Direct Estimation Research Articles

Genomic imprinting and environmental epigenetics: Their influence on sheep reproductive traits across parities.

This study aimed to explore the impact of genomic imprinting on the genetic variance of composite reproductive traits across three parities in Baluchi sheep. The traits analyzed included litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). We employed a univariate linear animal model for each trait, treating performance across parities as separate traits. Twenty-four animal models were assessed, incorporating direct additive genetic effects, maternal genetic effects, maternal permanent environmental effects, direct and maternal genetic covariances, as well as maternal and paternal imprinting. Model selection was based on Akaike's Information Criterion (AIC). Direct heritability estimates for the traits were generally low, ranging from 0.039 ± 0.017 for LMWLW to 0.085 ± 0.028 for TLWW. TLWB and TLWW exhibited higher heritability than LMWLB and LMWLW in their respective parities. In the best model (model 24), maternal imprinting heritability estimates from the first to third parity were 0.059 ± 0.016, 0.060 ± 0.013, and 0.085 ± 0.021 for TLWB, 0.075 ± 0.021, 0.068 ± 0.025, and 0.048 ± 0.016 for LMWLB, 0.051 ± 0.013, 0.065 ± 0.019, and 0.068 ± 0.020 for TLWW, and, 0.072 ± 0.012, 0.057 ± 0.018 and 0.054 ± 0.011 for LMWLW, respectively. Paternal imprinting heritability estimates were consistently lower than maternal imprinting estimates, with values across parities ranging from 0.001 ± 0.024 to 0.019 ± 0.032 for TLWB, 0.005 ± 0.022-0.010 ± 0.019 for LMWLB, 0.012 ± 0.05-0.017 ± 0.05 for TLWW and, 0.013 ± 0.01-0.016 ± 0.01 for LMWLW. In conclusion, imprinting effects should be included in breeding programs to increase the accuracy of genetic evaluation.

Epidemiology of dog bite and strike related hospital admissions in Scotland, 1997 to 2022.

To describe the epidemiology of dog-related injuries leading to hospital admission in Scotland and to estimate the direct health care costs. A retrospective, longitudinal analysis of Scottish hospital admissions coded under the W54 ICD-10 code 'bitten or struck by dog', 1997-2022. Hospital admissions episodes coded under W54 were extracted from the SMR1/01 dataset. Descriptive analyses were performed. Incidence rate ratios (IRR) were calculated using negative binomial distribution. Direct NHS health cost estimates were calculated. Incidence of dog bite and strike related admissions near tripled between 1997 and 2022, from 5.9 to 17.2 per 100,000 population with an annual IRR of 1.047 (P<0.001). Incidence has risen most steeply in adults, with all age groups over 15 years having an IRR between 1.040 and 1.065 (P<0.001). Incidence of overnight admissions doubled, and zero-day admissions increased by five times. Hospital admissions were 2.4 times higher in the most deprived quintile compared to the least deprived. Setting of bite was not consistently recorded, but young age groups were more likely to have the home setting recorded. Admissions related to dog bites or strikes resulted in an estimated £28.6 million of direct health care costs, from the admission alone, over the 25-year period. Dog bites and strikes can be devastating injuries. Unfortunately, over the last 25 years there has been a steep rise in incidence in Scotland, despite attempts at control through legislation. Further work to better understand the health and wider costs of these injuries, the epidemiology to inform preventative interventions, and improved co-ordination through inter-sectoral collaboration is recommended.

Unwrapping error and fading signal correction on multi-looked InSAR data

Multi-looking, aimed at reducing data size and improving the signal-to-noise ratio, is indispensable for large-scale InSAR data processing. However, the resulting “Fading Signal” caused by multi-looking breaks the phase consistency among triplet interferograms and introduces bias into the estimated displacements. This inconsistency challenges the assumption that only unwrapping errors are involved in triplet phase closure. Therefore, untangling phase unwrapping errors and fading signals from triplet phase closure is critical to achieving more precise InSAR measurements. To address this challenge, we propose a new method that mitigates phase unwrapping errors and fading signals. This new method consists of two key steps. The first step is triplet phase closure-based stacking, which allows for the direct estimation of fading signals in each interferogram. The second step is Basis Pursuit Denoising-based unwrapping error correction, which transforms unwrapping error correction into sparse signal recovery. Through these two procedures, the new method can be seamlessly integrated into the traditional InSAR workflow. Additionally, the estimated fading signal can be directly used to derive soil moisture as a by-product of our method. Experimental results on the San Francisco Bay area demonstrate that the new method reduces velocity estimation errors by approximately 9 %–19 %, effectively addressing phase unwrapping errors and fading signals. This performance outperforms both ILP and Lasso methods, which only account for unwrapping errors in the triplet closure. Additionally, the derived by-product, soil moisture, shows strong consistency with most external soil moisture products.

Lagrangian analysis of submesoscale flows from sparse data using Gaussian Process Regression for field reconstruction

Lagrangian analyses of oceanic flows provide insight into the various transport pathways in the ocean. This analysis typically relies on a dense set of trajectories that can be computed using high-resolution velocity fields, which are often not available during field experiments. Instruments like drifters and floats are often employed to overcome the limitations imposed by satellite- and radar-based velocity fields, to understand the transport pathways in the ocean. However, the sparsity in available drifter-trajectory data proves prohibitive to obtaining a comprehensive map of the Lagrangian characteristics of the underlying flow. To circumvent these issues, we use Gaussian Process Regression (GPR) to obtain velocity fields from sparse drifter data to generate synthetic trajectories and subsequently estimate two Lagrangian metrics, FTLE and dilation rate. A detailed error analysis is performed for drifter clusters deployed within various dynamical regions in the analytic Bickley jet system. The uncertainties in velocity reconstruction obtained from the GPR method, averaged along particle trajectories, locate Lagrangian confidence regions that are applicable both to synthetic trajectories and the dilation rate field. A sensitivity analysis reveals the role played by factors such as the spatial sampling density and temporal resolution of the drifter data, as well as the effect of position uncertainty as a result of GPS inaccuracy. The method is then applied to the drifter data from the Lagrangian Submesoscale Experiment in 2016 to locate convergent filaments. The results present a marked improvement over direct estimation of area-averaged dilation rates using drifter clusters.

Genetic analysis of growth traits in Salem Black goats under farm conditions.

In this study, data on pedigree, production traits for 19years (2002-2022) of Salem Black goat from the Mecheri Sheep Research Station, Tamil Nadu, India was used. Various growth traits viz., birth weight (BW), weaning weight (WW), 6-month weight (6W), 9-month weight (9W), 12-month weight (YW), pre- and post-weaning average daily gain (ADG) (3-6, 6-9, 9-12 and 3-12months) were analyzed. Univariate analysis was carried out after correcting for significant non-genetic factors. (Co)variance components for different production and functional traits were estimated by Restriction Maximum Likelihood method (REML) using WOMBAT. Heritability estimates for most of the growth traits were medium providing better scope for selection. Direct heritability estimates for BW, WW, 6W, 9W and YW, pre- and post-weaning ADG (3-12) were 0.177 ± 0.066, 0.094 ± 0.055, 0.111 ± 0.068, 0.123 ± 0.082, 0.181 ± 0.122, 0.118 ± 0.060 and 0.188 ± 0.126 respectively. Through bivariate analysis genetic and phenotypic correlations between growth traits were obtained. Positive, medium to high genetic correlation was observed in post-weaning body weight traits which would support multistage selection. Similar positive and medium to high genetic correlation between pre- and post-weaning ADG (3-6 and 3-12months) with post- weaning body weight traits would facilitate indirect selection. Breeding values (EBV) for BW, WW, 6W, 9W, YW, pre- and post-weaning ADG (3-12months) were estimated. High genetic variability was observed in weaning and post- weaning body weight traits. The findings of this study could further aid in the improvement of the breeding programme.

Neurobehavioral Symptoms Partially Mediate the Effects of Depression and PTSD on Participation for Veterans With Mild Traumatic Brain Injury: A Cross-Sectional Study.

To examine whether neurobehavioral symptoms mediate the relationship between comorbid mental health conditions (major depressive disorder [MDD] and/or posttraumatic stress disorder [PTSD]) and participation restriction among Veterans with mild traumatic brain injury (mTBI). Veterans Health Administration (VHA). National sample of Veterans with mTBI who received VHA outpatient care between 2012 and 2020. Secondary data analysis of VHA clinical data. We specified a latent variable path model to estimate relationships between: (1) comorbid mental health conditions and 3 latent indicators of neurobehavioral symptoms (vestibular-sensory; mood-behavioral; cognitive); (2) latent indicators of neurobehavioral symptoms and 2 latent indicators of participation restriction (social and community participation; productivity); and (3) comorbid mental health conditions and participation restriction. International Classification of Diseases codes, Neurobehavioral Symptom Inventory, and Mayo-Portland Adaptability Inventory Participation Index to measure mental health conditions, neurobehavioral symptoms, and participation restrictions, respectively. Indirect effect estimates indicated that comorbid MDD and/or PTSD was associated with greater social and community participation restrictions, as mediated by mood-behavioral (β=.22-.33; 99% CI 0.18-0.4; small to medium effect) and cognitive symptoms (β=.08-.13; 99% CI 0.05-0.18; small effect), and with greater productivity restrictions, as mediated by vestibular-sensory (β=.06-.11; 99% CI 0.04-0.15; small effect) and cognitive symptoms (β=.08-.13; 99% CI 0.05-0.18; small effect). Direct effect estimates indicated that comorbid MDD and/or PTSD was associated with greater challenges with both social and community participation (β=.19-.40; 99% CI 0.12-0.49; small to medium effect) and productivity (β=.08-.44; 99% CI-0.02 to 0.55; small to medium effect). Neurobehavioral symptoms partially mediated the impact of MDD and/or PTSD on participation restrictions among Veterans with mTBI. These findings advance the understanding of explanatory mechanisms underlying participation challenges among Veterans with comorbid mTBI and mental health challenges, thereby informing the development of tailored intervention strategies.

Size does not matter: direct estimations of mutation rates in baleen whales

Baleen whales were thought to have low mutation rates due to their size and long lifespan. By performing direct estimations of their mutation rates we found that despite being the largest and longest-living mammals, their mutation rates are similar to ours and other mammals. We also showed that it is possible to directly estimate mutation rates in wild populations in the absence of known pedigrees.

The alongshore tilt of mean dynamic topography and its implications for model validation and ocean monitoring

Abstract. Mean dynamic topography (MDT) plays an important role in the dynamics of shelf circulation. Coastal tide gauge observations in combination with the latest generation of geoid models are providing estimates of the alongshore tilt of MDT with unprecedented accuracy. Additionally, high-resolution ocean models are providing better representations of nearshore circulation and the associated tilt of MDT along their coastal boundaries. It has been shown that the newly available geodetic estimates can be used to validate model predictions of coastal MDT variability on global and basin scales. On smaller scales, however, there are significant variations in alongshore MDT that are of the same order of magnitude as the accuracy of the geoid models. In this study, we use a regional ocean model of the Gulf of Maine and Scotian Shelf (GoMSS) to demonstrate that the new observations of geodetically referenced coastal sea level can also provide valuable information for the validation of such high-resolution models. The predicted coastal MDT is in good agreement with coastal tide gauge observations referenced to the Canadian Gravimetric Geoid 2013 – Version A (CGG2013a), including a significant tilt of alongshore MDT along the coast of Nova Scotia. Using the validated GoMSS model and two idealized models, we show that this alongshore tilt of MDT can be interpreted in two complementary, and dynamically consistent, ways: in the coastal view, the tilt of MDT along the coast can provide a direct estimate of the average alongshore current. In the regional view, the tilt provides a measure of area-integrated nearshore circulation. This highlights the value of using geodetic MDT estimates for model validation and ocean monitoring.

Estimating soil strength using ultra high-resolution seismic data: A case study from a shallow water wind farm

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design due to their high foundation costs and complex integration into marine environments. This study explores the use of ultrahigh-resolution seismic (UHRS) data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We develop a convolutional neural network-based approach that leverages UHRS data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geologic settings. Our methodology involves preprocessing UHRS and cone penetration test data, interpreting and integrating geologic unit (GU) information, and applying repeated k-fold cross validation to evaluate model performance. The field data results demonstrate the model’s efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geologic interpretation in predicting soil strength and identify the dependency on valid data within each GU as a limitation.

Mixed effects models for extreme value index regression

Extreme value theory (EVT) provides an elegant mathematical tool for the statistical analysis of rare events. When data are collected from multiple population subgroups, because some subgroups may have less data available for extreme value analysis, a scientific interest of many researchers would be to improve the estimates obtained directly from each subgroup. To achieve this, we incorporate the mixed effects model (MEM) into the regression technique in EVT. In small area estimation, the MEM has attracted considerable attention as a primary tool for producing reliable estimates for subgroups with small sample sizes, i.e., “small areas.” The key idea of MEM is to incorporate information from all subgroups into a single model and to borrow strength from all subgroups to improve estimates for each subgroup. Using this property, in extreme value analysis, the MEM may contribute to reducing the bias and variance of the direct estimates from each subgroup. This prompts us to evaluate the effectiveness of the MEM for EVT through theoretical studies and numerical experiments, including its application to the risk assessment of a number of stocks in the cryptocurrency market.

Direct and Indirect Estimation of the Prevalence of Waterpipe and Cigarette Smoking Among Adults in Arak: An Approach Based on Network Scale-up Method

Direct and Indirect Estimation of the Prevalence of Waterpipe and Cigarette Smoking Among Adults in Arak: An Approach Based on Network Scale-up Method

Cost Analysis of Epilepsy Healthcare in Adults: A Direct Cost Estimate From a Colombian Perspective.

To estimate the direct healthcare costs related to outpatient care and hospital stays for adults with epilepsy in the context of the Colombian healthcare system. A cost analysis was conducted from a base case, which included direct medical costs related to diagnosis, follow-up, pharmacological and surgical treatment, and in-hospital care for status epilepticus. A Delphi panel was carried out to identify and quantify cost-generating events. The monetary valuation was estimated using official databases and tariff manuals available in Colombia. The cost distribution was obtained using the bootstrapping method. Two one-way deterministic sensitivity analyses were performed. The total annual cost of an adult patient with epilepsy without including hospital stays is US dollars $2416.31. If at least 1 intensive care unit stay (8-day average) is included to treat a patient with status epilepticus, the total annual cost increases to $61 567.72. The total cost of resective surgery is $14 894.44, and the vagus nerve stimulation implant costs $26 565.86. Epilepsy in adults represents a significant economic burden for the Colombian healthcare system, comparable to that of other upper-middle-income countries. The majority of expenditure is attributed to intensive care unit stays, less financial resources would be required if patients achieve better control of the disease.

A comparison of shrinkage estimators of the cosmological precision matrix

Abstract The determination of the covariance matrix and its inverse, the precision matrix, is critical in the statistical analysis of cosmological measurements. The covariance matrix is typically estimated with a limited number of simulations at great computational cost before inversion into the precision matrix; therefore, it can be ill-conditioned and overly noisy when the sample size n used for estimation is not much larger than the data vector dimension. In this work, we consider a class of methods known as shrinkage estimation for the precision matrix, which combines an empirical estimate with a target that is either analytical or stochastic. These methods include linear and non-linear shrinkage applied to the covariance matrix (the latter represented by the so-called NERCOME estimator), and the direct linear shrinkage estimation of the precision matrix which we introduce in a cosmological setting. By performing Bayesian parameter inference and using metrics like matrix loss functions, the Kullback–Leibler divergence and the eigenvalue spectrum, we compare their performance against the standard sample estimator with varying sample size n. We have found the shrinkage estimators to significantly improve the posterior distribution at low n, especially for the linear shrinkage estimators either inverted from the covariance matrix or applied directly to the precision matrix, with an empirical target constructed from the sample estimate. Our results are particularly relevant to the analyses of Stage-IV spectroscopic galaxy surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid, whose statistical power can be limited by the computational cost of obtaining an accurate precision matrix estimate.

Estimating the distribution of fitness effects in aye-ayes ( Daubentonia madagascariensis ), accounting for population history as well as mutation and recombination rate heterogeneity.

The distribution of fitness effects (DFE) characterizes the range of selection coefficients from which new mutations are sampled, and thus holds a fundamentally important role in evolutionary genomics. To date, DFE inference in primates has been largely restricted to haplorrhines, with limited data availability leaving the other suborder of primates, strepsirrhines, largely under-explored. To advance our understanding of the population genetics of this important taxonomic group, we here map exonic divergence in aye-ayes ( Daubentonia madagascariensis ) - the only extant member of the Daubentoniidae family of the Strepsirrhini suborder. We further infer the DFE in this highly-endangered species, utilizing a recently published high-quality annotated reference genome, a well-supported model of demographic history, as well as both direct and indirect estimates of underlying mutation and recombination rates. The inferred distribution is generally characterized by a greater proportion of deleterious mutations relative to humans, providing evidence of a larger long-term effective population size. In addition however, both immune-related and sensory-related genes were found to be amongst the most rapidly evolving in the aye-aye genome.

Ego-Motion Estimation for Autonomous Vehicles Based on Genetic Algorithms and CUDA Parallel Processing

Estimating ego-motion in autonomous vehicles is critical for tasks such as localization, navigation, obstacle avoidance, and so on. While traditional methods often rely on direct pose estimation or AI-based approaches, these can be computationally intensive, especially for small, incremental movements typically observed between consecutive frames. In this work, we propose a brute-force-based ego-motion estimation algorithm that takes advantage of the constraints of autonomous vehicles, which are assumed to have only three degrees of freedom (x, y, and yaw). Our approach is based on a genetic algorithm to efficiently explore potential vehicle movements. By generating an initial seed of random motion candidates and iteratively mutating and selecting the best-performing individuals, we minimize the cost function that measures image similarity between frames. Furthermore, we implement the algorithm using CUDA to exploit parallel processing, significantly improving computational speed. Experimental results demonstrate that our approach achieves accurate ego-motion estimation with high efficiency, making it suitable for real-time autonomous vehicle applications.

Direct Position Estimation (DPE): A Potential Application in Geodetic Networks

Direct Position Estimation (DPE) is a relatively new technique in the Global Navigation Satellite System (GNSS) that is used to estimate the user’s position, velocity, and time directly from the correlation values of the received GNSS signal with the internal replica signals of the receiver. Unlike the conventional two-step approach, DPE infers the position directly from the sampled data without intermediate steps by joining signal tracking, and the navigation technique directly compares the expected signal reception of multiple potential navigation candidates against the actual received signal. The theoretical results indicate that DPE-based GNSS receivers can achieve more robust localization than conventional two-step receivers. DPE localization algorithms that compute the navigation solution directly in the navigation domain have been proposed, providing ways to address the challenges of conventional two-step receivers at the expense of additional computational load. Despite its high computational load, DPE is a more robust positioning algorithm than conventional two-step receivers in terms of multipath mitigation. The resilience of DPE against multipath and non-line-of-sight can even potentially offer applications in geodetic networks, where robust estimators are traditionally employed to counteract outliers.

Multivariable MR Can Mitigate Bias in Two-Sample MR Using Covariable-Adjusted Summary Associations.

Genome-wide association studies (GWAS) are hypothesis-free studies that estimate the association between polymorphisms across the genome with a trait of interest. To increase power and to estimate the direct effects of these single-nucleotide polymorphisms (SNPs) on a trait GWAS are often conditioned on a covariate (such as body mass index or smoking status). This adjustment can introduce bias in the estimated effect of the SNP on the trait. Two-sample Mendelian randomisation (MR) studies use summary statistics from GWAS estimate the causal effect of a risk factor (or exposure) on an outcome. Covariate adjustment in GWAS can bias the effect estimates obtained from MR studies conducted using covariate adjusted GWAS data. Multivariable MR (MVMR) is an extension of MR that includes multiple traits as exposures. Here we propose the use of MVMR to correct the bias in MR studies from covariate adjustment.We show how MVMR can recover unbiased estimates of the direct effect of the exposure of interest by including the covariate used to adjust the GWAS within the analysis. We apply this method to estimate the effect of systolic blood pressure on type-2 diabetes and the effect of waist circumference on systolic blood pressure. Our analytical and simulation results show that MVMR provides unbiased effect estimates for the exposure when either the exposure or outcome of interest has been adjusted for a covariate. Our results also highlight the parameters that determine when MR will be biased by GWAS covariate adjustment. The results from the applied analysis mirror these results, with equivalent results seen in the MVMR with and without adjusted GWAS. When GWAS results have been adjusted for a covariate, biasing MR effect estimates, direct effect estimates of an exposure on an outcome can be obtained by including that covariate as an additional exposure in an MVMR estimation. However, the estimated effect of the covariate obtained from the MVMR estimation is biased.

Direct quantification of the plasmon dephasing time in ensembles of gold nanorods through two-dimensional electronic spectroscopy.

In this study, we used two-dimensional electronic spectroscopy to examine the early femtosecond dynamics of suspensions of colloidal gold nanorods with different aspect ratios. In all samples, the signal distribution in the 2D maps at this timescale shows a distinctive dispersive behavior, which can be explained by the interference between the exciting field and the field produced on the nanoparticle's surface by the collective motion of electrons when the plasmon is excited. Studying this interference effect, which is active only until the plasmon has been dephased, allows for a direct estimation of the dephasing time of the plasmon of an ensemble of colloidal particles. Our findings provide insight into the fundamental behavior of plasmonic states and highlight the potential of two-dimensional electronic spectroscopy in uncovering ultrafast and coherent optical phenomena at the nanoscale.

Use of GEDI Signal and Environmental Parameters to Improve Canopy Height Estimation over Tropical Forest Ecosystems in Mayotte Island

Canopy height is a fundamental parameter for describing forest ecosystems. GEDI is a spaceborne LiDAR system that was designed to measure vegetation’s vertical structure at a global scale. This study evaluates the accuracy of GEDI-derived canopy height estimates over complex tropical forests in Mayotte Island (Overseas France) characterized by moderate height and biomass levels as well as a relatively steep terrain. The influence of GEDI signal and environmental parameters (canopy height, beam sensitivity and slope) on height estimates was assessed. Linear as well as non-linear approaches were implemented using the GEDI L2A product to estimate canopy height. Empirical models were trained on reference data derived from airborne LiDAR scanning. The results showed that using regression models built on multiple GEDI metrics yielded improved accuracies compared to a direct estimation from a single GEDI height metric. Canopy height, beam sensitivity and terrain slope were found to have a significant impact on the height metrics derived from GEDI waveforms. Conversely, both linear and non-linear regression models produced unbiased and stable estimates.

Direct Estimation of Alcohol-Attributable Fractions for Suicide in the United States, 2021.

Objectives. To estimate the alcohol-attributable fraction (AAF) for suicide in the United States. Methods. Using restricted-access data from the National Violent Death Reporting System for 2021, we estimated the sex-specific AAF for suicide, among those 15 years of age and older, by sociodemographic characteristics and suicide means. An alcohol-attributable suicide was defined as that for which the decedent had a blood alcohol concentration of 0.10 grams per deciliter or higher. Results. In 2021, the AAF for suicide for males (20.2%) was significantly higher than that for females (17.8%; P < .001). The AAF for suicide was higher for both males and females who used a firearm as the means of suicide (23.4% and 22.8%, respectively) compared with their counterparts who used other means (16.5% and 15.9%, respectively). Conclusions. Despite some variation, AAFs for suicide were consistently high, with about 1 in 5 suicides being attributable to alcohol use. Therefore, suicide prevention initiatives in the United States should also target excessive alcohol use. (Am J Public Health. Published online ahead of print December 19, 2024:e1-e5. https://doi.org/10.2105/AJPH.2024.307910).