Abstract
Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact ...the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.
Proteins fold in 3-dimensional conformations which are important for their function. Characterizing the global conformation of proteins rigorously and separating secondary structure effects from ...topological effects is a challenge. New developments in applied knot theory allow to characterize the topological characteristics of proteins (knotted or not). By analyzing a small set of two-state and multi-state proteins with no knots or slipknots, our results show that 95.4% of the analyzed proteins have non-trivial topological characteristics, as reflected by the second Vassiliev measure, and that the logarithm of the experimental protein folding rate depends on both the local geometry and the topology of the protein's native state.
Comparing chemical abundances of a planet and the host star reveals the origin and formation pathway of the planet. Stellar abundance is measured with high-resolution spectroscopy. Planet abundance, ...on the other hand, is usually inferred from low-resolution data. For directly imaged exoplanets, the data are available from a slew of high-contrast imaging/spectroscopy instruments. Here, we study the chemical abundance of HR 8799 and its planet c. We measure stellar abundance using LBT/PEPSI (R = 120,000) and archival HARPS data: stellar C/H, O/H, and C/O are 0.11 0.12, 0.12 0.14, and , all consistent with solar values. We conduct atmospheric retrieval using newly obtained Subaru/CHARIS data together with archival Gemini/GPI and Keck/OSIRIS data. We model the planet spectrum with petitRADTRANS and conduct retrieval using PyMultiNest. Retrieved planetary abundance can vary by ∼0.5 dex, from sub-stellar to stellar C and O abundances. The variation depends on whether strong priors are chosen to ensure a reasonable planet mass. Moreover, comparison with previous works also reveals inconsistency in abundance measurements. We discuss potential issues that can cause the inconsistency, e.g., systematics in individual data sets and different assumptions in the physics and chemistry in retrieval. We conclude that no robust retrieval can be obtained unless the issues are fully resolved.
Introduction: Individuals with prediabetes (preDM) are at increased risk of developing type 2 diabetes (T2DM) and cardiovascular disease. We know little about the risk perception and lifestyle ...behaviors of young adults with preDM.
Methods: This nationally representative cross-sectional analysis of 2005-2018 National Health and Nutrition Examination Survey data was conducted on nonpregnant young adults (aged - 34) . PreDM and Diabetes (DM) were based on fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) definitions recommended by the American Diabetes Association. We used fasting sample weight to generate nationally representative estimates. Measures also include sociodemographic, lifestyle behaviors (diet, physical activity, and sleep) , awareness, and perceived risk for diabetes or preDM. Analyses were accounted for the complex sample design and performed using Stata 17.
Results: Overall, among 4272 young adults, the weighted prevalence of PreDM and DM were 26% and 2.6%, respectively. There was a significant difference (p<0.001) in the awareness of their disease condition between individuals with preDM (3.7%) and those with DM (13.2%) . The multivariable logistic regression model found that women were less likely to have preDM than men. Obese subjects were more likely (OR=3.19, 95%CI 2.39 - 4.27) to have preDM. College education, higher income, and physical activity (600 -1200 MET minutes/week) were protective factors against preDM. Long sleep duration (>=9 hours per day) is associated with a 56% increased risk for prediabetes (95%CI 1.02 - 2.39) .
Conclusions: The high prevalence of preDM in young adults reinforces the critical need for effective public health strategies that promote lifestyle behaviors, including physical activity, and improve sleep quality.
Disclosure
A.Yan: None. J.J.Wang: None. Z.Shi: None.
Abstract
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep Neural ...Networks (SDNN), for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO). The training data for SDNN are prepared by a Milne–Eddington (ME) inversion code used by BBSO. We quantitatively assess SDNN, comparing its inversion results with those obtained by the ME inversion code and related machine-learning (ML) algorithms such as multiple support vector regression, multilayer perceptrons, and a pixel-level convolutional neural network. Major findings from our experimental study are summarized as follows. First, the SDNN-inferred LOS velocities are highly correlated to the ME-calculated ones with the Pearson product–moment correlation coefficient being close to 0.9 on average. Second, SDNN is faster, while producing smoother and cleaner LOS velocity and Doppler width maps, than the ME inversion code. Third, the maps produced by SDNN are closer to ME’s maps than those from the related ML algorithms, demonstrating that the learning capability of SDNN is better than those of the ML algorithms. Finally, a comparison between the inversion results of ME and SDNN based on GST/NIRIS and those from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory in flare-prolific active region NOAA 12673 is presented. We also discuss extensions of SDNN for inferring vector magnetic fields with empirical evaluation.
Coronavirus disease 2019 (COVID-19) evolved quickly into a global pandemic with myriad systemic complications, including stroke. We report the largest case series to date of cerebrovascular ...complications of COVID-19 and compare with stroke patients without infection.
Retrospective case series of COVID-19 patients with imaging-confirmed stroke, treated at 11 hospitals in New York, between March 14 and April 26, 2020. Demographic, clinical, laboratory, imaging, and outcome data were collected, and cases were compared with date-matched controls without COVID-19 from 1 year prior.
Eighty-six COVID-19-positive stroke cases were identified (mean age, 67.4 years; 44.2% women). Ischemic stroke (83.7%) and nonfocal neurological presentations (67.4%) predominated, commonly involving multivascular distributions (45.8%) with associated hemorrhage (20.8%). Compared with controls (n=499), COVID-19 was associated with in-hospital stroke onset (47.7% versus 5.0%;
<0.001), mortality (29.1% versus 9.0%;
<0.001), and Black/multiracial race (58.1% versus 36.9%;
=0.001). COVID-19 was the strongest independent risk factor for in-hospital stroke (odds ratio, 20.9 95% CI, 10.4-42.2;
<0.001), whereas COVID-19, older age, and intracranial hemorrhage independently predicted mortality.
COVID-19 is an independent risk factor for stroke in hospitalized patients and mortality, and stroke presentations are frequently atypical.
Objective
This study examined the association between BMI and clinical outcomes among patients with coronavirus disease 2019 (COVID‐19) infection.
Methods
A total of 10,861 patients with COVID‐19 ...infection who were admitted to the Northwell Health system hospitals between March 1, 2020, and April 27, 2020, were included in this study. BMI was classified as underweight, normal weight, overweight, and obesity classes I, II, and III. Primary outcomes were invasive mechanical ventilation (IMV) and death.
Results
A total of 243 (2.2%) patients were underweight, 2,507 (23.1%) were normal weight, 4,021 (37.0%) had overweight, 2,345 (21.6%) had obesity class I, 990 (9.1%) had obesity class II, and 755 (7.0%) had obesity class III. Patients who had overweight (odds ratio OR = 1.27 95% CI: 1.11‐1.46), obesity class I (OR = 1.48 95% CI: 1.27‐1.72), obesity class II (OR = 1.89 95% CI: 1.56‐2.28), and obesity class III (OR = 2.31 95% CI: 1.88‐2.85) had an increased risk of requiring IMV. Underweight and obesity classes II and III were statistically associated with death (OR = 1.44 95% CI: 1.08‐1.92; OR = 1.25 95% CI: 1.03‐1.52; OR = 1.61 95% CI: 1.30‐2.00, respectively). Among patients who were on IMV, BMI was not associated with inpatient deaths.
Conclusions
Patients who are underweight or who have obesity are at risk for mechanical ventilation and death, suggesting that pulmonary complications (indicated by IMV) are a significant contributor for poor outcomes in COVID‐19 infection.
RNA junctions are important structural elements that form when three or more helices come together in space in the tertiary structures of RNA molecules. Determining their structural configuration is ...important for predicting RNA 3D structure. We introduce a computational method to predict, at the secondary structure level, the coaxial helical stacking arrangement in junctions, as well as classify the junction topology. Our approach uses a data mining approach known as random forests, which relies on a set of decision trees trained using length, sequence and other variables specified for any given junction. The resulting protocol predicts coaxial stacking within three- and four-way junctions with an accuracy of 81% and 77%, respectively; the accuracy increases to 83% and 87%, respectively, when knowledge from the junction family type is included. Coaxial stacking predictions for the five to ten-way junctions are less accurate (60%) due to sparse data available for training. Additionally, our application predicts the junction family with an accuracy of 85% for three-way junctions and 74% for four-way junctions. Comparisons with other methods, as well applications to unsolved RNAs, are also presented. The web server Junction-Explorer to predict junction topologies is freely available at: http://bioinformatics.njit.edu/junction.
Solar activity is often caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions (ARs) have been used to ...analyze and forecast eruptive events, such as solar flares and coronal mass ejections. Unfortunately, the most recent Solar Cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the
Helioseismic and Magnetic Imager
(HMI) on board the
Solar Dynamics Observatory
(SDO) since its launch in 2010. In this work, we look into another major instrument, namely the
Michelson Doppler Imager
(MDI) on board the
Solar and Heliospheric Observatory
(SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers a more active Solar Cycle 23 with many large flares. However, SOHO/MDI only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms,
B
x
and
B
y
, taken by SDO/HMI, along with H
α
observations collected by the
Big Bear Solar Observatory
(BBSO), and to generate synthetic vector components
B
x
′
and
B
y
′
of ARs. These generated vector components, together with observational LOS data, would form vector magnetograms for SOHO/MDI. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the MagNet method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of ARs for SOHO/MDI using SDO/HMI and H
α
data.
Image super-resolution is an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network for solar image super-resolution. Our method, ...named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). The ground-truth labels used for training SolarCNN are the LOS magnetograms collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Solar ARs consist of strong magnetic fields in which magnetic energy can suddenly be released to produce extreme space-weather events, such as solar flares, coronal mass ejections, and solar energetic particles. SOHO/MDI covers Solar Cycle 23, which is stronger with more eruptive events than Cycle 24. Enhanced SOHO/MDI magnetograms allow for better understanding and forecasting of violent events of space weather. Experimental results show that SolarCNN improves the quality of SOHO/MDI magnetograms in terms of the structural similarity index measure, Pearson’s correlation coefficient, and the peak signal-to-noise ratio.
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