The Quiet-Sun Photosphere and Chromosphere

The overall structure and the fine structure of the solar photosphere outside active regions are largely understood, except possibly important roles of a turbulent near-surface dynamo at its bottom, internal gravity waves at its top, and small-scale vorticity. Classical 1D static radiation-escape modelling has been replaced by 3D time-dependent MHD simulations that come closer to reality. The solar chromosphere, in contrast, remains ill-understood although its pivotal role in coronal mass and energy loading makes it a principal research area. Its fine structure defines its overall structure, so that hard-to-observe and hard-to-model small-scale dynamical processes are the key to understanding. However, both chromospheric observation and chromospheric simulation presently mature towards the required sophistication. The open-field features seem of greater interest than the easier-to-see closed-field features.Comment: Accepted for special issue "Astrophysical Processes on the Sun" of Phil. Trans. Royal Soc. A, ed. C. Parnell. Note: clicking on the year in a citation opens the corresponding ADS abstract page in the browse

All-sky Relative Opacity Mapping Using Night Time Panoramic Images

An all-sky cloud monitoring system that generates relative opacity maps over many of the world's premier astronomical observatories is described. Photometric measurements of numerous background stars are combined with simultaneous sky brightness measurements to differentiate thin clouds from sky glow sources such as air glow and zodiacal light. The system takes a continuous pipeline of all-sky images, and compares them to canonical images taken on other nights at the same sidereal time. Data interpolation then yields transmission maps covering almost the entire sky. An implementation of this system is currently operating through the Night Sky Live network of CONCAM3s located at Cerro Pachon (Chile), Mauna Kea (Hawaii), Haleakala (Hawaii), SALT (South Africa) and the Canary Islands (Northwestern Africa).Comment: Accepted for publication in PAS

Interpreting predictions of cognition from simulated versus empirical resting state functional connectivity

The relation between structure and function of the brain, and how behavior arises from it, is a central topic of interest in neuroscience. This problem can be formulated in terms of Structural Connectivity (SC) and Functional Connectivity (FC), respectively representing anatomical connections and functional interactions between regions in the brain. Recently, a study by Sarwar and colleagues has demonstrated individualized prediction of FC from SC using machine learning, additionally showing that variation in cognitive performance is explained by simulated FC (sFC) almost as well as by empirical FC (eFC). We investigated how decisions made to predict cognition differ between the models based on eFC and sFC. We predicted cognitive performance with Lasso regression in 100 cross-validation loops from both eFC and sFC separately, using FC between each of the 2278 pairs of regions in the 68-region Desikan-Killiany parcellation as features. We identified relevant predictors of cognition by inspecting permutation importance scores and keeping only features whose importance scores were consistently high across validation loops. 13 eFC features and 21 sFC features survived this procedure. Of these, only one feature overlapped between eFC and sFC. Analyzing overlap between regions corresponding to important features and functional systems known to support cognition revealed no patterns for either eFC or sFC features. In conclusion, we found that while cognition can be predicted from sFC almost as well as from eFC, different features are used in the models, and these features were not found to follow any structure in terms of functional systems. This shows that while machine learning models provide a theoretical upper bound on how accurately function can be predicted from structure, they do not necessarily produce output that can be interpreted in the same way as the data the models were trained on

56Ni dredge-up in the type IIp Supernova 1995V

We present contemporary infrared and optical spectra of the plateau type II SN 1995V in NGC 1087 covering four epochs, approximately 22 to 84 days after shock breakout. The data show, for the first time, the infrared spectroscopic evolution during the plateau phase of a typical type II event. In the optical region P Cygni lines of the Balmer series and of metals lines were identified. The infrared (IR) spectra were largely dominated by the continuum, but P Cygni Paschen lines and Brackett gamma lines were also clearly seen. The other prominent IR features are confined to wavelengths blueward of 11000 \AA and include Sr II 10327, Fe II 10547, C I 10695 and He I 10830 \AA. We demonstrate the presence of He I 10830 \AA on days 69 and 85. The presence of this line at such late times implies re-ionisation. A likely re-ionising mechanism is gamma-ray deposition following the radioactive decay of 56Ni. We examine this mechanism by constructing a spectral model for the He I 10830 \AA line based on explosion model s15s7b2f of Weaver & Woosley (1993). We find that this does not generate the observed line owing to the confinement of the 56Ni to the central zones of the ejecta. In order to reproduce the He I line, it was necessary to introduce additional upward mixing of the 56Ni, with 10^{-5} of the total nickel mass reaching above the helium photosphere. In addition, we argue that the He I line-formation region is likely to have been in the form of pure helium clumps in the hydrogen envelope.Comment: Accepted for publication in MNRAS, 32 pages including 11 figures (uses psfig.sty - included

Fully automatic meningioma segmentation using T1-weighted contrast-enhanced MR images only

Background Manual segmentation of brain tumors requires expertise, is time-consuming, and is subject to inter-rater variability. Fully automatic brain tumor segmentation is possible for glioma and meningioma when volumetric T1, T1 contrast-enhanced (T1c), T2, and Fluid-attenuated inversion recovery (FLAIR) MRIs are available. In clinical care of meningiomas, however, often only volumetric T1c scans are available. In this work, we trained a deep learning network to segment meningiomas using only T1c scans for use in clinical research. Material and Methods NnU-Net, a deep learning model that is optimized for medical image segmentation, was trained to segment meningiomas from T1c images. This was performed on a large clinically collected meningioma dataset (n=374) of T1c scans with semi-automatically generated enhancing tumor masks and additional data from the BraTS2020 glioma dataset. Model performance was compared against inter-rater reliability, between different models, between anatomical tumor locations, and against models using multiple MRI modalities. Results The best performing model obtained a Dice score of 0.90. This performance was 0.03 points lower when compared to inter-rater reliability (Dice=0.93) and almost equal to models using multiple MRI modalities. Model performance split over anatomical tumor locations was between 0.90 and 0.97 (Dice). Conclusion Fully automatic meningioma segmentation using only T1c images is possible with an accuracy that is similar to inter-rater reliability and models using multiple imaging modalities

HST Observations of Chromospheres in Metal Deficient Field Giants

HST high resolution spectra of metal-deficient field giants more than double the stars in previous studies, span about 3 magnitudes on the red giant branch, and sample an abundance range [Fe/H]= -1 to -3. These stars, in spite of their age and low metallicity, possess chromospheric fluxes of Mg II (2800 Angstrom) that are within a factor of 4 of Population I stars, and give signs of a dependence on the metal abundance at the lowest metallicities. The Mg II k-line widths depend on luminosity and correlate with metallicity. Line profile asymmetries reveal outflows that occur at lower luminosities (M_V = -0.8) than detected in Ca K and H-alpha lines in metal-poor giants, suggesting mass outflow occurs over a larger span of the red giant branch than previously thought, and confirming that the Mg II lines are good wind diagnostics. These results do not support a magnetically dominated chromosphere, but appear more consistent with some sort of hydrodynamic, or acoustic heating of the outer atmospheres.Comment: 36 pages, 12 figures, 7 tables, and accepted for publication in The Astronomical Journa

Эффективная система охлаждения квантоскопов

Разработана каскадная компрессорная система охлаждения, реализующая цикл Линде с многокомпонентными рабочими телами, ресурс работы которой составляет 30 тыс. часов

Effects of subcutaneous LPS injection on gestational length and intrauterine and neonatal mortality in mice

BACKGROUND Infection during pregnancy can predispose offspring to develop various psychiatric disorders such as depression in later life. In order to investigate the potential mechanisms underlying these associations, animal models of maternal infection have been employed. As such, lipopolysaccharide (LPS) has been commonly used to mimic a bacterial infection in pregnant mice. OBJECTIVE The original aim of our study was to investigate the effects of different doses of subcutaneous LPS administration on affective behavior in adult mouse offspring. In the present paper, however, we report that subcutaneous LPS administration has a profound impact on gestational length, litter size, and perinatal mortality in the offspring, even at a relatively low dose. METHODS Pregnant mice were randomly divided into 3 groups, receiving either a high (2 mg/kg) or a low (0.5 mg/kg) dose of LPS or phosphate-buffered saline by means of subcutaneous injection. Subsequently, the effects on gestational length, litter size, and perinatal mortality in the offspring were assessed. RESULTS After subcutaneous injection with a high dose of LPS, we observed a significant decrease in gestational length and an increase in neonatal mortality. When the low dose was administered, a tendency towards a reduced litter size was observed, most likely reflecting increased intrauterine mortality in response to prenatal maternal LPS exposure. CONCLUSIONS We showed that subcutaneous administration of 2 mg/kg LPS to pregnant mice in the last phase of gestation should be avoided because of high offspring mortality rates, whereas subcutaneous injection of 0.5 mg/kg LPS seems to result in reabsorption of the fetuses

Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

Aims Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). Methods and results In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. Conclusion The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement