A novel quantitative method for analyzing the distributions of nanoparticles between different tissue and intracellular compartments

The penetration, translocation, and distribution of ultrafine and nanoparticles in tissues and cells are challenging issues in aerosol research. This article describes a set of novel quantitative microscopic methods for evaluating particle distributions within sectional images of tissues and cells by addressing the following questions: (1) is the observed distribution of particles between spatial compartments random? (2) Which compartments are preferentially targeted by particles? and (3) Does the observed particle distribution shift between different experimental groups? Each of these questions can be addressed by testing an appropriate null hypothesis. The methods all require observed particle distributions to be estimated by counting the number of particles associated with each defined compartment. For studying preferential labeling of compartments, the size of each of the compartments must also be estimated by counting the number of points of a randomly superimposed test grid that hit the different compartments. The latter provides information about the particle distribution that would be expected if the particles were randomly distributed, that is, the expected number of particles. From these data, we can calculate a relative deposition index (RDI) by dividing the observed number of particles by the expected number of particles. The RDI indicates whether the observed number of particles corresponds to that predicted solely by compartment size (for which RDI = 1). Within one group, the observed and expected particle distributions are compared by chi-squared analysis. The total chi-squared value indicates whether an observed distribution is random. If not, the partial chi-squared values help to identify those compartments that are preferential targets of the particles (RDI > 1). Particle distributions between different groups can be compared in a similar way by contingency table analysis. We first describe the preconditions and the way to implement these methods, then provide three worked examples, and finally discuss the advantages, pitfalls, and limitations of this method.

An efficient simulation algorithm for the generalized von Mises distribution of order two

In this article we propose an exact efficient simulation algorithm for the generalized von Mises circular distribution of order two. It is an acceptance-rejection algorithm with a piecewise linear envelope based on the local extrema and the inflexion points of the generalized von Mises density of order two. We show that these points can be obtained from the roots of polynomials and degrees four and eight, which can be easily obtained by the methods of Ferrari and Weierstrass. A comparative study with the von Neumann acceptance-rejection, with the ratio-of-uniforms and with a Markov chain Monte Carlo algorithms shows that this new method is generally the most efficient.

Multiasset Derivatives and Joint Distributions of Asset Prices

Several of multiasset derivatives like basket options or options on the weighted maximum of assets exhibit the property that their prices determine uniquely the underlying asset distribution. Related to that the question how to retrieve this distributions from the corresponding derivatives quotes will be discussed. On the contrary, the prices of exchange options do not uniquely determine the underlying distributions of asset prices and the extent of this non-uniqueness can be characterised. The discussion is related to a geometric interpretation of multiasset derivatives as support functions of convex sets. Following this, various symmetry properties for basket, maximum and exchange options are discussed alongside with their geometric interpretations and some decomposition results for more general payoff functions.

A re-examination of petrogenesis and 40Ar/39Ar systematics in the the Chain of Ponds K-feldspar: "diffusion domain" archetype versus polyphase hygrochronology

K-feldspar (Kfs) from the Chain of Ponds Pluton (CPP) is the archetypal reference material, on which thermochronological modeling of Ar diffusion in discrete “domains” was founded. We re-examine the CPP Kfs using cathodoluminescence and back-scattered electron imaging, transmission electron microscopy, and electron probe microanalysis. 40Ar/39Ar stepwise heating experiments on different sieve fractions, and on handpicked and unpicked aliquots, are compared. Our results reproduce the staircase-shaped age spectrum and the Arrhenius trajectory of the literature sample, confirming that samples collected from the same locality have an identical Ar isotope record. Even the most pristine-looking Kfs from the CPP contains successive generations of secondary, metasomatic/retrograde mineral replacements that post-date magmatic crystallization. These chemically and chronologically distinct phases are responsible for its staircase-shaped age spectra, which are modified by handpicking. While genuine within-grain diffusion gradients are not ruled out by these data, this study demonstrates that the most important control on staircase-shaped age spectra is the simultaneous presence of heterochemical, diachronous post-magmatic mineral growth. At least five distinct mineral species were identified in the Kfs separate, three of which can be traced to external fluids interacting with the CPP in a chemically open system. Sieve fractions have size-shifted Arrhenius trajectories, negating the existence of the smallest “diffusion domains”. Heterochemical phases also play an important role in producing non-linear trajectories. In vacuo degassing rates recovered from Arrhenius plots are neither related to true Fick’s Law diffusion nor to the staircase shape of the age spectra. The CPP Kfs used to define the "diffusion domain" model demonstrates the predominance of metasomatic alteration by hydrothermal fluids and recrystallization in establishing the natural Ar distribution amongst different coexisting phases that gives rise to the staircase-shaped age spectrum. Microbeam imaging of textures is as essential for 40Ar-39Ar hygrochronology as it is for U-Pb geochronology.

Measurement of the distributions of event-by-event flow harmonics in lead-lead collisions at = 2.76 TeV with the ATLAS detector at the LHC

The distributions of event-by-event harmonic flow coefficients v_n for n=2-4 are measured in sqrt(s_NN)=2.76 TeV Pb+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using charged particles with transverse momentum pT> 0.5 GeV and in the pseudorapidity range |eta|<2.5 in a dataset of approximately 7 ub^-1 recorded in 2010. The shapes of the v_n distributions are described by a two-dimensional Gaussian function for the underlying flow vector in central collisions for v_2 and over most of the measured centrality range for v_3 and v_4. Significant deviations from this function are observed for v_2 in mid-central and peripheral collisions, and a small deviation is observed for v_3 in mid-central collisions. It is shown that the commonly used multi-particle cumulants are insensitive to the deviations for v_2. The v_n distributions are also measured independently for charged particles with 0.51 GeV. When these distributions are rescaled to the same mean values, the adjusted shapes are found to be nearly the same for these two pT ranges. The v_n distributions are compared with the eccentricity distributions from two models for the initial collision geometry: a Glauber model and a model that includes corrections to the initial geometry due to gluon saturation effects. Both models fail to describe the experimental data consistently over most of the measured centrality range.