Effect of dietary phytate and microbial phytase on mineral and trace element bioavailability- a literature review

Phytic acid (PA) is the main phosphorus storage compound in cereals, legumes and oil seeds. In human populations where phytate-rich cereals such as wheat, maize and rice are a staple food, phytate may lead to mineral and trace element deficiency. Zinc appears to be the trace element whose bioavailability is most influenced by PA. Furthermore, several studies in humans as well as in monogastric animals clearly indicate an inhibition of non-haem iron absorption at marginal iron supply due to phytic acid. In fact PA seems to be, at least partly, responsible for the low absorption efficiency and high incidence of iron deficiency anaemia evident in most developing countries, where largely vegetarian diets are consumed Microbial phytases have provided a realistic means of improving mineral availability from traditionally high-phytate diets. In fact it has been consistently shown that Aspergillus phytases significantly enhance the absorption of calcium, magnesium and zinc in pigs and rats. Furthermore there are a few studies in humans indicating an improvement of iron bioavailability due to microbial phytase.

Antecedent priming at trace positions in children's sentence processing

The present study examines whether children reactivate a moved constituent at its gap position and how children's more limited working memory span affects the way they process filler-gap dependencies. 46 5-7 year-old children and 54 adult controls participated in a cross-modal picture priming experiment and underwent a standardized working memory test. The results revealed a statistically significant interaction between the participants' working memory span and antecedent reactivation: High-span children (n = 19) and high-span adults (n = 22) showed evidence of antecedent priming at the gap site, while for low-span children and adults, there was no such effect. The antecedent priming effect in the high-span participants indicates that in both children and adults, dislocated arguments access their antecedents at gap positions. The absence of an antecedent reactivation effect in the low-span participants could mean that these participants required more time to integrate the dislocated constituent and reactivated the filler later during the sentence.

Managing groups of files in a rule oriented data management system (iRODS)

The iRODS system, created by the San Diego Supercomputing Centre, is a rule oriented data management system that allows the user to create sets of rules to define how the data is to be managed. Each rule corresponds to a particular action or operation (such as checksumming a file) and the system is flexible enough to allow the user to create new rules for new types of operations. The iRODS system can interface to any storage system (provided an iRODS driver is built for that system) and relies on its’ metadata catalogue to provide a virtual file-system that can handle files of any size and type. However, some storage systems (such as tape systems) do not handle small files efficiently and prefer small files to be packaged up (or “bundled”) into larger units. We have developed a system that can bundle small data files of any type into larger units - mounted collections. The system can create collection families and contains its’ own extensible metadata, including metadata on which family the collection belongs to. The mounted collection system can work standalone and is being incorporated into the iRODS system to enhance the systems flexibility to handle small files. In this paper we describe the motivation for creating a mounted collection system, its’ architecture and how it has been incorporated into the iRODS system. We describe different technologies used to create the mounted collection system and provide some performance numbers.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland

A first step in interpreting the wide variation in trace gas concentrations measured over time at a given site is to classify the data according to the prevailing weather conditions. In order to classify measurements made during two intensive field campaigns at Mace Head, on the west coast of Ireland, an objective method of assigning data to different weather types has been developed. Air-mass back trajectories calculated using winds from ECMWF analyses, arriving at the site in 1995–1997, were allocated to clusters based on a statistical analysis of the latitude, longitude and pressure of the trajectory at 12 h intervals over 5 days. The robustness of the analysis was assessed by using an ensemble of back trajectories calculated for four points around Mace Head. Separate analyses were made for each of the 3 years, and for four 3-month periods. The use of these clusters in classifying ground-based ozone measurements at Mace Head is described, including the need to exclude data which have been influenced by local perturbations to the regional flow pattern, for example, by sea breezes. Even with a limited data set, based on 2 months of intensive field measurements in 1996 and 1997, there are statistically significant differences in ozone concentrations in air from the different clusters. The limitations of this type of analysis for classification and interpretation of ground-based chemistry measurements are discussed.

Issues in the assimilation of the sources of a trace gas by inverse modelling

The problem of reconstructing the (otherwise unknown) source and sink field of a tracer in a fluid is studied by developing and testing a simple tracer transport model of a single-level global atmosphere and a dynamic data assimilation system. The source/sink field (taken to be constant over a 10-day assimilation window) and initial tracer field are analysed together by assimilating imperfect tracer observations over the window. Experiments show that useful information about the source/sink field may be determined from relatively few observations when the initial tracer field is known very accurately a-priori, even when a-priori source/sink information is biased (the source/sink a-priori is set to zero). In this case each observation provides information about the source/sink field at positions upstream and the assimilation of many observations together can reasonably determine the location and strength of a test source.

Impact of earthworms on trace element solubility in contaminated mine soils amended with green waste compost

The common practice of remediating metal contaminated mine soils with compost can reduce metal mobility and promote revegetation, but the effect of introduced or colonising earthworms on metal solubility is largely unknown. We amended soils from an As/Cu (1150 mgAs kg−1 and 362 mgCu kg−1) and Pb/Zn mine (4550 mgPb kg−1 and 908 mgZn kg−1) with 0, 5, 10, 15 and 20% compost and then introduced Lumbricus terrestris. Porewater was sampled and soil extracted with water to determine trace element solubility, pH and soluble organic carbon. Compost reduced Cu, Pb and Zn, but increased As solubility. Earthworms decreased water soluble Cu and As but increased Pb and Zn in porewater. The effect of the earthworms decreased with increasing compost amendment. The impact of the compost and the earthworms on metal solubility is explained by their effect on pH and soluble organic carbon and the environmental chemistry of each element.