Unstable coronal loops : numerical simulations with predicted observational signatures

We present numerical studies of the nonlinear, resistive magnetohydrodynamic (MHD) evolution of coronal loops. For these simulations we assume that the loops carry no net current, as might be expected if the loop had evolved due to vortex flows. Furthermore the initial equilibrium is taken to be a cylindrical flux tube with line-tied ends. For a given amount of twist in the magnetic field it is well known that once such a loop exceeds a critical length it becomes unstableto ideal MHD instabilities. The early evolution of these instabilities generates large current concentrations. Firstly we show that these current concentrations are consistent with the formation of a current sheet. Magnetic reconnection can only occur in the vicinity of these current concentrations and we therefore couple the resistivity to the local current density. This has the advantage of avoiding resistive diffusion in regions where it should be negligible. We demonstrate the importance of this procedure by comparison with simulations based on a uniform resistivity. From our numerical experiments we are able to estimate some observational signatures for unstable coronal loops. These signatures include: the timescale of the loop brightening; the temperature increase; the energy released and the predicted observable flow speeds. Finally we discuss to what extent these observational signatures are consistent with the properties of transient brightening loops.Comment: 13 pages, 9 figure

Numerical simulation of two-dimensional single- and multiple-material flow fields

Over the last several years, Sandia National Laboratories has had an interest in developing capabilities to predict the flow fields around vehicles entering or exiting the water at a wide range of speeds. Such prediction schemes have numerous engineering applications in the design of weapon systems. For example, such a scheme could be used to predict the forces and moments experienced by an air-launched anti-submarine weapon on water-entry. Furthermore, a water-exit prediction capability could be used to model the complicated surface closure jet resulting from a missile being shot out of the water. The CCICE (Cell-Centered Implicit Continuous-fluid Eulerian) code developed at Los Alamos National Laboratory (LANL) was chosen to provide the fluid dynamics solver for high speed water-entry and water-exit problems. This implicit time-marching, two-dimensional, conservative, finite-volume code solves the multi-material, compressible, inviscid fluid dynamics equations. The incompressible version of the CCICE code, CCMAC (cell-Centered Marker and Cell), was chosen for low speed water- entry and water-exit problems in order to reduce the computational expense. These codes were chosen to take advantage of certain advances in numerical methods for computational fluid dynamics (CFD) that have taken place at LANL. Notable among these advances is the ability to perform implicit, multi-material, compressible flow simulations, with a fully cell-centered data structure. This means that a single set of control volumes are used, on which a discrete form of the conservation laws is satisfied. This is in control to the more classical staggered mesh methods, in which separate control volumes are defined for mass and momentum. 12 refs

The Flare-energy Distributions Generated by Kink-unstable Ensembles of Zero-net-current Coronal Loops

It has been proposed that the million degree temperature of the corona is due to the combined effect of barely-detectable energy releases, so called nanoflares, that occur throughout the solar atmosphere. Alas, the nanoflare density and brightness implied by this hypothesis means that conclusive verification is beyond present observational abilities. Nevertheless, we investigate the plausibility of the nanoflare hypothesis by constructing a magnetohydrodynamic (MHD) model that can derive the energy of a nanoflare from the nature of an ideal kink instability. The set of energy-releasing instabilities is captured by an instability threshold for linear kink modes. Each point on the threshold is associated with a unique energy release and so we can predict a distribution of nanoflare energies. When the linear instability threshold is crossed, the instability enters a nonlinear phase as it is driven by current sheet reconnection. As the ensuing flare erupts and declines, the field transitions to a lower energy state, which is modelled by relaxation theory, i.e., helicity is conserved and the ratio of current to field becomes invariant within the loop. We apply the model so that all the loops within an ensemble achieve instability followed by energy-releasing relaxation. The result is a nanoflare energy distribution. Furthermore, we produce different distributions by varying the loop aspect ratio, the nature of the path to instability taken by each loop and also the level of radial expansion that may accompany loop relaxation. The heating rate obtained is just sufficient for coronal heating. In addition, we also show that kink instability cannot be associated with a critical magnetic twist value for every point along the instability threshold

Somatic mutations and clonal hematopoiesis in congenital neutropenia

© 2018 by The American Society of Hematology. Severe congenital neutropenia (SCN) and Shwachman-Diamond syndrome (SDS) are congenital neutropenia syndromes with a high rate of leukemic transformation. Hematopoietic stressors may contribute to leukemic transformation by increasing the mutation rate in hematopoietic stem/progenitor cells (HSPCs) and/or by promoting clonal hematopoiesis. We sequenced the exome of individual hematopoietic colonies derived from 13 patients with congenital neutropenia to measure total mutation burden and performed error-corrected sequencing on a panel of 46 genes on 80 patients with congenital neutropenia to assess for clonal hematopoiesis. An average of 3.6 ± 1.2 somatic mutations per exome was identified in HSPCs from patients with SCN compared with 3.960.4 for healthy controls (P = NS). Clonal hematopoiesis due to mutations in TP53 was present in 48% (13/ 27) of patients with SDS but was not seen in healthy controls (0/17, P \u3c .001) or patients with SCN (0/40, P \u3c .001). Our SDS cohort was young (median age 6.3 years), and many of the patients had multiple TP53 mutations. Conversely, clonal hematopoiesis due to mutations of CSF3R was present in patients with SCN but was not detected in healthy controls or patients with SDS. These data show that hematopoietic stress, including granulocyte colony-stimulating factor, do not increase the mutation burden in HSPCs in congenital neutropenia. Rather, distinct hematopoietic stressors result in the selective expansion of HSPCs carrying specific gene mutations. In particular, in SDS there is enormous selective pressure to expand TP53-mutated HSPCs, suggesting that acquisition of TP53 mutations is an early, likely initiating event, in the transformation to myelodysplastic syndrome/acute myeloid leukemia in patients with SDS

Incorporating prior knowledge improves detection of differences in bacterial growth rate

BACKGROUND: Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. RESULTS: We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. CONCLUSIONS: We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate

Deterministically Driven Avalanche Models of Solar Flares

We develop and discuss the properties of a new class of lattice-based avalanche models of solar flares. These models are readily amenable to a relatively unambiguous physical interpretation in terms of slow twisting of a coronal loop. They share similarities with other avalanche models, such as the classical stick--slip self-organized critical model of earthquakes, in that they are driven globally by a fully deterministic energy loading process. The model design leads to a systematic deficit of small scale avalanches. In some portions of model space, mid-size and large avalanching behavior is scale-free, being characterized by event size distributions that have the form of power-laws with index values, which, in some parameter regimes, compare favorably to those inferred from solar EUV and X-ray flare data. For models using conservative or near-conservative redistribution rules, a population of large, quasiperiodic avalanches can also appear. Although without direct counterparts in the observational global statistics of flare energy release, this latter behavior may be relevant to recurrent flaring in individual coronal loops. This class of models could provide a basis for the prediction of large solar flares.Comment: 24 pages, 11 figures, 2 tables, accepted for publication in Solar Physic

Role of chance and history during evolution in Chlamydomonas reinhardtii

The extent to which evolution is repeatable has important implications. If evolution is highly repeatable, the trajectories and outcomes of evolution in different lineages will always be the same. On the other hand, if evolution is not repeatable, then trajectories and outcomes will be diverse. Thus, the repeatability of evolution affects our understanding of the nature of biodiversity and can inform the extent to which evolutionary theory can be used to make predictions. The repeatability of evolution depends on the relative contribution of selection, chance, and history. To determine what factors affect the importance of chance and history during evolution, I propagated replicated populations of the unicellular green alga Chlamydomonas reinhardtii in controlled environments. I measured the change in fitness after a few hundred generations and determined how much variation had arisen among replicate populations and among populations with different histories. I applied a similar approach to study the importance of history in extinctions, and measured rates of extinction in populations with different histories. I found that evolution is much less repeatable in small than in large populations because history is more constraining and selection less efficient in small than in large populations. There is also a significant effect of sex and recombination on the repeatability of evolution at the fitness level, but this effect is highly dependent on the environment of selection. Sex can increase the importance of chance or history in some environments, but lower their importance in others, thereby leading to convergence or divergence depending on the environment. Thirdly, I found that the importance of history during evolution does not appear to come from the accumulation of past evolutionary selection pressures, but rather comes from only the most recent selection pressure as it determines genetic correlations for growth between different environments and the amount of genetic variance. Finally, I found that extinction risks are extremely high during continuous environmental deterioration, although a history of sexual reproduction and phenotypic plasticity play an important role in adaptation. By focusing not solely on the effect of treatments on mean trait values, but also on the variance that arises in our evolution experiments, we can gain a better understanding of the contribution that chance and history make to evolution. The repeatability of evolution can therefore inform us about the adaptive vs. stochastic nature of the diversity we see today, and about the specificity or generality of evolutionary outcomes