Bi-directional transfer of nitrogen between alfalfa and bromegrass: Short and long term evidence

Transfer of N from legumes to associated non-legumes has been demonstrated under a wide range of conditions. Because legumes are able to derive their N requirements from N₂ fixation, legumes can serve, through the transfer of N, as a source of N for accompanying non-legumes. Studies, therefore, are often limited to the transfer of N from the legume to the non-legume. However, legumes preferentially rely on available soil N as their source of N. To determine whether N can be transferred from a non-legume to a legume, two greenhouse experiments were conducted. In the short-term N-transfer experiment, a portion of the foliage of meadow bromegrass (Bromus riparius Rhem.) or alfalfa (Medicago sativa L.) was immersed in a highly labelled ¹⁵N-solution and following a 64 h incubation, the roots and leaves of the associated alfalfa and bromegrass were analyzed for ¹⁵N. In the long-term N transfer experiment, alfalfa and bromegrass were grown in an ¹⁵N-labelled nutrient solution and transplanted in pots with unlabelled bromegrass and alfalfa plants. Plants were harvested at 50 and 79 d after transplanting and analyzed for ¹⁵N content. Whether alfalfa or bromegrass were the donor plants in the short-term experiment, roots and leaves of all neighbouring alfalfa and bromegrass plants were enriched with ¹⁵N. Similarly, when alfalfa or bromegrass was labelled in the long-term experiment, the roots and shoots of neighbouring alfalfa and bromegrass plants became enriched with ¹⁵N. These two studies conclusively show that within a short period of time, N is transferred from both the N₂-fixing legume to the associated non-legume and also from the non-legume to the N₂-fixing legume. The occurrence of a bi-directional N transfer between N₂-fixing and non-N₂-fixing plants should be taken into consideration when the intensity of N cycling and the directional flow of N in pastures and natural ecosystems are investigated.     

Management of diabetes mellitus in children.

In many respects the management of diabetes mellitus in children differs from the management in adults. Guidelines for the short- and long-term management of the child with diabetes mellitus, particularly as it relates to clinical presentation, treatment of ketoacidosis, long-term therapy and psychological counseling, are presented. The specific aspects of diabetes management that are unique to the child are best met by an increased understanding of the problems by the child and the family and an integrated approach by the physician and allied health personnel.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.