Differential coupling of gibberellin responses by Rht-B1c suppressor alleles and Rht-B1b in wheat highlights a unique role for the DELLA N-terminus in dormancy

During the Green Revolution, substantial increases in wheat (Triticum aestivum) yields were realized, at least in part, through the introduction of the Reduced height (Rht)-B1b and Rht-D1b semi-dwarfing alleles. In contrast to Rht-B1b and Rht-D1b, the Rht-B1c allele is characterized by extreme dwarfism and exceptionally strong dormancy. Recently, 35 intragenic Rht-B1c suppressor alleles were created in the spring wheat cultivar Maringa, and termed overgrowth (ovg) alleles. Here, 14 ovg alleles with agronomically relevant plant heights were reproducibly classified into nine tall and five semi-dwarf alleles. These alleles differentially affected grain dormancy, internode elongation rate, and coleoptile and leaf lengths. The stability of these ovg effects was demonstrated for three ovg alleles in different genetic backgrounds and environments. Importantly, two semi-dwarf ovg alleles increased dormancy, which correlated with improved pre-harvest sprouting (PHS) resistance. Since no negative effects on grain yield or quality were observed, these semi-dwarf ovg alleles are valuable for breeding to achieve adequate height reduction and protection of grain quality in regions prone to PHS. Furthermore, this research highlights a unique role for the first 70 amino acids of the DELLA protein, encoded by the Rht-1 genes, in grain dormancy

N-terminal truncated RHT-1 proteins generated by translational reinitiation cause semi-dwarfing of wheat Green Revolution alleles

The unprecedented wheat yield increases during the Green Revolution were achieved through the introduction of the Reduced height (Rht)-B1b and Rht-D1b semi-dwarfing alleles. These Rht-1 alleles encode growth-repressing DELLA genes containing a stop codon within their open reading frame that confers gibberellin (GA)-insensitive semi-dwarfism. In this study, we successfully took the hurdle of detecting wild-type RHT-1 proteins in different wheat organs and confirmed their degradation in response to GAs. We further demonstrated that Rht-B1b and Rht-D1b produce N-terminal truncated proteins through translational reinitiation. Expression of these N-terminal truncated proteins in transgenic lines and in Rht-D1c, an allele containing multiple Rht-D1b copies, demonstrated their ability to cause strong dwarfism, resulting from their insensitivity to GA-mediated degradation. N-terminal truncated proteins were detected in spikes and nodes, but not in the aleurone layers. Since Rht-B1b and Rht-D1b alleles cause dwarfism but have wild-type dormancy, this finding suggests that tissue-specific differences in translational reinitiation may explain why the Rht-1 alleles reduce plant height without affecting dormancy. Taken together, our findings not only reveal the molecular mechanism underlying the Green Revolution but also demonstrate that translational reinitiation in the main open reading frame occurs in plants

Covariates of intravenous paracetamol pharmacokinetics in adults

Peer reviewe

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Neonatal clinical pharmacology

Effective and safe drug administration in neonates should be based on integrated knowledge on the evolving physiological characteristics of the infant who will receive the drug and the pharmacokinetics (PK) and pharmacodynamics (PD) of a given drug. Consequently, clinical pharmacology in neonates is as dynamic and diverse as the neonates we admit to our units while covariates explaining the variability are at least as relevant as median estimates. The unique setting of neonatal clinical pharmacology will be highlighted based on the hazards of simple extrapolation of maturational drug clearance when only based on 'adult' metabolism (propofol, paracetamol). Second, maturational trends are not at the same pace for all maturational processes. This will be illustrated based on the differences between hepatic and renal maturation (tramadol, morphine, midazolam). Finally, pharmacogenetics should be tailored to neonates, not just mirror adult concepts. Because of this diversity, clinical research in the field of neonatal clinical pharmacology is urgently needed and facilitated through PK/PD modeling. In addition, irrespective of already available data to guide pharmacotherapy, pharmacovigilance is needed to recognize specific side effects. Consequently, pediatric anesthesiologists should consider to contribute to improved pharmacotherapy through clinical trial design and collaboration, as well as reporting on adverse effects of specific drugs.status: publishe

Thin Layer Activation (TLA) leading to better insights in the fretting wear process of steel surfaces. Proc. Symp. Comp. and Exp. Methods Mech. Eng., Gent (Belgium), pp. 283 - 288

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