The CRaTER Special Issue of Space Weather: Building the observational foundation to deduce biological effects of space radiation

[1] The United States is preparing for exploration beyond low-Earth Orbit (LEO). However, the space radiation environment poses significant risks. The radiation hazard is potentially severe but not sufficiently well characterized to determine if long missions outside LEO can be accomplished with acceptable risk [Cucinotta et al., 2001; Schwadron et al., 2010; Cucinotta et al., 2010]. Radiation hazards may be over- or under-stated through incomplete characterization in terms of net quantities such as accumulated dose. Time-dependent characterization often changes acute risk estimates [NCRP, 1989; Cucinotta, 1999; Cucinotta et al., 2000; George et al., 2002]. For example, events with high accumulated doses but sufficiently low dose rates (/h) pose significantly reduced risks. Protons, heavy ions, and neutrons all contribute significantly to the radiation hazard. However, each form of radiation presents different biological effectiveness. As a result, quality factors and radiation-specific weighting factors are needed to assess biological effectiveness of different forms of radiation [e.g., NCRP 116, 1993] (Figure 1). More complete characterization must account for time-dependent radiation effects according to organ type, primary and secondary radiation composition, and acute effects (vomiting, sickness, and, at high exposures, death) versus chronic effects (such as cancer)

Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation

Two-locus sampling probabilities have played a central role in devising an efficient composite likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate estimates can be severely biased in certain cases of historical population size changes. To alleviate this problem, we develop here new methods to compute the sampling probability for variable population size functions that are piecewise constant. Our main theoretical result, implemented in a new software package called LDpop, is a novel formula for the sampling probability that can be evaluated by numerically exponentiating a large but sparse matrix. This formula can handle moderate sample sizes ($n \leq 50$) and demographic size histories with a large number of epochs ($\mathcal{D} \geq 64$). In addition, LDpop implements an approximate formula for the sampling probability that is reasonably accurate and scales to hundreds in sample size ($n \geq 256$). Finally, LDpop includes an importance sampler for the posterior distribution of two-locus genealogies, based on a new result for the optimal proposal distribution in the variable-size setting. Using our methods, we study how a sharp population bottleneck followed by rapid growth affects the correlation between partially linked sites. Then, through an extensive simulation study, we show that accounting for population size changes under such a demographic model leads to substantial improvements in fine-scale recombination rate estimation. LDpop is freely available for download at https://github.com/popgenmethods/ldpopComment: 32 pages, 13 figure

On the solar wind control of cusp aurora during northward IMF

[1] The location of cusp aurora during northward interplanetary magnetic field (IMF) conditions and the solar wind control of that location are explored. The cusp aurora is imaged by the Imager for Magnetopause-to-Aurora Global Exploration\u27s (IMAGE) Far Ultraviolet Instrument (FUV). Predicted locations of the cusp aurora were computed by assuming anti-parallel reconnection between the observed IMF orientation and the 1996 Tsyganenko model magnetopause magnetic field. While the majority of anti-parallel reconnection sites tailward of the cusp, when mapped to the ionosphere, coincide with the observed cusp aurora, the anti-parallel merging hypothesis cannot explain certain aspects of the observations, including its location dependence with IMF + By

Morphology and the gradient of a symmetric potential predicts gait transitions of dogs

Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore-aft, and spatio-temporal symmetry groups. We tested the ability of symmetries to model motion capture data of dogs walking, trotting and transitioning between those gaits. Fully symmetric models performed comparably to asymmetric with only a 22% increase in the residual sum of squares and only one-quarter of the parameters. This required adding a spatio-temporal shift representing a lag between fore and hind limbs. Without this shift, the symmetric model residual sum of squares was 1700% larger. This shift is related to (linear regression, n = 5, p = 0.0328) dog morphology. That this symmetry is respected throughout the gaits and transitions indicates that it generalizes outside a single gait. We propose that relative phasing of limb motions can be described by an interaction potential with a symmetric structure. This approach can be extended to the study of interaction of neurodynamic and kinematic variables, providing a system-level model that couples neuronal central pattern generator networks and mechanical models

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our framework can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.Comment: 9 pages, 8 figure

Ion observations from geosynchronous orbit as a proxy for ion cyclotron wave growth during storm times

[1] There is still much to be understood about the processes contributing to relativistic electron enhancements and losses in the radiation belts. Wave particle interactions with both whistler and electromagnetic ion cyclotron (EMIC) waves may precipitate or accelerate these electrons. This study examines the relation between EMIC waves and resulting relativistic electron flux levels after geomagnetic storms. A proxy for enhanced EMIC waves is developed using Los Alamos National Laboratory Magnetospheric Plasma Analyzer plasma data from geosynchronous orbit in conjunction with linear theory. In a statistical study using superposed epoch analysis, it is found that for storms resulting in net relativistic electron losses, there is a greater occurrence of enhanced EMIC waves. This is consistent with the hypothesis that EMIC waves are a primary mechanism for the scattering of relativistic electrons and thus cause losses of such particles from the magnetosphere