Assessment of the Precision of Spectral Model Turbulence Analysis Techniques Using Direct Numerical Simulation Data

The spectral model turbulence analysis technique is widely used to derive kinetic energy dissipation rates of turbulent structures (ɛ) from different in situ measurements in the Earth's atmosphere. The essence of this method is to fit a model spectrum to measured spectra of velocity or scalar quantity fluctuations and thereby to derive ɛ only from wavenumber dependence of turbulence spectra. Owing to the simplicity of spectral model of Heisenberg (1948), https://doi.org/10.1007/bf01668899 its application dominates in the literature. Making use of direct numerical simulations which are able to resolve turbulence spectra down to the smallest scales in dissipation range, we advance the spectral model technique by quantifying uncertainties for two spectral models, the Heisenberg (1948), https://doi.org/10.1007/bf01668899 and the Tatarskii (1971) model, depending on (a) resolution of measurements, (b) stage of turbulence evolution, (c) model used. We show that the model of Tatarskii (1971) can yield more accurate results and reveals higher sensitivity to the lowest ɛ-values. This study shows that the spectral model technique can reliably derive ɛ if measured spectra only resolve half-decade of power change within the viscous (viscous-convective) subrange. In summary, we give some practical recommendations on how to derive the most precise and detailed turbulence dissipation field from in situ measurements depending on their quality. We also supply program code of the spectral models used in this study in Python, IDL, and Matlab

Observations of Reduced Turbulence and Wave Activity in the Arctic Middle Atmosphere Following the January 2015 Sudden Stratospheric Warming

Measurements of turbulence and waves were made as part of the Mesosphere-Lower Thermosphere Turbulence Experiment (MTeX) on the night of 25–26 January 2015 at Poker Flat Research Range, Chatanika, Alaska (65°N, 147°W). Rocket-borne ionization gauge measurements revealed turbulence in the 70- to 88-km altitude region with energy dissipation rates between 0.1 and 24 mW/kg with an average value of 2.6 mW/kg. The eddy diffusion coefficient varied between 0.3 and 134 m2/s with an average value of 10 m2/s. Turbulence was detected around mesospheric inversion layers (MILs) in both the topside and bottomside of the MILs. These low levels of turbulence were measured after a minor sudden stratospheric warming when the circulation continued to be disturbed by planetary waves and winds remained weak in the stratosphere and mesosphere. Ground-based lidar measurements characterized the ensemble of inertia-gravity waves and monochromatic gravity waves. The ensemble of inertia-gravity waves had a specific potential energy of 0.8 J/kg over the 40- to 50-km altitude region, one of the lowest values recorded at Chatanika. The turbulence measurements coincided with the overturning of a 2.5-hr monochromatic gravity wave in a depth of 3 km at 85 km. The energy dissipation rates were estimated to be 3 mW/kg for the ensemble of waves and 18 mW/kg for the monochromatic wave. The MTeX observations reveal low levels of turbulence associated with low levels of gravity wave activity. In the light of other Arctic observations and model studies, these observations suggest that there may be reduced turbulence during disturbed winters

Observations of Reduced Turbulence and Wave Activity in the Arctic Middle Atmosphere Following the January 2015 Sudden Stratospheric Warming

Measurements of turbulence and waves were made as part of the Mesosphere-Lower Thermosphere Turbulence Experiment (MTeX) on the night of 25–26 January 2015 at Poker Flat Research Range, Chatanika, Alaska (65°N, 147°W). Rocket-borne ionization gauge measurements revealed turbulence in the 70- to 88-km altitude region with energy dissipation rates between 0.1 and 24 mW/kg with an average value of 2.6 mW/kg. The eddy diffusion coefficient varied between 0.3 and 134 m2/s with an average value of 10 m2/s. Turbulence was detected around mesospheric inversion layers (MILs) in both the topside and bottomside of the MILs. These low levels of turbulence were measured after a minor sudden stratospheric warming when the circulation continued to be disturbed by planetary waves and winds remained weak in the stratosphere and mesosphere. Ground-based lidar measurements characterized the ensemble of inertia-gravity waves and monochromatic gravity waves. The ensemble of inertia-gravity waves had a specific potential energy of 0.8 J/kg over the 40- to 50-km altitude region, one of the lowest values recorded at Chatanika. The turbulence measurements coincided with the overturning of a 2.5-hr monochromatic gravity wave in a depth of 3 km at 85 km. The energy dissipation rates were estimated to be 3 mW/kg for the ensemble of waves and 18 mW/kg for the monochromatic wave. The MTeX observations reveal low levels of turbulence associated with low levels of gravity wave activity. In the light of other Arctic observations and model studies, these observations suggest that there may be reduced turbulence during disturbed winters

Neuropathology of New-Onset Refractory Status Epilepticus (NORSE)

New-Onset Refractory Status Epilepticus (NORSE), including its subtype with a preceding febrile illness known as FIRES (Febrile Infection-Related Epilepsy Syndrome), is one of the most severe forms of status epilepticus. Despite an extensive workup (clinical evaluation, EEG, imaging, biological tests), the majority of NORSE cases remain unexplained (i.e., “cryptogenic NORSE”). Understanding the pathophysiological mechanisms underlying cryptogenic NORSE and the related long-term consequences is crucial to improve patient management and preventing secondary neuronal injury and drug-resistant post-NORSE epilepsy. Previously, neuropathological evaluations conducted on biopsies or autopsies have been found helpful for identifying the etiologies of some cases that were previously of unknown cause. Here, we summarize the findings of studies reporting neuropathology findings in patients with NORSE, including FIRES. We identified 64 cryptogenic cases and 66 neuropathology tissue samples, including 37 biopsies, 18 autopsies, and seven epilepsy surgeries (the type of tissue sample was not detailed for 4 cases). We describe the main neuropathology findings and place a particular emphasis on cases for which neuropathology findings helped establish a diagnosis or elucidate the pathophysiology of cryptogenic NORSE, or on described cases in which neuropathology findings supported the selection of specific treatments for patients with NORSE

Genetic and Environmental Effects on the Development of White Matter Hyperintensities in a Middle Age Twin Population

Introduction: White matter hyperintensities (WMH) indicate white matter brain lesions in magnetic resonance imaging (MRI), which can be used as a marker for brain aging and cerebrovascular and neurodegenerative disorders. Twin studies revealed substantial but not uniform WMH heritability in elderly twins. The objective of our study was to investigate the genetic and environmental components of WMH, as well as their importance in a healthy twin population, utilizing 3T MRI scanners in a middle-aged twin population. Methods: Brain MRI was performed on 120 healthy adult twins from the Hungarian Twin Registry on a 3T scanner (86 monozygotic, MZ and 34 dizygotic, DZ twins; median age 50 ± 26.5 years, 72.5% female and 27.5% male). The count of WMH on FLAIR images was calculated using an automated volumetry pipeline (volBrain) and human processing. The age- and sex-adjusted MZ and DZ intra-pair correlations were determined and the total variance was decomposed into genetic, shared and unique environmental components using structural equation modeling. Results: Age and sex-adjusted MZ intrapair correlations were higher than DZ correlations, indicating moderate genetic influence in each lesion (rMZ = 0.466, rDZ = −0.025 for total count; rMZ = 0.482, rDZ = 0.093 for deep white matter count; rMZ = 0.739, rDZ = 0.39 for infratentorial count; rMZ = 0.573, rDZ = 0.372 for cerebellar count and rMZ = 0.473, rDZ = 0.19 for periventricular count), indicating a moderate heritability (A = 40.3%, A = 45%, A = 72.7% and A = 55.5%and 47.2%, respectively). The rest of the variance was influenced by unique environmental effects (E between 27.3% and 59.7%, respectively). Conclusions: The number of WMH lesions is moderately influenced by genetic effects, particularly in the infratentorial region in middle-aged twins. These results suggest that the distribution of WMH in various brain regions is heterogeneous

Heritability of Subcortical Grey Matter Structures

Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson’s correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45−0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72−0.85). A strong correlation between CAT12 and volBrain (r = 0.74−0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases