25 26A long-standing conceptual model for deep submarine eruptions is that high hydrostatic pressure 27 hinders degassing and acceleration, and suppresses magma fragmentation. The 2012 submarine 28 rhyolite eruption of Havre volcano in the Kermadec arc provided constraints on critical 29 parameters to quantitatively test these concepts. This eruption produced a > 1 km 3 raft of floating 30 pumice and a 0.1 km 3 field of giant (>1 m) pumice clasts distributed down-current from the vent. 31We address the mechanism of creating these clasts using a model for magma ascent in a conduit. 32We use water ingestion experiments to address why some clasts float and others sink. We show 33 that at the eruption depth of 900 m, the melt retained enough dissolved water, and hence had a 34 low enough viscosity, that strain-rates were too low to cause brittle fragmentation in the conduit, 35 despite mass discharge rates similar to Plinian eruptions on land. There was still, however, 36 enough exsolved vapor at the vent depth to make the magma buoyant relative to seawater. 37Buoyant magma was thus extruded into the ocean where it rose, quenched, and fragmented to 38 produce clasts up to several meters in diameter. We show that these large clasts would have 39 floated to the sea surface within minutes, where air could enter pore space, and the fate of clasts 40 is then controlled by the ability to trap gas within their pore space. We show that clasts from the 41 raft retain enough gas to remain afloat whereas fragments from giant pumice collected from the 42 seafloor ingest more water and sink. The pumice raft and the giant pumice seafloor deposit were 43 thus produced during a clast-generating effusive submarine eruption, where fragmentation 44 occurred above the vent, and the subsequent fate of clasts was controlled by their ability to ingest 45 water. 46 3 47
One goal of single-cell RNA sequencing (scRNA seq) is to expose possible heterogeneity within cell populations due to meaningful, biological variation. Examining cell-to-cell heterogeneity, and further, identifying subpopulations of cells based on scRNA seq data has been of common interest in life science research. A key component to successfully identifying cell subpopulations (or clustering cells) is the (dis)similarity measure used to group the cells. In this paper, we introduce a novel measure, named SIDEseq, to assess cell-to-cell similarity using scRNA seq data. SIDEseq first identifies a list of putative differentially expressed (DE) genes for each pair of cells. SIDEseq then integrates the information from all the DE gene lists (corresponding to all pairs of cells) to build a similarity measure between two cells. SIDEseq can be implemented in any clustering algorithm that requires a (dis)similarity matrix. This new measure incorporates information from all cells when evaluating the similarity between any two cells, a characteristic not commonly found in existing (dis)similarity measures. This property is advantageous for two reasons: (a) borrowing information from cells of different subpopulations allows for the investigation of pairwise cell relationships from a global perspective and (b) information from other cells of the same subpopulation could help to ensure a robust relationship assessment. We applied SIDEseq to a newly generated human ovarian cancer scRNA seq dataset, a public human embryo scRNA seq dataset, and several simulated datasets. The clustering results suggest that the SIDEseq measure is capable of uncovering important relationships between cells, and outperforms or at least does as well as several popular (dis)similarity measures when used on these datasets.
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