Pizgrischite, (Cu,Fe)Cu14PbBi17S35, a new sulfosalt from the Swiss Alps: Description, crystal structure and occurrence

Pizgrischite, (Cu,Fe)Cu14PbBi17S35, is a new mineral species named after the type locality, Piz Grisch Mountain, Val Ferrera, Graubunden, Switzerland. This sulfosalt occurs as thin, striated, metallic lead-grey blades measuring up to I cm in length, embedded in quartz and associated with tetrahedrite, chalcopyrite, pyrite, sphalerite, emplectite and derivatives of the aikinite-bismuthinite series. In plane-polarized light, the new species is brownish grey with no perceptible pleochroism; under crossed nicols in oil immersion, it presents a weak anisotropy with dark brown tints. Minimum and maximum reflectance values (in %) in air are: 40.7-42.15 (470 nm), 41.2-43.1 (546 nm), 41.2-43.35 (589 nm) and 40.7-43.3 (650 nm). Cleavage is perfect along 001 I and well developed on {010}. Abundant polysynthetic twinning is observed on (010). The mean micro-indentation hardness is 190 kg/mm(2) (Mohs hardness 3.3), and the calculated density is 6.58 g/cm(3). Electron-microprobe analyses yield (wt%; mean result of seven analyses): Cu 16.48, Pb 2.10, Fe 0.77, Bi 60.70, Sb 0.35, S 19.16, Se 0.04, total 99.60. The resulting empirical chemical formula is (Cu15.24Fe0.80Pb0.60)(Sigma 16.64)(Bi17.07Sb0.17)(Sigma 17.24)(S35.09Se0.03)(Sigma 35.12), in accordance with the formula derived from the single-crystal refinement of the structure, (Cu,Fe)Cu14PbBi17S35. Pizgrischite is monoclinic, space group C2/m, with the following unit-cell parameters: a 35.054(2), b3.91123(I), c43.192(2) angstrom, beta 96.713(4)degrees, V5881.24 angstrom(3), Z=4. The strongest seven X-ray powder-diffraction lines [d in angstrom (I)(hkl)] are: 5.364(40)((6) over bar 04), 4.080(50)((8) over bar 05), 3.120(40)(118), 3.104(68)((3) over bar 18), 2.759(53) ((9) over bar 11),2.752(44)(910) and 1.956(100)(020). The crystal structure is an expanded monoclinic derivative of kupcikite. Pizgrischite belongs to the cuprobismutite series of bismuth sulfosalts but, sensu stricto, it is not a homologue of cuprobismutite. At the type locality. pizarischite is the result of the Alpine metamorphism under greenschist-facies conditions of pre-Tertiary hydrothermal Cu-Bi mineralization.

Permanent Genetic Resources added to Molecular Ecology Resources Database 1 August 2010-30 September 2010

This article documents the addition of 229 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Acacia auriculiformis x Acacia mangium hybrid, Alabama argillacea, Anoplopoma fimbria, Aplochiton zebra, Brevicoryne brassicae, Bruguiera gymnorhiza, Bucorvus leadbeateri, Delphacodes detecta, Tumidagena minuta, Dictyostelium giganteum, Echinogammarus berilloni, Epimedium sagittatum, Fraxinus excelsior, Labeo chrysophekadion, Oncorhynchus clarki lewisi, Paratrechina longicornis, Phaeocystis antarctica, Pinus roxburghii and Potamilus capax. These loci were cross-tested on the following species: Acacia peregrinalis, Acacia crassicarpa, Bruguiera cylindrica, Delphacodes detecta, Tumidagena minuta, Dictyostelium macrocephalum, Dictyostelium discoideum, Dictyostelium purpureum, Dictyostelium mucoroides, Dictyostelium rosarium, Polysphondylium pallidum, Epimedium brevicornum, Epimedium koreanum, Epimedium pubescens, Epimedium wushanese and Fraxinus angustifolia.

Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations

Geophysical techniques can help to bridge the inherent gap with regard to spatial resolution and the range of coverage that plagues classical hydrological methods. This has lead to the emergence of the new and rapidly growing field of hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data. To address this problem, we have developed a strategy towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for local-scale studies characterized by high-resolution and high-quality datasets. Monte-Carlo-based optimization techniques are flexible and versatile, allow for accounting for a wide variety of data and constraints of differing resolution and hardness and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions. Compared to more conventional approaches of this kind, our approach provides significant advancements in the way that the larger-scale deterministic information resolved by the hydrogeophysical data can be accounted for, which represents an inherently problematic, and as of yet unresolved, aspect of Monte-Carlo-type conditional simulation techniques. We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to corresponding field data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

Double incretin receptor knock-out (DIRKO) mice present with alterations of trabecular and cortical micromorphology and bone strength.

A role for gut hormone in bone physiology has been suspected. We evidenced alterations of microstructural morphology (trabecular and cortical) and bone strength (both at the whole-bone - and tissue-level) in double incretin receptor knock-out (DIRKO) mice as compared to wild-type littermates. These results support a role for gut hormones in bone physiology. INTRODUCTION: The two incretins, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), have been shown to control bone remodeling and strength. However, lessons from single incretin receptor knock-out mice highlighted a compensatory mechanism induced by elevated sensitivity to the other gut hormone. As such, it is unclear whether the bone alterations observed in GIP or GLP-1 receptor deficient animals resulted from the lack of a functional gut hormone receptor, or by higher sensitivity for the other gut hormone. The aims of the present study were to investigate the bone microstructural morphology, as well as bone tissue properties, in double incretin receptor knock-out (DIRKO) mice. METHODS: Twenty-six-week-old DIRKO mice were age- and sex-matched with wild-type (WT) littermates. Bone microstructural morphology was assessed at the femur by microCT and quantitative X-ray imaging, while tissue properties were investigated by quantitative backscattered electron imaging and Fourier-transformed infrared microscopy. Bone mechanical response was assessed at the whole-bone- and tissue-level by 3-point bending and nanoindentation, respectively. RESULTS: As compared to WT animals, DIRKO mice presented significant augmentations in trabecular bone mass and trabecular number whereas bone outer diameter, cortical thickness, and cortical area were reduced. At the whole-bone-level, yield stress, ultimate stress, and post-yield work to fracture were significantly reduced in DIRKO animals. At the tissue-level, only collagen maturity was reduced by 9 % in DIRKO mice leading to reductions in maximum load, hardness, and dissipated energy. CONCLUSIONS: This study demonstrated the critical role of gut hormones in controlling bone microstructural morphology and tissue properties.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.