The crystal structure of eulytite Pb3BiV3O12

The crystal structure of Pb3BiV3O12 was solved using single-crystal X-ray diffraction technique. The compound crystallizes in the cubic system View the MathML source (No. 220) with eulytite structure with a = 10.7490(7) Å, V = 1241.95(14) Å3 and Z = 4. The final R1 value of 0.0198 (wR2=0.0384) was achieved for 359 independent reflections during the structure refinement. The Pb2+ and Bi3+ cations occupy the special position (16c) while the oxygen anions occupy the general position (48e) in the crystal structure. Unlike many other eulytite compounds, all the crystallographic positions are fully occupied. The structure consists of edge-shared Pb/Bi octahedra linked at the corners to independent [VO4]3− tetrahedra units, generating a eulytite-type network in the crystal lattice.

Size-dependent elasticity of nanocrystalline titania

Synchrotron-based high-pressure x-ray diffraction measurements indicate that compressibility, a fundamental materials property, can have a size-specific minimum value. The bulk modulus of nanocrystalline titania has a maximum at particle size of 15 nm. This can be explained by dislocation behavior because very high dislocation contents can be achieved when shear stress induced within nanoparticles counters the repulsion between dislocations. As particle size decreases, compression increasingly generates dislocation networks hardened by overlap of strain fields that shield intervening regions from external pressure. However, when particles become too small to sustain high dislocation concentrations, elastic stiffening declines. The compressibility has a minimum at intermediate sizes.

Syntheses and characterization of trans-[NiL2(NCS)2][L = 2-(aminomethyl) pyridine], trans-[NiL02(NSC)2][L0 = 2-(2-aminoethyl)pyridine] and trans-[NiL00 2(NSC)2] [L00 = 2-(2-methylaminoethyl)pyridine] complexes: X-ray single crystal structure of trans-[NiL02(NSC)2] [L0 = 2-(2-aminoethyl)pyridine]

[NiL2(NCS)2] (1) [L = 2-(aminomethyl)pyridine], [NiL02(NCS)2] (2) [(L0) = 2-(2-aminoethyl)pyridine and [NiL00 2(NCS)2] (3) [L00 = 2-(2-methylaminoethyl)pyridine] have been synthesized from solution. All the complexes possess trans geometry as is evident from solid state UV–Vis spectral study and X-ray single crystal structure analysis of complex 2 unambiguously proves trans geometry of the species.

Synthesis, crystal structures and protease activity of amino acid Schiff base iron(III) complexes

Iron(III) complexes, (NHEt3)[Fe(III)(sal-met)(2)] and (NHEt3)[Fe(III)(sal-phe)(2)], of amino acid Schiffbase ligands, viz., N-salicylidene-L-methionine and N-salicylidene L-phenylalanine, have been prepared and their binding to bovine serum albumin (BSA) and photo-induced BSA cleavage activity have been investigated. The complexes are structurally characterized by single crystal X-ray crystallography. The crystal Structures of the discrete mononuclear rnonoanionic complexes show FeN2O4 octahedral coordination geometry in which the tridentate dianionic amino acid Schiff base ligand binds through phenolate and carboxylate oxygen and imine nitrogen atoms. The imine nitrogen atoms are trans to each other. The Fe-O and Fe-N bond distances range between 1.9 and 2.1 angstrom. The sal-met complex has two pendant thiomethyl groups. The high-spin iron(III) complexes (mu(eff) similar to 5.9 mu(B)) exhibit quasi-reversible Fe(III)/Fe(II) redox process near -0.6 V vs. SCE in water. These complexes display a visible electronic hand near 480 nm in tris-HCl buffer assignable to the phenolate-to-iron(III) charge transfer transition. The water soluble complexes bind to BSA giving binding constant values of similar to 10(5) M-1. The Complexes show non-specific oxidative cleavage of BSA protein on photo-irradiation with UV-A light of 365 nm.

Rotating crystal ESR cavity for angular variation studies at room temperature

We describe an X-band ESR cavity for angular variation studies on single crystals at room temperature. The cavity was found to have a high Q over wide rotation angles. Review of Scientific Instruments is copyrighted by The American Institute of Physics.

Atheroma: Is calcium important or not? A modelling study of stress within the atheromatous fibrous cap in relation to position and size of calcium deposits

Atheromatous plaque rupture h the cause of the majority of strokes and heart attacks in the developed world. The role of calcium deposits and their contribution to plaque vulnerability are controversial. Some studies have suggested that calcified plaque tends to be more stable whereas others have suggested the opposite. This study uses a finite element model to evaluate the effect of calcium deposits on the stress within the fibrous cap by varying their location and size. Plaque fibrous cap, lipid pool and calcification were modeled as hyperelastic, Isotropic, (nearly) incompressible materials with different properties for large deformation analysis by assigning time-dependent pressure loading on the lumen wall. The stress and strain contours were illustrated for each condition for comparison. Von Mises stress only increases up to 1.5% when varying the location of calcification in the lipid pool distant to the fibrous cap. Calcification in the fibrous cap leads to a 43% increase of Von Mises stress when compared with that in the lipid pool. An increase of 100% of calcification area leads to a 15% stress increase in the fibrous cap. Calcification in the lipid pool does not increase fibrous cap stress when it is distant to the fibrous cap, whilst large areas of calcification close to or in the fibrous cap may lead to a high stress concentration within the fibrous cap, which may cause plaque rupture. This study highlights the application of a computational model on a simulation of clinical problems, and it may provide insights into the mechanism of plaque rupture.

Crystal data for high-temperature dicalcium lead propionate, Ca2Pb(CH3CH2COO)6

The high-temperature paraelectric phase of dicalcium lead propionate, DCLP, at 363 ± 5 K is tetragonal, with a = 12.574 (6), c = 17.403 (9) Å, V = 2751.4 Å3, Z = 4 and corresponds to the space group P41212 (or P43212). The thermal expansion curve shows the transition somewhere between 328 and 343 K.

Crystal structures of 1,4-diaza bicyclo[2.2.2.]octane and N,N-dimethyl formamide adducts of copper acetate

The crystal structures of copper acetate adducts with 1,4-diaza bicyclo [2.2.2.]octane and N,N-dimethyl formamide are shown to be dimeric with Cu---Cu distances of 2.633 Å and 2.616 Å respectively.

x-ray studies on crystalline complexes involving amino-acids and peptides .10. head-to-tail sequences in the crystal-structure of l-lysine acetate

l-Lysine acetate crystallises in the monoclinic space group P21 with a = 5.411 (1), b = 7.562(1), c= l2.635(2) Å and β = 91.7(1). The crystal structure was solved by direct methods and refined to an R value of 0.049 using the full matrix least squares method. The conformation and the aggregation of lysine molecules in the structure are similar to those found in the crystal structure of l-lysine l-aspartate. A conspicuous similarity between the crystal structures of l-arginine acetate and l-lysine acetate is that in both cases the strongly basic side chain, although having the largest pK value, interacts with the weakly acidic acetate group leaving the α-amino and the α-carboxylate groups to take part in head-to-tail sequences. These structures thus indicate that electrostatic effects are strongly modulated by other factors so as to give rise to head-to-tail sequences which have earlier been shown to be an almost universal feature of amino acid aggregation in the solid state.

Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual's previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag-recapture data and tag-recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

An extravariation model for improving confidence intervals of population size estimates from removal data

We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).

Size-dependent natural mortality of juvenile banana prawns Penaeus merguiensis in the Gulf of Carpentaria, Australia

Natural mortality of marine invertebrates is often very high in the early life history stages and decreases in later stages. The possible size-dependent mortality of juvenile banana prawns, P. merguiensis (2-15 mm carapace length) in the Gulf of Carpentaria was investigated. The analysis was based on the data collected at 2-weekly intervals by beam trawls at four sites over a period of six years (between September 1986 and March 1992). It was assumed that mortality was a parametric function of size, rather than a constant. Another complication in estimating mortality for juvenile banana prawns is that a significant proportion of the population emigrates from the study area each year. This effect was accounted for by incorporating the size-frequency pattern of the emigrants in the analysis. Both the extra parameter in the model required to describe the size dependence of mortality, and that used to account for emigration were found to be significantly different from zero, and the instantaneous mortality rate declined from 0.89 week(-1) for 2 mm prawns to 0.02 week(-1) for 15 mm prawns.

Does the size of subsamples taken from multispecies trawl catches affect estimates of catch composition and abundance?

Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were "rare" in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the "rare" species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the "abundant" species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.