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Despite the great agricultural and ecological importance of efficient use of urea-containing nitrogen fertilizers by crops, molecular and physiological identities of urea transport in higher plants have been investigated only in Arabidopsis. We performed short-time urea-influx assays which have identified a low-affinity and high-affinity (Km of 7.55 mu M) transport system for urea-uptake by rice roots (Oryza sativa). A high-affinity urea transporter OsDUR3 from rice was functionally characterized here for the first time among crops. OsDUR3 encodes an integral membrane-protein with 721 amino acid residues and 15 predicted transmembrane domains. Heterologous expression demonstrated that OsDUR3 restored yeast dur3-mutant growth on urea and facilitated urea import with a Km of c. 10 mu M in Xenopus oocytes. Quantitative reverse-transcription polymerase chain reaction (qPCR) analysis revealed upregulation of OsDUR3 in rice roots under nitrogen-deficiency and urea-resupply after nitrogen-starvation. Importantly, overexpression of OsDUR3 complemented the Arabidopsis atdur3-1 mutant, improving growth on low urea and increasing root urea-uptake markedly. Together with its plasma membrane localization detected by green fluorescent protein (GFP)-tagging and with findings that disruption of OsDUR3 by T-DNA reduces rice growth on urea and urea uptake, we suggest that OsDUR3 is an active urea transporter that plays a significant role in effective urea acquisition and utilisation in rice.
A catalog of genetic loci associated with kidney function from analyses of a million individuals
(2019)
Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
Nonlinear data assimilation
(2015)
This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters.
The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
The Gaussian Graphical Model (GGM) is a popular tool for incorporating sparsity into joint multivariate distributions. The G-Wishart distribution, a conjugate prior for precision matrices satisfying general GGM constraints, has now been in existence for over a decade. However, due to the lack of a direct sampler, its use has been limited in hierarchical Bayesian contexts, relegating mixing over the class of GGMs mostly to situations involving standard Gaussian likelihoods. Recent work has developed methods that couple model and parameter moves, first through reversible jump methods and later by direct evaluation of conditional Bayes factors and subsequent resampling. Further, methods for avoiding prior normalizing constant calculations-a serious bottleneck and source of numerical instability-have been proposed. We review and clarify these developments and then propose a new methodology for GGM comparison that blends many recent themes. Theoretical developments and computational timing experiments reveal an algorithm that has limited computational demands and dramatically improves on computing times of existing methods. We conclude by developing a parsimonious multivariate stochastic volatility model that embeds GGM uncertainty in a larger hierarchical framework. The method is shown to be capable of adapting to swings in market volatility, offering improved calibration of predictive distributions.