Variations in mid-ocean ridge CO2 emissions driven by glacial cycles

The geological record shows links between glacial cycles and volcanic productivity, both subaerially and at mid-ocean ridges. Sea-level-driven pressure changes could also affect chemical properties of mid-ocean ridge volcanism. We consider how changing sea-level could alter the CO2 emissions rate from mid-ocean ridges, on both the segment and global scale. We develop a simplified transport model for a highly incompatible element through a homogenous mantle; variations in the melt concentration the emission rate of the element are created by changes in the depth of first silicate melting. The model predicts an average global mid-ocean ridge CO2 emissions-rate of 53 Mt/yr, in line with other estimates. We show that falling sea level would cause an increase in ridge CO2 emissions with a lag of about 100 kyrs after the causative sea level change. The lag and amplitude of the response are sensitive to mantle permeability and plate spreading rate. For a reconstructed sea-level time series of the past million years, we predict variations of up to 12% (7 Mt/yr) in global mid-ocean ridge CO2 emissions. The magnitude and timing of the predicted variations in CO2 emissions suggests a potential role for ridge carbon emissions in glacial cycles

Standoff Detection via Single-Beam Spectral Notch Filtered Pulses

We demonstrate single-beam coherent anti-Stokes Raman spectroscopy (CARS), for detecting and identifying traces of solids, including minute amounts of explosives, from a standoff distance (>50 m) using intense femtosecond pulses. Until now, single-beam CARS methods relied on pulse-shapers in order to obtain vibrational spectra. Here we present a simple and easy-to-implement detection scheme, using a commercially available notch filter, that does not require the use of a pulse-shaper.Comment: 3 pages, 3 figure

A novel method for RNA extraction from FFPE samples reveals significant differences in biomarker expression between orthotopic and subcutaneous pancreatic cancer patient-derived xenografts.

Next-generation sequencing (NGS) can identify and validate new biomarkers of cancer onset, progression and therapy resistance. Substantial archives of formalin-fixed, paraffin-embedded (FFPE) cancer samples from patients represent a rich resource for linking molecular signatures to clinical data. However, performing NGS on FFPE samples is limited by poor RNA purification methods. To address this hurdle, we developed an improved methodology for extracting high-quality RNA from FFPE samples. By briefly integrating a newly-designed micro-homogenizing (mH) tool with commercially available FFPE RNA extraction protocols, RNA recovery is increased by approximately 3-fold while maintaining standard A260/A280 ratios and RNA quality index (RQI) values. Furthermore, we demonstrate that the mH-purified FFPE RNAs are longer and of higher integrity. Previous studies have suggested that pancreatic ductal adenocarcinoma (PDAC) gene expression signatures vary significantly under in vitro versus in vivo and in vivo subcutaneous versus orthotopic conditions. By using our improved mH-based method, we were able to preserve established expression patterns of KRas-dependency genes within these three unique microenvironments. Finally, expression analysis of novel biomarkers in KRas mutant PDAC samples revealed that PEAK1 decreases and MST1R increases by over 100-fold in orthotopic versus subcutaneous microenvironments. Interestingly, however, only PEAK1 levels remain elevated in orthotopically grown KRas wild-type PDAC cells. These results demonstrate the critical nature of the orthotopic tumor microenvironment when evaluating the clinical relevance of new biomarkers in cells or patient-derived samples. Furthermore, this new mH-based FFPE RNA extraction method has the potential to enhance and expand future FFPE-RNA-NGS cancer biomarker studies

Thermodynamics with long-range interactions: from Ising models to black-holes

New methods are presented which enables one to analyze the thermodynamics of systems with long-range interactions. Generically, such systems have entropies which are non-extensive, (do not scale with the size of the system). We show how to calculate the degree of non-extensivity for such a system. We find that a system interacting with a heat reservoir is in a probability distribution of canonical ensembles. The system still possesses a parameter akin to a global temperature, which is constant throughout the substance. There is also a useful quantity which acts like a {\it local temperatures} and it varies throughout the substance. These quantities are closely related to counterparts found in general relativity. A lattice model with long-range spin-spin coupling is studied. This is compared with systems such as those encountered in general relativity, and gravitating systems with Newtonian-type interactions. A long-range lattice model is presented which can be seen as a black-hole analog. One finds that the analog's temperature and entropy have many properties which are found in black-holes. Finally, the entropy scaling behavior of a gravitating perfect fluid of constant density is calculated. For weak interactions, the entropy scales like the volume of the system. As the interactions become stronger, the entropy becomes higher near the surface of the system, and becomes more area-scaling.Comment: Corrects some typos found in published version. Title changed 22 pages, 2 figure

Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems

As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe. This paper presents a set of backward reachability approaches for safety certification of neural feedback loops (NFLs), i.e., closed-loop systems with NN control policies. While backward reachability strategies have been developed for systems without NN components, the nonlinearities in NN activation functions and general noninvertibility of NN weight matrices make backward reachability for NFLs a challenging problem. To avoid the difficulties associated with propagating sets backward through NNs, we introduce a framework that leverages standard forward NN analysis tools to efficiently find over-approximations to backprojection (BP) sets, i.e., sets of states for which an NN policy will lead a system to a given target set. We present frameworks for calculating BP over approximations for both linear and nonlinear systems with control policies represented by feedforward NNs and propose computationally efficient strategies. We use numerical results from a variety of models to showcase the proposed algorithms, including a demonstration of safety certification for a 6D system.Comment: 17 pages, 15 figures. Journal extension of arXiv:2204.0831