Dichotomy in the NRT Gene Families of Dicots and Grass Species

A large proportion of the nitrate (NO3−) acquired by plants from soil is actively transported via members of the NRT families of NO3− transporters. In Arabidopsis, the NRT1 family has eight functionally characterised members and predominantly comprises low-affinity transporters; the NRT2 family contains seven members which appear to be high-affinity transporters; and there are two NRT3 (NAR2) family members which are known to participate in high-affinity transport. A modified reciprocal best hit (RBH) approach was used to identify putative orthologues of the Arabidopsis NRT genes in the four fully sequenced grass genomes (maize, rice, sorghum, Brachypodium). We also included the poplar genome in our analysis to establish whether differences between Arabidopsis and the grasses may be generally applicable to monocots and dicots. Our analysis reveals fundamental differences between Arabidopsis and the grass species in the gene number and family structure of all three families of NRT transporters. All grass species possessed additional NRT1.1 orthologues and appear to lack NRT1.6/NRT1.7 orthologues. There is significant separation in the NRT2 phylogenetic tree between NRT2 genes from dicots and grass species. This indicates that determination of function of NRT2 genes in grass species will not be possible in cereals based simply on sequence homology to functionally characterised Arabidopsis NRT2 genes and that proper functional analysis will be required. Arabidopsis has a unique NRT3.2 gene which may be a fusion of the NRT3.1 and NRT3.2 genes present in all other species examined here. This work provides a framework for future analysis of NO3− transporters and NO3− transport in grass crop species

Cost-Effectiveness of a Telephone-Delivered Intervention for Physical Activity and Diet

Background: Given escalating rates of chronic disease, broad-reach and cost-effective interventions to increase physical activity and improve dietary intake are needed. The cost-effectiveness of a Telephone Counselling intervention to improve physical activity and diet, targeting adults with established chronic diseases in a low socio-economic area of a major Australian city was examined. Methodology/Principal Findings: A cost-effectiveness modelling study using data collected between February 2005 and November 2007 from a cluster-randomised trial that compared Telephone Counselling with a “Usual Care” (brief intervention) alternative. Economic outcomes were assessed using a state-transition Markov model, which predicted the progress of participants through five health states relating to physical activity and dietary improvement, for ten years after recruitment. The costs and health benefits of Telephone Counselling, Usual Care and an existing practice (Real Control) group were compared. Telephone Counselling compared to Usual Care was not cost-effective ($78,489 per quality adjusted life year gained). However, the Usual Care group did not represent existing practice and is not a useful comparator for decision making. Comparing Telephone Counselling outcomes to existing practice (Real Control), the intervention was found to be cost-effective ($29,375 per quality adjusted life year gained). Usual Care (brief intervention) compared to existing practice (Real Control) was also cost-effective ($12,153 per quality adjusted life year gained). Conclusions/Significance: This modelling study shows that a decision to adopt a Telephone Counselling program over existing practice (Real Control) is likely to be cost-effective. Choosing the ‘Usual Care’ brief intervention over existing practice (Real Control) shows a lower cost per quality adjusted life year, but the lack of supporting evidence for efficacy or sustainability is an important consideration for decision makers. The economics of behavioural approaches to improving health must be made explicit if decision makers are to be convinced that allocating resources toward such programs is worthwhile

Modelling in Public Health

This chapter outlines the main uses and challenges of modelling in public health. Individual model methods are discussed alongside examples of their use. This will help you to interpret public health models and to be aware of some of their main assumptions, thereby allowing you to use models more appropriately in your day-to-day work

Crystal structure of a nitrate/nitrite exchanger

Mineral nitrogen in nature is often found in the form of nitrate (NO(3)(-)). Numerous microorganisms evolved to assimilate nitrate and use it as a major source of mineral nitrogen uptake(1). Nitrate, which is central in nitrogen metabolism, is first reduced to nitrite (NO(2)(-)) through a two-electron reduction reaction(2,3). The accumulation of cellular nitrite can be harmful because nitrite can be reduced to the cytotoxic nitric oxide. Instead, nitrite is rapidly removed from the cell by channels and transporters, or reduced to ammonium or dinitrogen through the action of assimilatory enzymes(3). Despite decades of effort no structure is currently available for any nitrate transport protein and the mechanism by which nitrate is transported remains largely obscure. Here we report the structure of a bacterial nitrate/nitrite transport protein, NarK, from Escherichia coli, with and without substrate. The structures reveal a positively charged substrate-translocation pathway lacking protonatable residues, suggesting that NarK functions as a nitrate/nitrite exchanger and that H(+)s are unlikely to be co-transported. Conserved arginine residues form the substrate-binding pocket, which is formed by association of helices from the two halves of NarK. Key residues that are important for substrate recognition and transport are identified and related to extensive mutagenesis and functional studies. We propose that NarK exchanges nitrate for nitrite by a rocker-switch mechanism facilitated by inter-domain H-bond networks

The Determinants of Systematic Risk of Renewable Energy Firms

Conventional asset pricing theory predicts that expected stock returns are driven by systematic risk. In this context, we investigate the determinants of systematic risk of renewable energy firms. To do so, we estimate a dynamic beta model using a cross-country panel data set of 578 renewable energy firms from 52 countries for the period 2005–2016. We employ both global and country-specific factors to explain both variation over time and variation between firms in their systematic risk. The results show that systematic risk of renewable energy firms is negatively influenced by oil returns and that country-level net-imports, environmental policy stringency, and environmental policy stability explain differences in risk at the country level