotropic peak luminosity (Liso), total isotropic emission (Eiso), the time-integrated spectral peak energy (Epkz) and duration (T90z) of LGRBs are derived. The presented analysis provides evidence for a relatively large fraction of LGRBs that have been missed by BATSE detector with Eiso extending down to ~ 10^49 [erg] and observed spectral peak energies (Epk) as low as ~ 5 [keV]. LGRBs with rest-frame duration T90z < 1 [s] or observer-frame duration T90 < 2 [s] appear to be rare events (<0.1% chance of occurrence). The model predicts a fairly strong but highly significant correlation (rho=0.58 \pm 0.04) between Eiso & Epkz of LGRBs. Also predicted are strong correlations of Liso & Eiso with T90z and moderate correlation between Liso & Epkz. The strength and significance of the correlations found, encourage the search for underlying mechanisms, though undermine their capabilities as probes of Dark Energy's equation of state at high redshifts. The presented analysis favors -- but does not necessitate -- a cosmic rate for BATSE LGRBs tracing metallicity evolution consistent with a cutoff ~ 0.2-0.5, assuming no luminosity-redshift evolution.

astro-ph.HE↗

arXiv2010

Hardness as a Spectral Peak Estimator for Gamma-Ray Bursts

Simple hardness ratios are found to be a good estimator for the spectral peak energy in Gamma-Ray Bursts (GRBs). Specifically, a high correlation strength is found between the $νF_ν$ peak in the spectrum of BATSE GRBs, $\epo$, and the hardness of GRBs, $\hr$, as defined by the fluences in channels 3 and 4, divided by the combined fluences in channels 1 and 2 of the BATSE Large Area Detectors. The correlation is independent of the type of the burst, whether Long-duration GRB (LGRB) or Short-duration (SGRB) and remains almost linear over the wide range of the BATSE energy window (20-2000 KeV). Based on Bayes theorem and Markov Chain Monte Carlo techniques, we also present multivariate analyses of the observational data while accounting for data truncation and sample-incompleteness. Prediction intervals for the proposed \hrep ~relation are derived. Results and further simulations are used to compute $\epo$ estimates for nearly the entire BATSE catalog: 2130 GRBs. These results may be useful for investigating the cosmological utility of the spectral peak in GRBs intrinsic luminosity estimates.

astro-ph.HE↗