Olfactory attractiveness of mixtures of some host plant and conspecific semiochemicals to the banana weevil Cosmopolites sordidus (Germar, 1824)

A simple "Y" shaped olfactometer was used in laboratory studies on the olfactory attractiveness of mixtures in various proportions of industrial analogues of some host plant and conspecific-based semiochemicals, or their combinations with banana rhizome, to the banana weevil. The aim was to identify factors that influence their attractiveness to the weevil, and consider the possibility for their use as lures for trapping the weevil in the field. Cosmopolites sordidus was attracted to the mixtures at specific concentrations and proportions of constituent chemicals. 6-methylhept-5-en-2-one was only attractive on its own at 1 µl/100 ml and in mixture with 4- mercaptophenol, but not at 10 µl, 0.01 µl, or in combination with banana rhizome. 4-mercaptohpenol and 2-n-butylfuran, which were compatible with most host plant-based chemicals and were attractive as a mixture, were perceived to be key elements in the composition of attractants to the weevil. It was concluded that in addition to the composition, other factors that may determine the attractiveness or otherwise of a mixture to C. sordidus are the proportions and concentrations of the constituent chemicals.

Asymptotic normality in mixtures of power series distributions

The problem of estimating the individual probabilities of a discrete distribution is considered. The true distribution of the independent observations is a mixture of a family of power series distributions. First, we ensure identifiability of the mixing distribution assuming mild conditions. Next, the mixing distribution is estimated by non-parametric maximum likelihood and an estimator for individual probabilities is obtained from the corresponding marginal mixture density. We establish asymptotic normality for the estimator of individual probabilities by showing that, under certain conditions, the difference between this estimator and the empirical proportions is asymptotically negligible. Our framework includes Poisson, negative binomial and logarithmic series as well as binomial mixture models. Simulations highlight the benefit in achieving normality when using the proposed marginal mixture density approach instead of the empirical one, especially for small sample sizes and/or when interest is in the tail areas. A real data example is given to illustrate the use of the methodology.

Equivalence of truncated count mixture distributions and mixtures of truncated count distributions

This article is about modeling count data with zero truncation. A parametric count density family is considered. The truncated mixture of densities from this family is different from the mixture of truncated densities from the same family. Whereas the former model is more natural to formulate and to interpret, the latter model is theoretically easier to treat. It is shown that for any mixing distribution leading to a truncated mixture, a (usually different) mixing distribution can be found so. that the associated mixture of truncated densities equals the truncated mixture, and vice versa. This implies that the likelihood surfaces for both situations agree, and in this sense both models are equivalent. Zero-truncated count data models are used frequently in the capture-recapture setting to estimate population size, and it can be shown that the two Horvitz-Thompson estimators, associated with the two models, agree. In particular, it is possible to achieve strong results for mixtures of truncated Poisson densities, including reliable, global construction of the unique NPMLE (nonparametric maximum likelihood estimator) of the mixing distribution, implying a unique estimator for the population size. The benefit of these results lies in the fact that it is valid to work with the mixture of truncated count densities, which is less appealing for the practitioner but theoretically easier. Mixtures of truncated count densities form a convex linear model, for which a developed theory exists, including global maximum likelihood theory as well as algorithmic approaches. Once the problem has been solved in this class, it might readily be transformed back to the original problem by means of an explicitly given mapping. Applications of these ideas are given, particularly in the case of the truncated Poisson family.

Aqueous gels of mixtures of ionic surfactant SDS with pluronic copolymers P123 or F127

Gel diagrams based on tube inversion and oscillatory rheometry are reported for Pluronic copolymers F127 (E98P67E98) and P123 (E21P67E21) in mixtures with anionic surfactant sodium dodecyl sulfate (SDS). Total concentrations (e, SDS+copolymer) were as high as 50 wt% with mole ratios SDS/copolymer (mr) in the ranges 1-5 (F127) a lid 1-7 (PI 23). Temperatures were its high as 90 degrees C. Determination of the temperature dependences of the dynamic moduli served to confirm the gel boundaries from tube inversion and to reveal the high elastic moduli of the gels, e.g., compared at corn parable positions in the gel phase, a 50 wt% SDS/P123 wit h mr = 7 had G' three times that of a corresponding gel of P123 alone. Sin all-angle X-ray scattering (SAX S) was used to show that the structures of all the SDS/F127 gels were bee and that the structures of the SDS/P123 gels with mr = I were either fcc(c = 30 wt%) or hex (c = 40 wt%). Assignment of structures to SDS/P123 gels with values of mr in the range 3-7 was more difficult, as high-order scattering peaks Could be very weak, and at the higher values of c and mr, the SAXS peaks included multiple reflections.

A SAXS study of the structure of gels formed by mixtures of polyoxyalkylene triblock copolymers

Small-angle X-ray scattering was used to characterise aqueous micellar gels of triblock copolymers E137S18E137, E82S9E82, E76S5E76, E62P39E62, and of two mixtures: E137S18E137 and E62P39E62 (Mix 1) and ES2S9E82 and E62P39E62 (Mix 2), each 50/50 wt%. E = oxyethylene, CH2CH2O; S = oxyphenylethylene, OCH2CH(C6H5); and P = oxypropylene, OCH2CH(CH3)- Within the concentration and temperature ranges investigated (30-40 wt% copolymer, 20-80 degrees C), spherical micelles of copolymers E137S18E137, E82S9ES2 and E62P39E62 packed into bodycentred cubic (BCC) structures. Gels of E76S5E76 were stable only at high concentrations and low temperatures, and a 70 wt/o copolymer solution at T = 30 degrees C formed a hexagonal gel consistent with cylindrical micelles. It is likely that the mixed copolymers would form two distributions of micelles, and more complex structures were expected. However, gels of Mix 2 had well-ordered BCC structures, while the less ordered gels of Mix 1 were also best characterised as BCC. (c) 2006 Society of Chemical Industry.

Self-organisation in the assembly of gels from mixtures of different dendritic peptide building blocks

This paper investigates dendritic peptides capable of assembling into nanostructured gels, and explores the effect on self-assembly of mixing different molecular building blocks. Thermal measurements, small angle Xray scattering (SAXS) and circular dichroism (CD) spectroscopy are used to probe these materials on macroscopic, nanoscopic and molecular length scales. The results from these investigations demonstrate that in this case, systems with different "size" and "chirality" factors can self-organise, whilst systems with different "shape" factors cannot. The "size" and "chirality" factors are directly connected with the molecular information programmed into the dendritic peptides, whilst the shape factor depends on the group linking these peptides together-this is consistent with molecular recognition hydrogen bond pathways between the peptidic building blocks controlling the ability of these systems to self-recognise. These results demonstrate that mixtures of relatively complex peptides, with only subtle differences on the molecular scale, can self-organise into nanoscale structures, an important step in the spontaneous assembly of ordered systems from complex mixtures.

Solvent extraction of Am(III) and Eu(III) from nitrate solution using synergistic mixtures of n-tridentate heterocycles and chlorinated cobalt dicarbollide

The separation by solvent extraction of Am-241(III) from Eu-152(III), in 1 M NaNO3 weakly acidic (pH 4) aqueous solutions, into dilute (ca. 10(-2) M) solutions of triazinylbipyridine derivatives (diethylhemi-BTP or di(benzyloxyphenyl) hemi-BTP) and chlorinated cobalt dicarbollide (COSAN) in 1-octanol or nitrobenzene has been studied. The N-tridentate heterocyclic ligands, which are selective for Am(III) over Eu(III), secured efficient separation of the two metal ions, while COSAN, strongly hydrophobic and fully dissociated in polar diluents, enhanced the extraction of the metal ions by ion-pair formation. Molecular interactions between the two co-extractants, observed at higher concentrations, led to the precipitation of their 1: 1 molecular adduct. In spite of that, efficient separations of Am and Eu ions were attained, with high separation factors, SFAm/Eu of 40 and even 60, provided the concentration of hemi-BTP was significantly greater than that of COSAN. Excess COSAN concentrations caused an antagonistic effect, decreasing both the distribution ratio of the metal ions and their separation factor.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Environmental oestrogens and breast cancer: long-term low-dose effects of mixtures of various chemical combinations

The incidence of breast cancer has risen worldwide to unprecedented levels in recent decades, making it now the major cancer of women in many parts of the world.1 Although diet, alcohol, radiation and inherited loss of BRCA1/2 genes have all been associated with increased incidence, the main identified risk factors are life exposure to hormones including physiological variations associated with puberty/pregnancy/menopause,1 personal choice of use of hormonal contraceptives2 and/or hormone replacement therapy.3–6 On this basis, exposure of the human breast to the many environmental pollutant chemicals capable of mimicking or interfering with oestrogen action7 should also be of concern.8 Hundreds of such environmental chemicals have now been measured in human breast tissue from a range of dietary and domestic exposure sources7 ,9 including persistent organochlorine pollutants (POPs),10 polybrominated diphenylethers and polybromobiphenyls,11 polychlorinated biphenyls,12 dioxins,13 alkyl phenols,14 bisphenol-A and chlorinated derivatives,15 as well as other less lipophilic compounds such as parabens (alkyl esters of p-hydroxybenzoic acid),16 but studies investigating any association between raised levels of such compounds and the development of breast cancer remain inconclusive.7–16 However, the functionality of these chemicals has continued to be assessed on the basis of individual chemicals rather than the environmental reality of long-term low-dose exposure to complex mixtures. This misses the potential for individuals to have high concentrations of different compounds but with a common mechanism of action. It also misses the complex interactions between chemicals and physiological hormones which together may act to alter the internal homeostasis of the oestrogenic environment of mammary tissue.

Coassembly in binary mixtures of peptide amphiphiles containing oppositely charged residues

The self-assembly in water of designed peptide amphiphile (PA) C16-ETTES containing two anionic residues and its mixtures with C16-KTTKS containing two cationic residues has been investigated. Multiple spectroscopy, microscopy, and scattering techniques are used to examine ordering extending from the β-sheet structures up to the fibrillar aggregate structure. The peptide amphiphiles both comprise a hexadecyl alkyl chain and a charged pentapeptide headgroup containing two charged residues. For C16-ETTES, the critical aggregation concentration was determined by fluorescence experiments. FTIR and CD spectroscopy were used to examine β-sheet formation. TEM revealed highly extended tape nanostructures with some striped regions corresponding to bilayer structures viewed edge-on. Small-angle X-ray scattering showed a main 5.3 nm bilayer spacing along with a 3 nm spacing. These spacings are assigned respectively to predominant hydrated bilayers and a fraction of dehydrated bilayers. Signs of cooperative self-assembly are observed in the mixtures, including reduced bundling of peptide amphiphile aggregates (extended tape structures) and enhanced β-sheet formation.

Multiwalled nanotubes formed by catanionic mixtures of drug amphiphiles

Mixing of oppositely charged amphiphilic molecules (catanionic mixing) offers an attractive strategy to produce morphologies different from those formed by individual molecules. We report here on the use of catanionic mixing of anticancer drug amphiphiles to construct multiwalled nanotubes containing a fixed and high drug loading. We found that the molecular mixing ratio, the solvent composition, the overall drug concentrations, as well as the molecular design of the studied amphiphiles are all important experimental parameters contributing to the tubular morphology. We believe these results demonstrate the remarkable potential that anticancer drugs could offer to self-assemble into discrete nanostructures and also provide important insight into the formation mechanism of nanotubes by catanionic mixtures. Our preliminary animal studies reveal that the CPT nanotubes show significantly prolonged retention time in the tumor site after intratumoral injection.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.