Characterisation of organoclays and adsorption of p-nitrophenol : environmental application

Organoclays were synthesised through ion exchange of a single surfactant for sodium ions, and characterised by a range of method including X-ray diffraction (XRD), BET, X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR), and transmission electron microscopy (TEM). The change in surface properties of montmorillonite and organoclays intercalated with the surfactant, tetradecyltrimethylammonium bromide (TDTMA) were determined using XRD through the change in basal spacing and the expansion occurred by the adsorbed p-nitrophenol. The changes of interlayer spacing were observed in TEM. In addition, the surface measurement such as specific surface area and pore volume was measured and calculated using BET method, this suggested the loaded surfactant is highly important to determine the sorption mechanism onto organoclays. The collected results of XPS provided the chemical composition of montmorillonite and organoclays, and the high-resolution XPS spectra offered the chemical states of prepared organoclays with binding energy. Using TGA and FT-IR, the confirmation of intercalated surfactant was investigated. The collected data from various techniques enable an understanding of the changes in structure and surface properties. This study is of importance to provide mechanisms for the adsorption of organic molecules, especially in contaminated environmental sites and polluted waters.

An evaluation of alternative media for pebble matrix filtration using clay balls and recycled crushed glass

Protecting slow sand filters (SSFs) from high-turbidity waters by pretreatment using pebble matrix filtration (PMF) has previously been studied in the laboratory at University College London, followed by pilot field trials in Papua New Guinea and Serbia. The first full-scale PMF plant was completed at a water-treatment plant in Sri Lanka in 2008, and during its construction, problems were encountered in sourcing the required size of pebbles and sand as filter media. Because sourcing of uniform-sized pebbles may be problematic in many countries, the performance of alternative media has been investigated for the sustainability of the PMF system. Hand-formed clay balls made at a 100-yearold brick factory in the United Kingdom appear to have satisfied the role of pebbles, and a laboratory filter column was operated by using these clay balls together with recycled crushed glass as an alternative to sand media in the PMF. Results showed that in countries where uniform-sized pebbles are difficult to obtain, clay balls are an effective and feasible alternative to natural pebbles. Also, recycled crushed glass performed as well as or better than silica sand as an alternative fine media in the clarification process, although cleaning by drainage was more effective with sand media. In the tested filtration velocity range of ð0:72–1:33Þ m=h and inlet turbidity range of (78–589) NTU, both sand and glass produced above 95% removal efficiencies. The head loss development during clogging was about 30% higher in sand than in glass media.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Developing the atlas of cancer in Queensland : methodological issues

Background: Achieving health equity has been identified as a major challenge, both internationally and within Australia. Inequalities in cancer outcomes are well documented, and must be quantified before they can be addressed. One method of portraying geographical variation in data uses maps. Recently we have produced thematic maps showing the geographical variation in cancer incidence and survival across Queensland, Australia. This article documents the decisions and rationale used in producing these maps, with the aim to assist others in producing chronic disease atlases. Methods: Bayesian hierarchical models were used to produce the estimates. Justification for the cancers chosen, geographical areas used, modelling method, outcome measures mapped, production of the adjacency matrix, assessment of convergence, sensitivity analyses performed and determination of significant geographical variation is provided. Conclusions: Although careful consideration of many issues is required, chronic disease atlases are a useful tool for assessing and quantifying geographical inequalities. In addition they help focus research efforts to investigate why the observed inequalities exist, which in turn inform advocacy, policy, support and education programs designed to reduce these inequalities.

Transition from n- to p-type of spray pyrolysis deposited Cu doped ZnO thin films for NO2 sensing

The structural, optical, and gas-sensing properties of spray pyrolysis deposited Cu doped ZnO thin films were investigated. Gas response of the undoped and doped films to N02 (oxidizing) gas shows an increase and decrease in resistance, respectively, indicating p-type conduction in doped samples. The UV-Vis spectra of the films show decrease in the bandgap with increasing Cu concentration in ZnO. The observed p-type conductivity is attributed to the holes generated by incorporated Cu atoms on Zn sites in ZnO thin films. The X-ray diffraction spectra showed that samples are polycrystalline with the hexagonal wurtzite structure and increasing the concentration of Cu caused a decrease in the intensity of the dominant (002) peak. The surface morphology of films was studied by scanning electron microscopy and the presence of Cu was also confirmed by X-ray photoelectron spectroscopy. Seebeck effect measurements were utilized to confirm the p-type conduction of Cu doped ZnO thin films. Copyright © 2009 American Scientific Publishers All rights reserved.

[P,P-Di-tert-butyl-N-trimethylsilyl- P-(trimethylsilylamino)phosphineimidato-N,N]bis(pyridine-N)- lithium(I)

In the title compound, [Li(C14H36N2PSi2)(C5H5N)2], the bulky chelating monoanionic P,P-di-tert-butyl-N-trimethylsilyl-P-(trimethylsilylamino)phosphine imidate ligand and two pyridine ligands bind to Li in a pseudo-tetrahedral arrangement with twofold symmetry. The Li-N [phosphine]distance is 2.048 (5) Å, while the LiP distance is 2.520 (6) Å

Extracellular matrix of the human cyclic corpus luteum

Extracellular matrix regulates many cellular processes likely to be important for development and regression of corpora lutea. Therefore, we identified the types and components of the extracellular matrix of the human corpus luteum at different stages of the menstrual cycle. Two different types of extracellular matrix were identified by electron microscopy; subendothelial basal laminas and an interstitial matrix located as aggregates at irregular intervals between the non-vascular cells. No basal laminas were associated with luteal cells. At all stages, collagen type IV α1 and laminins α5, β2 and γ1 were localized by immunohistochemistry to subendothelial basal laminas, and collagen type IV α1 and laminins α2, α5, β1 and β2 localized in the interstitial matrix. Laminin α4 and β1 chains occurred in the subendothelial basal lamina from mid-luteal stage to regression; at earlier stages, a punctate pattern of staining was observed. Therefore, human luteal subendothelial basal laminas potentially contain laminin 11 during early luteal development and, additionally, laminins 8, 9 and 10 at the mid-luteal phase. Laminin α1 and α3 chains were not detected in corpora lutea. Versican localized to the connective tissue extremities of the corpus luteum. Thus, during the formation of the human corpus luteum, remodelling of extracellular matrix does not result in basal laminas as present in the adrenal cortex or ovarian follicle. Instead, novel aggregates of interstitial matrix of collagen and laminin are deposited within the luteal parenchyma, and it remains to be seen whether this matrix is important for maintaining the luteal cell phenotype.

Homodimerization controls the fibroblast growth factor 9 subfamily's receptor binding and heparan sulfate-dependent diffusion in the extracellular matrix

Uncontrolled fibroblast growth factor (FGF) signaling can lead to human diseases, necessitating multiple layers of self-regulatory control mechanisms to keep its activity in check. Herein, we demonstrate that FGF9 and FGF20 ligands undergo a reversible homodimerization, occluding their key receptor binding sites. To test the role of dimerization in ligand autoinhibition, we introduced structure-based mutations into the dimer interfaces of FGF9 and FGF20. The mutations weakened the ability of the ligands to dimerize, effectively increasing the concentrations of monomeric ligands capable of binding and activating their cognate FGF receptor in vitro and in living cells. Interestingly, the monomeric ligands exhibit reduced heparin binding, resulting in their increased radii of heparan sulfate-dependent diffusion and biologic action, as evidenced by the wider dilation area of ex vivo lung cultures in response to implanted mutant FGF9-loaded beads. Hence, our data demonstrate that homodimerization autoregulates FGF9 and FGF20's receptor binding and concentration gradients in the extracellular matrix. Our study is the first to implicate ligand dimerization as an autoregulatory mechanism for growth factor bioactivity and sets the stage for engineering modified FGF9 subfamily ligands, with desired activity for use in both basic and translational research.