Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Accurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. Methods: To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. Findings: During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. Interpretation: Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. Funding: Bill & Melinda Gates Foundation

Thermodynamic reassessment of the Mn-Ni-Si system

Iased on the new experimental data available in the literature, the Mn-Ni-Si system has been reassessed using the CALPHAD (CALculation of PHAse Diagram) approach. Compared with the previous modeling, the τ8 and τ12 ternary phases were treated as the same phase according to the new experimental data. The Mn3Si phase was described with two sublattice model (Mn, Ni)3(Si)1. The reported new ternary phase τ was not considered in the present work. Comprehensive comparisons between the calculated and measured phase diagrams showed that a set of thermodynamic parameters of the Mn-Ni-Si system obtained in this work was more accurate than the previous one

Muscleblind localizes to nuclear foci of aberrant RNA in myotonic dystrophy types 1 and 2

The phenotypes in myotonic dystrophy types 1 and 2 (DM1 and DM2) are similar, suggesting a shared pathophysiologic mechanism. DM1 is caused by expansion of a CTG repeat in the DMPK gene. Pathogenic effects of this mutation are likely to be mediated, at least in part, by the expanded CUG repeat in mutant mRNA. The mutant transcripts are retained in the nucleus in multiple discrete foci. We investigated the possibility that DM2 is also caused by expansion of a CTG repeat or related sequence. Analysis of DNA by repeat expansion detection methods, and RNA by ribonuclease protection, did not show an expanded CTG or CUG repeat in DM2. However, hybridization of muscle sections with fluorescence-labeled CAG-repeat oligonucleotides showed nuclear foci in DM2 similar to those seen in DM1. Nuclear foci were present in all patients with symptomatic DM1 (n = 9) or DM2 (n = 9) but not in any disease controls or healthy subjects (n = 23). The foci were not seen with CUG- or GUC-repeat probes. Foci in DM2 were distinguished from DM1 by lower stability of the probe-target duplex, suggesting that a sequence related to the DM1 CUG expansion accumulates in the DM2 nucleus. Muscleblind proteins, which interact with expanded CUG repeats in vitro, localized to the nuclear foci in both DM1 and DM2. These results support the idea that nuclear accumulation of mutant RNA is pathogenic in DM1, suggest that a similar disease process occurs in DM2, and point to a role for muscleblind in the pathogenesis of both disorders

Simulation of EAST quasi-snowflake discharge by tokamak simulation code

Both theory and experiment have proved Snowflake configuration could reduce the heat loads on divertor plate. Due to limitation of PF coils, EAST could only operate with quasi-snowflake (QSF). In 2014 EAST campaign, QSF has been achieved by RZIp control. The next important task is the QSF shape control. As tokamak discharge simulation code, Tokamak Simulation Code (TSC), which has been benchmarked by experimental data, is used to simulate EAST QSF discharge. Singular Value Decomposition (SVD) method, a way to decouple the PF current and control parameter, is implemented in TSC code to simulate the course of QSF shape control. The simulation results show SVD method is a good way for EAST QSF shape control

The first achievement of the double feedback control of the detachment in the long-pulse plasma on EAST

EAST implements the double feedback control experiments of the detachment by using two control systems at the same time to explore the new detachment control way. We hire two PID controllers and two gas puffing valves dividedly to inject impurity gases from different locations, increasing the radiated power of the bulk plasma and reducing the divertor electron temperature (Te,t) simultaneously. This control method is applied in the EAST long-pulse H-mode plasma, the controlled variables of the radiative feedback control is the local radiative intensity and the total radiated power respectively. In the experiments, the longest double control duration has been up to 13.5 s, the divertor heat flux is reduced significantly in the control duration, while the particle flux has an overall drop at the end of the control phase. The chosen impurity species in the control system are Neon (Ne) and Argon (Ar): Ne is injected from the upper divertor to raise the core radiated power, and the Ar is injected from the lower divertor to reduce the Te,t beneath 8 eV. However, the Ar injection is not only decreasing the Te,t, but also generates an unfavorable increment of the core radiated power. This increment makes the bulk radiated power is higher than the target value, the radiative feedback control thus is paused for a long time. This mechanism results in the double feedback control back to the sequential implementations of the two feedback controls, which does not accord with the original expection(the both controls run at the same time), and degrades the control precision. In the future, many mitigating ways will be tested to improve the control effect. We will also develop a dynamic response model of the impurity seeding to simulate the time evolution of the divertor heat load with two kinds of impurities. The modeling results will be used to optimize the double feedback control system to gain a better experimental results