Spectral Analysis of Solar Radio Type III Bursts from 20 kHz to 410 MHz

We present the statistical analysis of the spectral response of solar radio type III bursts over the wide frequency range between 20 kHz and 410 MHz. For this purpose, we have used observations that were carried out using both spaced-based (Wind/Waves) and ground-based (Nançay Decameter Array and Nançay Radioheliograph) facilities. In order to compare the flux densities observed by the different instruments, we have carefully calibrated the data and displayed them in solar flux units. The main result of our study is that type III bursts, in the metric to hectometric wavelength range, statistically exhibit a clear maximum of their median radio flux density around 2 MHz. Although this result was already reported by inspecting the spectral profiles of type III bursts in the frequency range 20 kHz–20 MHz, our study extends such analysis for the first time to metric radio frequencies (i.e., from 20 kHz to 410 MHz) and confirms the maximum spectral response around 2 MHz. In addition, using a simple empirical model we show that the median radio flux S of the studied data set obeys the polynomial form Y = 0.04X 3 − 1.63X 2 + 16.30X − 41.24, with X = ln ( F MHz ) and with Y = ln ( S SFU ) . Using the Sittler and Guhathakurtha model for coronal streamers, we have found that the maximum of radio power therefore falls in the range 4 to 10 R ⊙, depending on whether the type III emissions are assumed to be at the fundamental or the harmonic.

1H-Nuclear Magnetic Resonance Analysis of Urine as Diagnostic Tool for Organic Acidemias and Aminoacidopathies

The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC–MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.

Transfreq: a Python package for computing the theta-to-alpha transition frequency from resting state EEG data

A classic approach to estimate the individual theta-to-alpha transition frequency requires two electroencephalographic (EEG) recordings, one acquired in restingstate condition and one showing an alpha de-synchronisation due e.g. to task execution. This translates into longer recording sessions that my be cumbersome in studies involving patients. Moreover, incomplete de-synchronisation of the alpha rhythm may compromise the final estimation of the transition frequency. Here we present transfreq, a Python library that allows the computation of the transition frequency from resting-state data by clustering the spectral profiles at different EEG channels based on their content in the alpha and theta bands. We first provide an overview of the transfreq core algorithm and of the software architecture. Then we demonstrate its feasibility and robustness across different experimental setups on a publicly available EEG data set and on in-house recordings. A detailed documentation of transfreq and the codes for reproducing the analysis of the paper with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/

The Spatial and Temporal Variations of Turbulence in a Solar Flare

Magnetohydrodynamic plasma turbulence is believed to play a vital role in the production of energetic electrons during solar flares, and the nonthermal broadening of spectral lines is a key sign of this turbulence. Here, we determine how flare turbulence evolves in time and space using spectral profiles of Fe xxiv, Fe xxiii, and Fe xvi, observed by the Hinode/EUV Imaging Spectrometer. Maps of nonthermal velocity are created for times covering the X-ray rise, peak, and decay. For the first time, the creation of kinetic energy density maps reveal where energy is available for energization, suggesting that similar levels of energy may be available to heat and/or accelerate electrons in large regions of the flare. We find that turbulence is distributed throughout the entire flare, often greatest in the coronal loop tops, and decaying at different rates at different locations. For hotter ions (Fe xxiv and Fe xxiii), the nonthermal velocity decreases as the flare evolves and during/after the X-ray peak shows a clear spatial variation decreasing linearly from the loop apex toward the ribbon. For the cooler ion (Fe xvi), the nonthermal velocity remains relativity constant throughout the flare, but steeply increases in one region corresponding to the southern ribbon, peaking just prior to the peak in hard X-rays before declining. The results suggest turbulence has a more complex temporal and spatial structure than previously assumed, while newly introduced turbulent kinetic energy maps show the availability of the energy and identify important spatial inhomogeneities in the macroscopic plasma motions leading to turbulence.

Unsupervised Machine Learning for the Identification of Preflare Spectroscopic Signatures

The study of the preflare environment is of great importance to understanding what drives solar flares. k-means clustering, an unsupervised machine-learning technique, has the ability to cluster large data set in a way that would be impractical or impossible for a human to do. In this paper we present a study using k-means clustering to identify possible preflare signatures in spectroscopic observations of the Mg ii h and k spectral lines made by NASA's Interface Region Imaging Spectrometer. Our analysis finds that spectral profiles showing single-peak Mg ii h and k and single-peaked emission in the Mg ii UV triplet lines are associated with preflare activity up to 40 minutes prior to flaring. Subsequent inversions of these spectral profiles reveal increased temperature and electron density in the chromosphere, which suggest that significant heating events in the chromosphere may be associated with precursor signals to flares.

Properties of the C ii 1334 Å Line in Coronal Hole and Quiet Sun as Observed by IRIS

Coronal holes (CHs) have subdued intensity and net blueshifts when compared to the quiet Sun (QS) at coronal temperatures. At transition region temperatures, such differences are obtained for regions with identical absolute photospheric magnetic flux density (∣B∣). In this work, we use spectroscopic measurements of the C ii 1334 Å line from the Interface Region Imaging Spectrograph, formed at chromospheric temperatures, to investigate the intensity, Doppler shift, line width, skew, and excess kurtosis variations with ∣B∣. We find the intensity, Doppler shift, and linewidths to increase with ∣B∣ for CHs and QS. The CHs show deficit in intensity and excess total widths over QS for regions with identical ∣B∣. For pixels with only upflows, CHs show excess upflows over QS, while for pixels with only downflows, CHs show excess downflows over QS that cease to exist at ∣B∣ ≤ 40. Finally, the spectral profiles are found to be more skewed and flatter than a Gaussian, with no difference between CHs and QS. These results are important in understanding the heating of the atmosphere in CH and QS, including solar wind formation, and provide further constraints on the modeling of the solar atmosphere.

The natural frequencies of the resting human brain: an MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Furthermore, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

Mapping of population disparities in the cholangiocarcinoma urinary metabolome

Phenotypic diversity in urinary metabolomes of different geographical populations has been recognized recently. In this study, urinary metabolic signatures from Western (United Kingdom) and South-East Asian (Thai) cholangiocarcinoma patients were characterized to understand spectral variability due to host carcinogenic processes and/or exogenous differences (nutritional, environmental and pharmaceutical). Urinary liquid chromatography mass spectroscopy (LC–MS) spectral profiles from Thai (healthy = 20 and cholangiocarcinoma = 14) and UK cohorts (healthy = 22 and cholangiocarcinoma = 10) were obtained and modelled using chemometric data analysis. Healthy metabolome disparities between the two distinct populations were primarily related to differences in dietary practices and body composition. Metabolites excreted due to drug treatment were dominant in urine specimens from cholangiocarcinoma patients, particularly in Western individuals. Urine from participants with sporadic (UK) cholangiocarcinoma contained greater levels of a nucleotide metabolite (uridine/pseudouridine). Higher relative concentrations of 7-methylguanine were observed in urine specimens from Thai cholangiocarcinoma patients. The urinary excretion of hippurate and methyladenine (gut microbial-host co-metabolites) showed a similar pattern of lower levels in patients with malignant biliary tumours from both countries. Intrinsic (body weight and body composition) and extrinsic (xenobiotic metabolism) factors were the main causes of disparities between the two populations. Regardless of the underlying aetiology, biological perturbations associated with cholangiocarcinoma urine metabolome signatures appeared to be influenced by gut microbial community metabolism. Dysregulation in nucleotide metabolism was associated with sporadic cholangiocarcinoma, possibly indicating differences in mitochondrial energy production pathways between cholangiocarcinoma tumour subtypes. Mapping population-specific metabolic disparities may aid in interpretation of disease processes and identification of candidate biomarkers.

Near-infrared manipulation of multiple neuronal populations via trichromatic upconversion

Using multi-color visible lights for independent optogenetic manipulation of multiple neuronal populations offers the ability for sophisticated brain functions and behavior dissection. To mitigate invasive fiber insertion, infrared light excitable upconversion nanoparticles (UCNPs) with deep tissue penetration have been implemented in optogenetics. However, due to the chromatic crosstalk induced by the multiple emission peaks, conventional UCNPs or their mixture cannot independently activate multiple targeted neuronal populations. Here, we report NIR multi-color optogenetics by the well-designed trichromatic UCNPs with excitation-specific luminescence. The blue, green and red color emissions can be separately tuned by switching excitation wavelength to match respective spectral profiles of optogenetic proteins ChR2, C1V1 and ChrimsonR, which enables selective activation of three distinct neuronal populations. Such stimulation with tunable intensity can not only activate distinct neuronal populations selectively, but also achieve transcranial selective modulation of the motion behavior of awake-mice, which opens up a possibility of multi-color upconversion optogenetics.