Nitrogen supply from belowground residues of lentil and wheat to a subsequent wheat crop

Non-Peer ReviewedLentil (Lens culinaris) plants can form an association with rhizobia and thereby biologically fix much of the nitrogen (N) required for their growth. This not only reduces the need for expensive N fertilizer when the lentil crop is grown, but there is a potential to contribute a net increment of N to the soil that can be utilized by the subsequent crop. However, estimating this net increment of N remains a challenge, because of the difficulty in estimating the amount of root and root-derived N. The purpose of this greenhouse study was to quantify the belowground N (BGN) of lentil and wheat (Triticum aestivum) using shoot 15N labeling and to trace the 15N from BGN into subsequently grown wheat plants. Belowground N comprised 34 and 51 % of total plant N in lentil and wheat, respectively. Biomass production and N uptake by wheat grown on lentil belowground residues (BGR) were 49 and 14 % higher than wheat grown on wheat BGR. Moreover, a higher proportion of added 15N from lentil BGN was recovered in the succeeding wheat crop, indicating that lentil BGN was more readily mineralized than wheat BGN. The disproportionately high increase in yield vs. N uptake for wheat grown on lentil BGR, however, indicates that non-N factors also contributed to the increase in wheat yield. This study highlights the importance of including estimates of BGN when evaluating the positive effects of including lentil crops in rotation with cereals

Contribution of pulse crop residues and soil to N2O and CO2 emissions in a subsequent wheat crop: a 13C/15N study

Non-Peer Reviewe

Land use impacts of rapid transit implications of recent experience

The report seeks to display available evidence on the extent to which recent (post-World War II) major rapid transit improvements in the United States and Canada have influenced urban land use. From this compilation are derived several types of conclusions. The factors governing the size and nature of land use impacts of transit are determined; implications for appropriate Federal policy are drawn; and specific needs for related future research are identified. The report's intended use is as a resource for those involved in the planning and evaluation of possible improvements in urban transit systems. Document type: Repor

Vibrational pair cat states

Published versio

Making use of transcription factor enrichment to identify functional microRNA-regulons

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.Pacôme B. Prompsy, John Toubia, Linden J. Gearing, Randle L. Knight, Samuel C. Forster, Cameron P. Bracken, Michael P. Gantie

Molybdenum blue nano-rings: an effective catalyst for the partial oxidation of cyclohexane

Molybdenum blue (MB), a multivalent molybdenum oxide with a nano-ring morphology is well-known in analytical chemistry but, to date it has been largely ignored in other applications. In the present work, MB has been characterized by STEM-HAADF imaging for the first time, showing the nano-ring morphology of this complex molybdenum oxide and the ordered super-molecular framework crystals that can result from the self-assembly of these MB nano-ring units. The potential of MB as an oxidation catalyst has also been investigated, where it is shown to have excellent catalytic activity and stability in the selective oxidation of cyclohexane to cyclohexanol and cyclohexanone which are important intermediates in the production of nylon

FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium

Background:Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium. Methods:Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression. Results:Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95 confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2. Conclusion:Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2. © 2014 Cancer Research UK

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D.Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case's onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood.Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation.KEY MESSAGESThe ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.Helena Oakey ... Lynne C. Giles ... Rebecca L. Thomson ... Pat Ashwood ... Emma J. Knight ... Simon C. Barry ... Kelly McGorm ... Jennifer J. Couper ... Megan A. S. Penno ... the ENDIA Study Group ... et al