Rb-Sr and Sm-Nd Studies of Olivine-Phyric Shergottites RBT 04262 and LAR 06319: Isotopic Evidence for Relationship to Enriched Basaltic Shergottites

RBT 04262 and LAR 06319 are two Martian meteorites recently discovered in Antarctica. Both contain abundant olivines, and were classified as olivine-phyric shergottites. A detailed petrographic study of RBT 04262 suggested it should be reclassified as a lherzolitic shergottite. However, the moderately LREE-depleted REE distribution pattern indicated that it is closely related to enriched basaltic shergottites like Shergotty, Zagami, Los Angeles, etc. In earlier studies of a similarly olivinephyric shergottite NWA 1068 which contains 21% modal olivine, it was shown that it probably was produced from an enriched basaltic shergottite magma by olivine accumulation . As for LAR 06319, recent petrographic studies suggested that it is different from either lherzolitic shergottites or the highly LREE-depleted olivine-phyric shergottites. We performed Rb-Sr and Sm-Nd isotopic analyses on RBT 04262 and LAR 06319 to determine their crystallization ages and Sr and Nd isotopic signatures, and to better understand the petrogenetic relationships between them and other basaltic, lherzolitic and depleted olivine-phyric shergottites

Chlorine Isotopes: As a Possible Tracer of Fluid/Bio-Activities on Mars and a Progress Report on Chlorine Isotope Analysis by TIMs

Significantly large mass fractionations between chlorine isotopes (Cl-35, Cl-37) have been reported for terrestrial materials including both geological samples and laboratory materials. Also, the chlorine isotopic composition can be used as a tracer for early solar system processes. Moreover, chlorine is ubiquitous on the Martian surface. Typical chlorine abundances in Gusev soils are approx.0.5 %. The global surface average chlorine abundance also is approx.0.5 %. Striking variations among outcrop rocks at Meridiani were reported with some chlorine abundances as high as approx.2%. Characterizing conditions under which chlorine isotopic fractionation may occur is clearly of interest to planetary science. Thus, we have initiated development of a chlorine isotopic analysis technique using TIMS at NASA-JSC. We present here a progress report on the current status of development at JSC and discuss the possible application of chlorine isotopic analysis to Martian meteorites in a search for fluid- and possibly biological activity on Mars

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types