Abstract
Developments in the stability of modern spectrographs have led to extremely precise instrumental radial velocity (RV) measurements. For most stars, the detection limit of planetary ...companions with these instruments is expected to be dominated by astrophysical noise sources such as starspots. Correlated signals caused by rotationally modulated starspots can obscure or mimic the Doppler shifts induced by even the closest, most massive planets. This is especially true for young, magnetically active stars where stellar activity can cause fluctuation amplitudes of ≳0.1 mag in brightness and ≳100 m s
−1
in RV semiamplitudes. Techniques that can mitigate these effects and increase our sensitivity to young planets are critical to improving our understanding of the evolution of planetary systems. Gaussian processes (GPs) have been successfully employed to model and constrain activity signals in individual cases. However, a principled approach of this technique, specifically for the joint modeling of photometry and RVs, has not yet been developed. In this work, we present a GP framework to simultaneously model stellar activity signals in photometry and RVs that can be used to investigate the relationship between both time series. Our method, inspired by the
FF
′
framework of Aigrain et al., models spot-driven activity signals as the linear combinations of two independent latent GPs and their time derivatives. We also simulate time series affected by starspots by extending the
starry
software to incorporate time evolution of stellar features. Using these synthetic data sets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches.
Abstract Multisystemic inflammatory syndrome in children (MIS-C) might manifest in a broad spectrum of clinical scenarios, ranging from mild features to multi-organ dysfunction and mortality. ...However, this novel entity has a heterogenicity of data regarding prognostic factors associated with severe outcomes. The present study aimed to identify independent predictors for severity by using multivariate regression models. A total of 391 patients (255 boys and 136 girls) were admitted to Vietnam National Children’s Hospital from January 2022 to June 2023. The median age was 85 (range: 2–188) months, and only 12 (3.1%) patients had comorbidities. 161 (41.2%) patients required PICU admission, and the median PICU LOS was 4 (2–7) days. We observed independent factors related to PICU admission, including CRP $$\ge $$ ≥ 50 (mg/L) (OR 2.52, 95% CI 1.39–4.56, p = 0.002), albumin $$\le $$ ≤ 30 (g/L) (OR 3.18, 95% CI 1.63–6.02, p = 0.001), absolute lymphocyte count $$\le $$ ≤ 2 (× 10 9 /L) (OR 2.18, 95% CI 1.29–3.71, p = 0.004), ferritin ≥ 300 (ng/mL) (OR 2.35, 95% CI 1.38–4.01), p = 0.002), and LVEF < 60 (%) (OR 2.48, 95% CI 1.28–4.78, p = 0.007). Shock developed in 140 (35.8%) patients, especially for those decreased absolute lymphocyte $$\le $$ ≤ 2 (× 10 9 /L) (OR 2.48, 95% CI 1.10–5.61, p = 0.029), albumin $$\le $$ ≤ 30 (g/L) (OR 2.53, 95% CI 1.22–5.24, p = 0.013), or LVEF < 60 (%) (OR 2.24, 95% CI 1.12–4.51, p = 0.022). In conclusion, our study emphasized that absolute lymphocyte count, serum albumin, CRP, and LVEF were independent predictors for MIS-C severity. Further well-designed investigations are required to validate their efficacy in predicting MIS-C severe cases, especially compared to other parameters. As MIS-C is a new entity and severe courses may progress aggressively, identifying high-risk patients optimizes clinicians' follow-up and management to improve disease outcomes.
Abstract Transiting giant planets provide a natural opportunity to examine stellar obliquities, which offer clues about the origin and dynamical histories of close-in planets. Hot Jupiters orbiting ...Sun-like stars show a tendency for obliquity alignment, which suggests that obliquities are rarely excited or that tidal realignment is common. However, the stellar obliquity distribution is less clear for giant planets at wider separations where realignment mechanisms are not expected to operate. In this work, we uniformly derive line-of-sight inclinations for 47 cool stars ( T eff < 6200 K) harboring transiting hot and warm giant planets by combining rotation periods, stellar radii, and v sin i measurements. Among the systems that show signs of spin–orbit misalignment in our sample, three are identified as being misaligned here for the first time. Of particular interest are Kepler-1654, one of the longest-period (1047 days; 2.0 au) giant planets in a misaligned system, and Kepler-30, a multiplanet misaligned system. By comparing the reconstructed underlying inclination distributions, we find that the inferred minimum misalignment distributions of hot Jupiters spanning a / R * = 3–20 (≈0.01–0.1 au) and warm Jupiters spanning a / R * = 20–400 (≈0.1–1.9 au) are in good agreement. With 90% confidence, at least 24 − 10 + 7 % of warm Jupiters and 14 − 8 + 5 % of hot Jupiters around cool stars are misaligned by at least 10°. Most stars harboring warm Jupiters are therefore consistent with spin–orbit alignment. The similarity of the hot and warm Jupiter misalignment rates suggests that either the occasional misalignments are primordial and originate in misaligned disks, or the same underlying processes that create misaligned hot Jupiters also lead to misaligned warm Jupiters.
From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently ...more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people's lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 Formula: see text and 0.012 MAPE at world level with 31-step forecast and up to 0.99 Formula: see text and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.
Abstract
The orientation between a star’s spin axis and a planet’s orbital plane provides valuable information about the system’s formation and dynamical history. For non-transiting planets at wide ...separations, true stellar obliquities are challenging to measure, but lower limits on spin–orbit orientations can be determined from the difference between the inclination of the star’s rotational axis and the companion’s orbital plane (Δ
i
). We present results of a uniform analysis of rotation periods, stellar inclinations, and obliquities of cool stars (SpT ≳ F5) hosting directly imaged planets and brown dwarf companions. As part of this effort, we have acquired new
v
sin
i
*
values for 22 host stars with the high-resolution Tull spectrograph at the Harlan J. Smith telescope. Altogether our sample contains 62 host stars with rotation periods, most of which are newly measured using light curves from the Transiting Exoplanet Survey Satellite. Among these, 53 stars have inclinations determined from projected rotational and equatorial velocities, and 21 stars predominantly hosting brown dwarfs have constraints on Δ
i
. Eleven of these (52
−
11
+
10
% of the sample) are likely misaligned, while the remaining 10 host stars are consistent with spin–orbit alignment. As an ensemble, the minimum obliquity distribution between 10 and 250 au is more consistent with a mixture of isotropic and aligned systems than either extreme scenario alone—pointing to direct cloud collapse, formation within disks bearing primordial alignments and misalignments, or architectures processed by dynamical evolution. This contrasts with stars hosting directly imaged planets, which show a preference for low obliquities. These results reinforce an emerging distinction between the orbits of long-period brown dwarfs and giant planets in terms of their stellar obliquities and orbital eccentricities.
Abstract We report the discovery of a hot Jupiter candidate orbiting HS Psc, a K7 (≈0.7 M ⊙ ) member of the ≈130 Myr AB Doradus moving group. Using radial velocities over 4 yr from the Habitable-zone ...Planet Finder spectrograph at the Hobby–Eberly Telescope, we find a periodic signal of P b = 3.986 − 0.003 + 0.044 days. A joint Keplerian and Gaussian process stellar activity model fit to the radial velocities yields a minimum mass of m p sin i = 1.5 − 0.4 + 0.6 M Jup . The stellar rotation period is well constrained by the Transiting Exoplanet Survey Satellite light curve ( P rot = 1.086 ± 0.003 days) and is not an integer harmonic nor alias of the orbital period, supporting the planetary nature of the observed periodicity. HS Psc b joins a small population of young, close-in giant planet candidates with robust age and mass constraints and demonstrates that giant planets can either migrate to their close-in orbital separations by 130 Myr or form in situ. Given its membership in a young moving group, HS Psc represents an excellent target for follow-up observations to characterize this young hot Jupiter further, refine its orbital properties, and search for additional planets in the system.
Abstract
We present the direct-imaging discovery of a giant planet orbiting the young star AF Lep, a 1.2
M
⊙
member of the 24 ± 3 Myr
β
Pic moving group. AF Lep was observed as part of our ongoing ...high-contrast imaging program targeting stars with astrometric accelerations between Hipparcos and Gaia that indicate the presence of substellar companions. Keck/NIRC2 observations in
L
′
with the vector vortex coronagraph reveal a point source, AF Lep b, at ≈340 mas, which exhibits orbital motion at the 6
σ
level over the course of 13 months. A joint orbit fit yields precise constraints on the planet’s dynamical mass of
3.2
−
0.6
+
0.7
M
Jup
, semimajor axis of
8.4
−
1.3
+
1.1
au, and eccentricity of
0.24
−
0.15
+
0.27
. AF Lep hosts a debris disk located at ∼50 au, but it is unlikely to be sculpted by AF Lep b, implying there may be additional planets in the system at wider separations. The stellar inclination (
i
*
=
54
−
9
+
11
°
) and orbital inclination (
i
o
=
50
−
12
+
9
°
) are in good agreement, which is consistent with the system having spin–orbit alignment. AF Lep b is the lowest-mass imaged planet with a dynamical mass measurement and highlights the promise of using astrometric accelerations as a tool to find and characterize long-period planets.
Abstract
Benchmark brown dwarf companions with well-determined ages and model-independent masses are powerful tools to test substellar evolutionary models and probe the formation of giant planets and ...brown dwarfs. Here, we report the independent discovery of HIP 21152 B, the first imaged brown dwarf companion in the Hyades, and conduct a comprehensive orbital and atmospheric characterization of the system. HIP 21152 was targeted in an ongoing high-contrast imaging campaign of stars exhibiting proper-motion changes between Hipparcos and Gaia, and was also recently identified by Bonavita et al. (2022) and Kuzuhara et al. (2022). Our Keck/NIRC2 and SCExAO/CHARIS imaging of HIP 21152 revealed a comoving companion at a separation of 0.″37 (16 au). We perform a joint orbit fit of all available relative astrometry and radial velocities together with the Hipparcos-Gaia proper motions, yielding a dynamical mass of
24
−
4
+
6
M
Jup
, which is 1–2
σ
lower than evolutionary model predictions. Hybrid grids that include the evolution of cloud properties best reproduce the dynamical mass. We also identify a comoving wide-separation (1837″ or 7.9 × 10
4
au) early-L dwarf with an inferred mass near the hydrogen-burning limit. Finally, we analyze the spectra and photometry of HIP 21152 B using the Saumon & Marley (2008) atmospheric models and a suite of retrievals. The best-fit grid-based models have
f
sed
= 2, indicating the presence of clouds,
T
eff
= 1400 K, and
log
g
=
4.5
dex
. These results are consistent with the object’s spectral type of T0 ± 1. As the first benchmark brown dwarf companion in the Hyades, HIP 21152 B joins the small but growing number of substellar companions with well-determined ages and dynamical masses.
Abstract Atmospheric escape shapes the fate of exoplanets, with statistical evidence for transformative mass loss imprinted across the mass–radius–insolation distribution. Here, we present transit ...spectroscopy of the highly irradiated, low-gravity, inflated hot Saturn HAT-P-67 b. The Habitable Zone Planet Finder spectra show a detection of up to 10% absorption depth of the 10833 Å helium triplet. The 13.8 hr of on-sky integration time over 39 nights sample the entire planet orbit, uncovering excess helium absorption preceding the transit by up to 130 planetary radii in a large leading tail. This configuration can be understood as the escaping material overflowing its small Roche lobe and advecting most of the gas into the stellar—and not planetary—rest frame, consistent with the Doppler velocity structure seen in the helium line profiles. The prominent leading tail serves as direct evidence for dayside mass loss with a strong day-/nightside asymmetry. We see some transit-to-transit variability in the line profile, consistent with the interplay of stellar and planetary winds. We employ one-dimensional Parker wind models to estimate the mass-loss rate, finding values on the order of 2 × 10 13 g s −1 , with large uncertainties owing to the unknown X-ray and ultraviolet (XUV) flux of the F host star. The large mass loss in HAT-P-67 b represents a valuable example of an inflated hot Saturn, a class of planets recently identified to be rare, as their atmospheres are predicted to evaporate quickly. We contrast two physical mechanisms for runaway evaporation: ohmic dissipation and XUV irradiation, slightly favoring the latter.
•Information from electronic health records can be used to help us understand what contributes to the onset of diseases including type 2 diabetes mellitus.•Machine learning, including deep learning, ...has been used to predict the onset of diseases using information from electronic health records.•Our work is the first to use wide and deep learning, a state-of-the-art deep learning architecture that achieves both memorisation and generalisation abilities, to predict the onset of type 2 diabetes mellitus using electronic health records.•Our algorithm is better at predicting the onset of type 2 diabetes mellitus than other state-of-the-art machine learning algorithms using the same dataset with similar experimental settings.•The synthetic minority over-sampling technique (SMOTE) was found to work better with the wide and deep learning framework than other machine learning algorithms in improving sensitivity for imbalanced electronic health record datasets.
Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM).
The proposed method was implemented by training various models into a logistic loss function using a stochastic gradient descent. We applied this model using public hospital record data provided by the Practice Fusion EHRs for the United States population. The dataset consists of de-identified electronic health records for 9948 patients, of which 1904 have been diagnosed with T2DM. Prediction of diabetes in 2012 was based on data obtained from previous years (2009–2011). The imbalance class of the model was handled by Synthetic Minority Oversampling Technique (SMOTE) for each cross-validation training fold to analyse the performance when synthetic examples for the minority class are created. We used SMOTE of 150 and 300 percent, in which 300 percent means that three new synthetic instances are created for each minority class instance. This results in the approximated diabetes:non-diabetes distributions in the training set of 1:2 and 1:1, respectively.
Our final ensemble model not using SMOTE obtained an accuracy of 84.28%, area under the receiver operating characteristic curve (AUC) of 84.13%, sensitivity of 31.17% and specificity of 96.85%. Using SMOTE of 150 and 300 percent did not improve AUC (83.33% and 82.12%, respectively) but increased sensitivity (49.40% and 71.57%, respectively) with a moderate decrease in specificity (90.16% and 76.59%, respectively).
Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.
