5-hydroxymethyl-cytosine enrichment of non-committed cells is not a universal feature of vertebrate development

5-hydroxymethyl-cytosine (5-hmc) is a cytosine modification that is relatively abundant in mammalian pre-implantation embryos and embryonic stem cells (Esc) derived from mammalian blastocysts. Recent observations imply that both 5-hmc and Tet1/2/3 proteins, catalyzing the conversion of 5-methyl-cytosine to 5-hmc, may play an important role in self renewal and differentiation of Escs. here we assessed the distribution of 5-hmc in zebrafish and chick embryos and found that, unlike in mammals, 5-hmc is immunochemically undetectable in these systems before the onset of organogenesis. In addition, Tet1/2/3 transcripts are either low or undetectable at corresponding stages of zebrafish development. however, 5-hmc is enriched in later zebrafish and chick embryos and exhibits tissue-specific distribution in adult zebrafish. Our findings show that 5-hmc enrichment of non-committed cells is not a universal feature of vertebrate development and give insights both into evolution of embryonic pluripotency and the potential role of 5-hmc in its regulation

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19 : a study protocol

Introduction: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. Methods and analysis: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. Ethics and dissemination: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication

Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

Background: To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. Methods: A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity &lt;6/18, ≥3/60) and blindness (presenting visual acuity &lt;3/60). Estimates are age-standardized using the GBD standard population. Results: In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by −27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%). Conclusions: The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.</p

Global estimates on the number of people blind or visually impaired by cataract : a meta-analysis from 2000 to 2020

DATA AVAILABILITY : Data sources for the Global Vision Database are listed at the following weblink http://www.anglia.ac.uk/verigbd. Fully disaggregated data is not available publicly due to data sharing agreements with some principal investigators yet requests for summary data can be made to the corresponding author.CHANGE HISTORY 16 July 2024 : A Correction to this paper has been published: https://doi.org/10.1038/s41433-024-03161-7.BACKGROUND : To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. METHODS : A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity <6/18, ≥3/60) and blindness (presenting visual acuity <3/60). Estimates are age-standardized using the GBD standard population. RESULTS : In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by −27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%). CONCLUSIONS : The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.Brien Holden Vision Institute, Fondation Thea, Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation (LCIF), Sightsavers International, and University of Heidelberg. Open Access funding enabled and organized by CAUL and its Member Institutions.https://www.nature.com/eyehj2024School of Health Systems and Public Health (SHSPH)SDG-03:Good heatlh and well-bein

Bayesian model selection for nonlinear aeroelastic systems using wind-tunnel data

In this paper we present a Bayesian framework for parameter estimation and model selection for nonlinear aeroelastic oscillatory systems using their measured response. The framework is motivated by the fact that (1) the oscillations involve the complex nonlinear fluid-structure interaction whose numerical modeling poses both conceptual and computational challenges, (2) several plausible models fit the experimental data with reasonable accuracy, and (3) the model calibration involves estimating the joint probability density function (pdf) of high-dimensional (between 10 and 22) parameter vectors including the variance of model prediction error (modeling error). A four-stage process is followed. First, a candidate model set consisting of parameterized stochastic models is proposed based on the prior understanding of the physics. Second, the joint posterior pdf of the model parameters of each model is estimated using Bayesian inference which involves Markov Chain Monte Carlo (MCMC) sampling complemented by a nonlinear filter for state estimation. Owing to the dense observational data, the state estimation is performed by the extended Kalman filter (EKF). The unnormalized, high-dimensional, joint posterior pdf is sampled through an MCMC method, whereby a parallel version of single-block, adaptive proposal-based Metropolis-Hastings (MH) algorithm is employed to tune the proposal pdf. Third, using Bayes' theorem each model is weighted according to the evidence provided by the data in its favor. For single-block MCMC sampling, an estimate of the evidence is readily available through the Chib-Jeliazkov method using the posterior samples generated after the burn-in. Fourth, the

Bayesian model selection using automatic relevance determination for nonlinear dynamical systems

Bayesian model selection is augmented with automatic relevance determination (ARD) to perform model reduction of complex dynamical systems modelled by nonlinear, stochastic ordinary differential equations (ODE). Given noisy measurement data, a parametrically flexible model is envisioned to represent the dynamical system. A Bayesian model selection problem is posed to find the best model nested under the envisioned model. This model selection problem is transferred from the model space to hyper-parameter space by regularizing the parameter posterior space through a parametrized prior distribution called the ARD prior. The resulting joint prior pdf is the combination of parametrized ARD priors assigned to parameters whose relevance to the system dynamics is questionable and the known prior pdf for parameters whose relevance is known a priori. The hyper-parameter of each ARD prior explicitly represents the relevance of the corresponding model parameter. The hyper-parameters are estimated using the measurement data by performing evidence maximization or type-II maximum likelihood. Superfluous model parameters are switched off during evidence maximization by the corresponding ARD prior, forcing the model parameter to be irrelevant for prediction purposes. An efficient numerical implementation for evidence computation using Markov Chain Monte Carlo sampling of the parameter posterior distribution is presented for the case when the analytical evaluation of evidence is not possible. The ARD approach is validated with synthetic measurements generated from a nonlinear, unsteady aeroelastic oscillator consisting of a NACA0012 airfoil undergoing limit cycle oscillation. A set of intentionally flexible stochastic ODEs having different state-space formulation is proposed to model the synthetic data. ARD is used to obtain an optimal nested model corresponding to each proposed model. The optimal nested model with the maximum posterior model probability is chosen as the overall optimal model. ARD provides a flexible Bayesian platform to find the optimal nested model by eliminating the need to propose candidate nested models and its prior pdfs