Piggery pond sludge as a nitrogen source for crops - 2. Assay of wet and stockpiled piggery pond sludge by successive cereal crops or direct measurement of soil available N

The appropriate use of wastes is a significant issue for the pig industry due to increasing pressure from regulatory authorities to protect the environment from pollution. Nitrogen contained in piggery pond sludge ( PPS) is a potential source of supplementary nutrient for crop production. Nitrogen contribution following the application of PPS to soil was obtained from 2 field experiments on the Darling Downs in southern Queensland on contrasting soil types, a cracking clay ( Vertosol) and a hardsetting sandy loam (Sodosol), and related to potentially mineralisable N from laboratory incubations conducted under controlled conditions and NO3- accumulation in the field. Piggery pond sludge was applied as-collected ( wet PPS) and following stockpiling to dry ( stockpiled PPS). Soil NO3- levels increased with increased application rates of wet and stockpiled PPS. Supplementary N supply from PPS estimated by fertiliser equivalence was generally unsatisfactory due to poor precision with this method, and also due to a high level of NO3- in the clay soil before the first assay crop. Also low recoveries of N by subsequent sorghum ( Sorghum bicolor) and wheat ( Triticum aestivum) assay crops at the 2 sites due to low in-crop rainfall in 1999 resulted in low apparent N availability. Over all, 29% ( range 12 - 47%) of total N from the wet PPS and 19% ( range 0 - 50%) from the stockpiled PPS were estimated to be plant-available N during the assay period. The high concentration of NO3- for the wet PPS application on sandy soil after the first assay crop ( 1998 barley, Hordeum vulgare) suggests that leaching of NO3- could be of concern when high rates of wet PPS are applied before infrequent periods of high precipitation, due primarily to the mineral N contained in wet PPS. Low yields, grain protein concentrations, and crop N uptake of the sorghum crop following the barley crop grown on the clay soil demonstrated a low residual value of N applied in PPS. NO3- in the sandy soil before sowing accounted for 79% of the variation in plant N uptake and was a better index than anaerobically mineralisable N ( 19% of variation explained). In clay soil, better prediction of crop N uptake was obtained when both anaerobically mineralisable N (39% of variation explained) and soil pro. le NO3- were used in combination (R-2 = 0.49).

Piggery pond sludge as a nitrogen source for crops. 1. Mineral N supply estimated from laboratory incubations and field application of stockpiled and wet sludge

Piggery pond sludge (PPS) was applied, as-collected (Wet PPS) and following stockpiling for 12 months ( Stockpiled PPS), to a sandy Sodosol and clay Vertosol at sites on the Darling Downs of Queensland. Laboratory measures of N availability were carried out on unamended and PPS-amended soils to investigate their value in estimating supplementary N needs of crops in Australia's northern grains region. Cumulative net N mineralised from the long-term ( 30 weeks) leached aerobic incubation was described by a first-order single exponential model. The mineralisation rate constant (0.057/week) was not significantly different between Control and PPS treatments or across soil types, when the amounts of initial mineral N applied in PPS treatments were excluded. Potentially mineralisable N (N-o) was significantly increased by the application of Wet PPS, and increased with increasing rate of application. Application of Wet PPS significantly increased the total amount of inorganic N leached compared with the Control treatments. Mineral N applied in Wet PPS contributed as much to the total mineral N status of the soil as did that which mineralised over time from organic N. Rates of CO2 evolution during 30 weeks of aerobic leached incubation indicated that the Stockpiled PPS was more stabilised (19-28% of applied organic C mineralised) than the Wet PPS (35-58% of applied organic C mineralised), due to higher lignin content in the former. Net nitrate-N produced following 12 weeks of aerobic non-leached incubation was highly correlated with net nitrate-N leached during 12 weeks of aerobic incubation (R-2 = 0.96), although it was

Neogenin interacts with RGMa and Netrin-1 to guide axons within the embryonic vertebrate forebrain

In the embryonic forebrain, pioneer axons establish a simple topography of dorsoventral and longitudinal tracts. The cues used by these axons during the initial formation of the axon scaffold remain largely unknown. We have investigated the axon guidance role of Neogenin, a member of the immunoglobulin (Ig) superfamily that binds to the chemoattractive ligand Netrin-1, as well as to the chemorepulsive ligand repulsive guidance molecule (RGMa). Here, we show strong expression of Neogenin and both of its putative ligands in the developing Xenopus forebrain. Neogenin loss-of-function mutants revealed that this receptor was essential for axon guidance in an early forming dorsoventral brain pathway. Similar mutant phenotypes were also observed following loss of either RGMa or Netrin-1. Simultaneous partial knock downs of these molecules revealed dosage-sensitive interactions and confirmed that these receptors and ligands were acting in the same pathway. The results provide the first evidence that Neogenin acts as an axon guidance molecule in vivo and support a model whereby Neogenin-expressing axons respond to a combination of attractive and repulsive cues as they navigate their ventral trajectory. (c) 2006 Elsevier Inc. All rights reserved.

The BAR domain proteins: Molding membranes in fission, fusion, and phagy

The Bin1/amphiphysin/Rvs167 (BAR) domain proteins are a ubiquitous protein family. Genes encoding members of this family have not yet been found in the genomes of prokaryotes, but within eukaryotes, BAR domain proteins are found universally from unicellular eukaryotes such as yeast through to plants, insects, and vertebrates. BAR domain proteins share an N-terminal BAR domain with a high propensity to adopt alpha-helical structure and engage in coiled-coil interactions with other proteins. BAR domain proteins are implicated in processes as fundamental and diverse as fission of synaptic vesicles, cell polarity, endocytosis, regulation of the actin cytoskeleton, transcriptional repression, cell-cell fusion, signal transduction, apoptosis, secretory vesicle fusion, excitation-contraction coupling, learning and memory, tissue differentiation, ion flux across membranes, and tumor suppression. What has been lacking is a molecular understanding of the role of the BAR domain protein in each process. The three-dimensional structure of the BAR domain has now been determined and valuable insight has been gained in understanding the interactions of BAR domains with membranes. The cellular roles of BAR domain proteins, characterized over the past decade in cells as distinct as yeasts, neurons, and myocytes, can now be understood in terms of a fundamental molecular function of all BAR domain proteins: to sense membrane curvature, to bind GTPases, and to mold a diversity of cellular membranes.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.

Improving support vector solutions by selecting a sequence of training subsets

In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.