Nonlocal correlations in iron pnictides and chalcogenides

Deviations of low-energy electronic structurse of iron-based superconductors from density-functional-theory predictions have been parametrized in terms of band- and orbital-dependent mass renormalizations and energy shifts. The former have typically been described in terms of a local self-energy within the framework of dynamical mean field theory, while the latter appears to require nonlocal effects due to interband scattering. By calculating the renormalized band structure in both random phase approximation (RPA) and the two-particle self-consistent approximation (TPSC), we show that correlations in pnictide systems like LaFeAsO and LiFeAs can be described rather well by a nonlocal self-energy. In particular, Fermi pocket shrinkage as seen in experiments occurs due to repulsive interband finite-energy scattering. For the canonical iron chalcogenide system FeSe in its bulk tetragonal phase, the situation is, however, more complex since even including momentum-dependent band renormalizations cannot explain experimental findings. We propose that the nearest-neighbor Coulomb interaction may play an important role in band-structure renormalization in FeSe. We further compare our evaluations of nonlocal quasiparticle scattering lifetime within RPA and TPSC with experimental data for LiFeAs

Greenland Ice Core Greenland Ice Core Signal Characteristics: An Expanded View of Climate Change

The last millenium of Earth history is of particular interest because it documents the environmental complexities of both natural variability and anthropogenic activity. We have analyzed the major ions contained in the Greenland Ice Sheet Project 2 (GISP 2) ice core from the present to ∼674 A.D. to yield an environmental reconstruction for this period that includes a description of nitrogen and sulfur cycling, volcanic emissions, sea salt and terrestrial influences. We have adapted and extended mathematical procedures for extracting sporadic (e.g., volcanic) events, secular trends, and periodicities found in the data sets. Finally, by not assuming that periodic components (signals) were “stationary” and by utilizing evolutionary spectral analysis, we were able to reveal periodic processes in the climate system which change in frequency, “turn on,” and “turn off” with other climate transitions such as that between the little ice age and the medieval warm period

Retrieving a common accumulation record from Greenland ice cores for the past 1800 years

Abstract. In the accumulation zone of the Greenland ice sheet the annual accumulation rate may be determined through identification of the annual cy-cle in the isotopic climate signal and other parameters that exhibit seasonal vari-ations. On an annual basis the accumulation rate in different Greenland ice cores is highly variable, and the degree of correlation between accumulation series from different ice cores is low. However, when using multi year averages of the dif-ferent accumulation records the correlation increases significantly. A statistical model has been developed to estimate the common climate signal in the differ-ent accumulation records through optimization of the ratio between the variance of the common signal and of the residual. Using this model a common Green-land accumulation record with five years resolution for the past 1800 years has been extracted. The record establishes a climatic record which implies that very dry conditions during the 13th century together with dry and cold spells dur-ing the 14th century may have put extra strain on the Norse population in Green-land and have contributed to their extinction

Effect of ischemic preconditioning and a Kv7 channel blocker on cardiac ischemia-reperfusion injury in rats

Recently, we found cardioprotective effects of ischemic preconditioning (IPC), and from a blocker of KCNQ voltage-gated K+ channels (KV7), XE991 (10,10-bis(4-pyridinylmethyl)-9(10H)-anthracenone), in isolated rat hearts. The purpose of the present study was to investigate the cardiovascular effects of IPC and XE991 and whether they are cardioprotective in intact rats. In conscious rats, we measured the effect of the KV7 channel blocker XE991 on heart rate and blood pressure by use of telemetry. In anesthetized rats, cardiac ischemia was induced by occluding the left coronary artery, and the animals received IPC (2 × 5 min of occlusion), XE991, or a combination. After a 2 h reperfusion period, the hearts were excised, and the area at risk and infarct size were determined. In both anesthetized and conscious rats, XE991 increased blood pressure, and the highest dose (7.5 mg/kg) of XE991 also increased heart rate, and 44% of conscious rats died. XE991 induced marked changes in the electrocardiogram (e.g., increased PR interval and prolonged QTC interval) without changing cardiac action potentials. The infarct size to area at risk ratio was reduced from 53 ± 2% (n = 8) in the vehicle compared to 36 ± 3% in the IPC group (P < 0.05, n = 9). XE991 (0.75 mg/kg) treatment alone or on top of IPC failed to reduce myocardial infarct size. Similar to the effect in isolated hearts, locally applied IPC was cardioprotective in intact animals exposed to ischemia-reperfusion. Systemic administration of XE991 failed to protect the heart against ischemia-reperfusion injury suggesting effects on the autonomic nervous system counteracting the cardioprotection in intact animals. © 2019 Elsevier B.V.Aarhus Universitets ForskningsfondHjerteforeningen: 17-R116-A7616-22074K. Corydon was supported by a scholarship from Aarhus University Research Foundation , U. Simonsen and E.R. Hedegaard were supported by the Danish Heart Foundation (grant 17-R116-A7616-22074 ). The study was also supported by Jens Anker Andersens Foundation, Helge and Peter Kornings Foundation, Direktør Kurt Bønnelycke and wife Mrs Grethe Bønnelyckes Foundation, Helge Peetz and Verner Peetz and wife Vilma Peetz grant. Appendix

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file

Psalms for Organ, Volume 11: Ulster

Pages 23-27 in Psalms for Organ, Volume 11

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented