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The origin, evolution, and cycling of volatiles on the Moon are established by processes such as the giant moon forming impact, degassing of the lunar magma ocean, degassing during surface eruptions, and lunar surface gardening events. These processes typically induce mass‐dependent stable isotope fractionations. Mass‐independent fractionation of stable isotopes has yet to be demonstrated during events that release large volumes of gas on the moon and establish transient lunar atmospheres. We present quadruple sulfur isotope compositions of orange and black glass beads from drive tube 74002/1. The sulfur isotope and concentration data collected on the orange and black glasses confirm a role for magmatic sulfur loss during eruption. The Δ³³S value of the orange glasses is homogenous (Δ³³S = −0.029‰ ± 0.004‰, 2SE) and different from the isotopic composition of lunar basalts (Δ³³S = 0.002‰ ± 0.004‰, 2SE). We link the negative Δ³³S composition of the orange glasses to an anomalous sulfur source in the lunar mantle. The nature of this anomalous sulfur source remains unknown and is either linked to (a) an impactor that delivered anomalous sulfur after late accretion, (b) sulfur that was photochemically processed early during lunar evolution and was transported to the lunar mantle, or (c) a primitive sulfur component that survived mantle mixing. The examined black glass preserves a mass‐dependent Δ³³S composition (−0.008‰ ± 0.006‰, 2SE). The orange and black glasses are considered genetically related, but the discrepancy in Δ³³S composition among the two samples calls their relationship into question.

 

Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call "candidate genes", by evaluating the ability of gene combinations to classify samples from a dataset, which we call "classification potential". Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.