Stellar wind-magnetosphere interaction at exoplanets: computations of auroral radio powers

We present calculations of the auroral radio powers expected from exoplanets with magnetospheres driven by an Earth-like magnetospheric interaction with the solar wind. Specifically, we compute the twin cell-vortical ionospheric flows, currents, and resulting radio powers resulting from a Dungey cycle process driven by dayside and nightside magnetic reconnection, as a function of planetary orbital distance and magnetic field strength. We include saturation of the magnetospheric convection, as observed at the terrestrial magnetosphere, and we present power law approximations for the convection potentials, radio powers and spectral flux densities. We specifically consider a solar-age system and a young (1 Gyr) system. We show that the radio power increases with magnetic field strength for magnetospheres with saturated convection potential, and broadly decreases with increasing orbital distance. We show that the magnetospheric convection at hot Jupiters will be saturated, and thus unable to dissipate the full available incident Poynting flux, such that the magnetic Radiometric Bode's Law (RBL) presents a substantial overestimation of the radio powers for hot Jupiters. Our radio powers for hot Jupiters are $\sim$5-1300 TW for hot Jupiters with field strengths of 0.1-10 $B_J$ orbiting a Sun-like star, while we find that competing effects yield essentially identical powers for hot Jupiters orbiting a young Sun-like star. However, in particular for planets with weaker magnetic fields our powers are higher at larger orbital distances than given by the RBL, and there are many configurations of planet that are expected to be detectable using SKA.Comment: Accepted for publication in Mon. Not. R. Astron. So

NAIP/NLRC4 inflammasome activation in MRP8+ cells is sufficient to cause systemic inflammatory disease.

Inflammasomes are cytosolic multiprotein complexes that initiate protective immunity in response to infection, and can also drive auto-inflammatory diseases, but the cell types and signalling pathways that cause these diseases remain poorly understood. Inflammasomes are broadly expressed in haematopoietic and non-haematopoietic cells and can trigger numerous downstream responses including production of IL-1β, IL-18, eicosanoids and pyroptotic cell death. Here we show a mouse model with endogenous NLRC4 inflammasome activation in Lysozyme2 + cells (monocytes, macrophages and neutrophils) in vivo exhibits a severe systemic inflammatory disease, reminiscent of human patients that carry mutant auto-active NLRC4 alleles. Interestingly, specific NLRC4 activation in Mrp8 + cells (primarily neutrophil lineage) is sufficient to cause severe inflammatory disease. Disease is ameliorated on an Asc -/- background, and can be suppressed by injections of anti-IL-1 receptor antibody. Our results provide insight into the mechanisms by which NLRC4 inflammasome activation mediates auto-inflammatory disease in vivo

A guideline for heavy ion radiation testing for Single Event Upset (SEU)

A guideline for heavy ion radiation testing for single event upset was prepared to assist new experimenters in preparing and directing tests. How to estimate parts vulnerability and select an irradiation facility is described. A broad brush description of JPL equipment is given, certain necessary pre-test procedures are outlined and the roles and testing guidelines for on-site test personnel are indicated. Detailed descriptions of equipment needed to interface with JPL test crew and equipment are not provided, nor does it meet the more generalized and broader requirements of a MIL-STD document. A detailed equipment description is available upon request, and a MIL-STD document is in the early stages of preparation

Dynamic filtering of static dipoles in magnetoencephalography

We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem and model the neural current as a collection of evolving current dipoles, which appear and disappear, but whose locations are constant throughout their lifetime. This fully reflects the physiological interpretation of the model. In order to conduct inference under this proposed model, it was necessary to develop an algorithm based around state-of-the-art sequential Monte Carlo methods employing carefully designed importance distributions. Previous work employed a bootstrap filter and an artificial dynamic structure where dipoles performed a random walk in space, yielding nonphysical artefacts in the reconstructions; such artefacts are not observed when using the proposed model. The algorithm is validated with simulated data, in which it provided an average localisation error which is approximately half that of the bootstrap filter. An application to complex real data derived from a somatosensory experiment is presented. Assessment of model fit via marginal likelihood showed a clear preference for the proposed model and the associated reconstructions show better localisation

Weak Hopf algebras corresponding to Cartan matrices

We replace the group of group-like elements of the quantized enveloping algebra $U_q({\frak{g}})$ of a finite dimensional semisimple Lie algebra ${\frak g}$ by some regular monoid and get the weak Hopf algebra ${\frak{w}}_q^{\sf d}({\frak g})$. It is a new subclass of weak Hopf algebras but not Hopf algebras. Then we devote to constructing a basis of ${\frak{w}}_q^{\sf d}({\frak g})$ and determine the group of weak Hopf algebra automorphisms of ${\frak{w}}_q^{\sf d}({\frak g})$ when $q$ is not a root of unity.Comment: 21 page

Cluster Mass Inference Method via Random Field Theory

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, extend it to Student’s t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single-subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test

Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects metaregression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM

Microdosing psychedelics: More questions than answers? An overview and suggestions for future research

Background: In the past few years, the issue of \u2018microdosing\u2019 psychedelics has been openly discussed in the public arena where claims have been made about their positive effect on mood state and cognitive processes such as concentration. However, there are very few scientific studies that have specifically addressed this issue, and there is no agreed scientific consensus on what microdosing is. Aim: This critique paper is designed to address questions that need to be answered by future scientific studies and to offer guidelines for these studies. Approach: Owing to its proximity for a possible approval in clinical use and short-lasting pharmacokinetics, our focus is predominantly on psilocybin. Psilocybin is allegedly, next to lysergic acid diethylamide (LSD), one of the two most frequently used psychedelics to microdose. Where relevant and available, data for other psychedelic drugs are also mentioned. Conclusion: It is concluded that while most anecdotal reports focus on the positive experiences with microdosing, future research should also focus on potential risks of (multiple) administrations of a psychedelic in low doses. To that end, (pre)clinical studies including biological (e.g. heart rate, receptor turnover and occupancy) as well as cognitive (e.g. memory, attention) parameters have to be conducted and will shed light on the potential negative consequences microdosing could have

Radiation effects on silicon solar cells Final report, Dec. 1, 1961 - Dec. 31, 1962

Displacement defects in silicon solar cells by high energy electron irradiation using electron spin resonance, galvanometric, excess carrier lifetime, and infrared absorption measurement

A novel framework for quantifying past methane recycling by Sphagnum-methanotroph symbiosis using carbon and hydrogen isotope ratios of leaf wax biomarkers

The concentration of atmospheric methane is strongly linked to variations in Earth's climate. Currently, we can directly reconstruct the total atmospheric concentration of methane, but not individual terms of the methane cycle. Northern wetlands, dominated by Sphagnum, are an important contributor of atmospheric methane, and we seek to understand the methane cycle in these systems. We present a novel method for quantifying the proportion of carbon Sphagnum assimilates from its methanotrophic symbionts using stable isotope ratios of leaf-wax biomarkers. Carbon isotope ratios of Sphagnum compounds are determined by two competing influences, water content and the isotope ratio of source carbon. We disentangled these effects using a combined hydrogen and carbon isotope approach. We constrained Sphagnum water content using the contrast between the hydrogen isotope ratios of Sphagnum and vascular plant biomarkers. We then used Sphagnum water content to calculate the carbon isotope ratio of Sphagnum's carbon pool. Using a mass balance equation, we calculated the proportion of recycled methane contributed to the Sphagnum carbon pool, “PRM.” We quantified PRM in peat monoliths from three microhabitats in the Mer Bleue peatland complex. Modern studies have shown that water table depth and vegetation have strong influences on the peatland methane cycle on instrumental time scales. With this new approach, δ13C of Sphagnum compounds are now a useful tool for investigating the relationships among hydrology, vegetation, and methanotrophy in Sphagnum peatlands over the time scales of entire peatland sediment records, vital to our understanding of the global carbon cycle through the Late Glacial and Holocene