Determining gypsum growth temperatures using monophase fluid inclusions-Application to the giant gypsum crystals of Naica, Mexico

Determining the formation temperature of minerals using fluid inclusions is a crucial step in understanding rock-forming scenarios. Unfortunately, fluid inclusions in minerals formed at low temperature, such as gypsum, are commonly in a metastable monophase liquid state. To overcome this problem, ultra-short laser pulses can be used to induce vapor bubble nucleation, thus creating a stable two-phase fluid inclusion appropriate for subsequent measurements of the liquid-vapor homogenization temperature, T-h. In this study we evaluate the applicability of T-h data to accurately determine gypsum formation temperatures. We used fluid inclusions in synthetic gypsum crystals grown in the laboratory at different temperatures between 40 degrees C and 80 degrees C under atmospheric pressure conditions. We found an asymmetric distribution of the T-h values, which are systematically lower than the actual crystal growth temperatures, T-g; this is due to (1) the effect of surface tension on liquid-vapor homogenization, and (2) plastic deformation of the inclusion walls due to internal tensile stress occurring in the metastable state of the inclusions. Based on this understanding, we have determined growth temperatures of natural giant gypsum crystals from Naica (Mexico), yielding 47 +/- 1.5 degrees C for crystals grown in the Cave of Swords (120 m below surface) and 54.5 +/- 2 degrees C for giant crystals grown in the Cave of Crystals (290 m below surface). These results support the earlier hypothesis that the population and the size of the Naica crystals were controlled by temperature. In addition, this experimental method opens a door to determining the growth temperature of minerals forming in low-temperature environments.

Monotone Constrained Tensor-product B-spline with application to screening studies

When different markers are responsive to different aspects of a disease, combination of multiple markers could provide a better screening test for early detection. It is also resonable to assume that the risk of disease changes smoothly as the biomarker values change and the change in risk is monotone with respect to each biomarker. In this paper, we propose a boundary constrained tensor-product B-spline method to estimate the risk of disease by maximizing a penalized likelihood. To choose the optimal amount of smoothing, two scores are proposed which are extensions of the GCV score (O'Sullivan et al. (1986)) and the GACV score (Ziang and Wahba (1996)) to incorporate linear constraints. Simulation studies are carried out to investigate the performance of the proposed estimator and the selection scores. In addidtion, sensitivities and specificities based ona pproximate leave-one-out estimates are proposed to generate more realisitc ROC curves. Data from a pancreatic cancer study is used for illustration.

Smooth Estimation of a Monotone Density

We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a sample of observations. The nonparametric maximum likelihood estimator of a decreasing density on the positive half line attains a rate of convergence at a fixed point if the density has a negative derivative. The same rate is obtained by a kernel estimator, but the limit distributions are different. If the density is both differentiable and known to be monotone, then a third estimator is obtained by isotonization of a kernel estimator. We show that this again attains the rate of convergence and compare the limit distributors of the three types of estimators. It is shown that both isotonization and smoothing lead to a more concentrated limit distribution and we study the dependence on the proportionality constant in the bandwidth. We also show that isotonization does not change the limit behavior of a kernel estimator with a larger bandwidth, in the case that the density is known to have more than one derivative.