Dynamic temperature selection for parallel-tempering in Markov chain Monte Carlo simulations

Modern problems in astronomical Bayesian inference require efficient methods for sampling from complex, high-dimensional, often multi-modal probability distributions. Most popular methods, such as Markov chain Monte Carlo sampling, perform poorly on strongly multi-modal probability distributions, rarely jumping between modes or settling on just one mode without finding others. Parallel tempering addresses this problem by sampling simultaneously with separate Markov chains from tempered versions of the target distribution with reduced contrast levels. Gaps between modes can be traversed at higher temperatures, while individual modes can be efficiently explored at lower temperatures. In this paper, we investigate how one might choose the ladder of temperatures to achieve more efficient sampling, as measured by the autocorrelation time of the sampler. In particular, we present a simple, easily-implemented algorithm for dynamically adapting the temperature configuration of a sampler while sampling. This algorithm dynamically adjusts the temperature spacing to achieve a uniform rate of exchanges between chains at neighbouring temperatures. We compare the algorithm to conventional geometric temperature configurations on a number of test distributions and on an astrophysical inference problem, reporting efficiency gains by a factor of 1.2-2.5 over a well-chosen geometric temperature configuration and by a factor of 1.5-5 over a poorly chosen configuration. On all of these problems a sampler using the dynamical adaptations to achieve uniform acceptance ratios between neighbouring chains outperforms one that does not.Comment: 21 pages, 21 figure

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

Searching for stochastic gravitational waves using data from the two colocated LIGO Hanford detectors

Searches for a stochastic gravitational-wave background (SGWB) using terrestrial detectors typically involve cross-correlating data from pairs of detectors. The sensitivity of such cross-correlation analyses depends, among other things, on the separation between the two detectors: the smaller the separation, the better the sensitivity. Hence, a colocated detector pair is more sensitive to a gravitational-wave background than a noncolocated detector pair. However, colocated detectors are also expected to suffer from correlated noise from instrumental and environmental effects that could contaminate the measurement of the background. Hence, methods to identify and mitigate the effects of correlated noise are necessary to achieve the potential increase in sensitivity of colocated detectors. Here we report on the first SGWB analysis using the two LIGO Hanford detectors and address the complications arising from correlated environmental noise. We apply correlated noise identification and mitigation techniques to data taken by the two LIGO Hanford detectors, H1 and H2, during LIGO’s fifth science run. At low frequencies, 40–460 Hz, we are unable to sufficiently mitigate the correlated noise to a level where we may confidently measure or bound the stochastic gravitational-wave signal. However, at high frequencies, 460–1000 Hz, these techniques are sufficient to set a 95% confidence level upper limit on the gravitational-wave energy density of Ω(f) < 7.7 × 10[superscript -4](f/900  Hz)[superscript 3], which improves on the previous upper limit by a factor of ~180. In doing so, we demonstrate techniques that will be useful for future searches using advanced detectors, where correlated noise (e.g., from global magnetic fields) may affect even widely separated detectors.National Science Foundation (U.S.)United States. National Aeronautics and Space AdministrationCarnegie TrustDavid & Lucile Packard FoundationAlfred P. Sloan Foundatio

dfm/emcee: emcee v3.0.1

Added support for long double dtypes Prepared manuscript to submit to JOSS Improved packaging and release infrastructure Fixed bug in initial linear dependence tes

SAFB1 interacts with and suppresses the transcriptional activity of p53

A significant amount of nuclear p53 is found associated with the nuclear matrix in cells that were exposed to genotoxic stress. In this study we identified Scaffold attachment factor B1 (SAFB1), a nuclear matrix-associated protein that binds the scaffold or matrix attachment regions (S/MARs) of genomic DNA, as a novel p53-interacting protein. SAFB1 was able to associate with p53 through its C-terminal domain, while significant co-localization of the two proteins was observed in cells treated with 5-fluorouracil or mithramycin. Binding of p53 to SAFB1 had a significant functional outcome, since SAFB1 was shown to suppress p53-mediated reporter gene expression. These data suggest that nuclear matrix-associated proteins may play a critical role in regulating p53 localization and activity. Structured summary: p53 physically interacts with SRPK1a:shown by two hybrid (view interaction) p53 physically interacts with SRPK1a:shown by pull down (view interaction) p53 physically interacts with SRPK1a:shown by anti bait coimmunoprecipitation (view interaction) p53 physically interacts with SRPK1a:shown by anti tag coimmunoprecipitation (view interaction) SAFB1 physically interacts with p53:shown by pull down (view interactions 1, 2) SAFB1 physically interacts with p53:shown by anti bait coimmunoprecipitation (view interactions 1, 2) SAFB1 and p53 colocalize:shown by fluorescence microscopy (view interaction) SAFB2 physically interacts with p53:shown by pull down (view interaction) (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserved

corner.py: corner.py v2.0.0

Version 2 of corner.py is now tested, documented, and citable

Corner.Py: Corner.Py V2.0.0

Version 2 of corner.py is now tested, documented, and citable.</span