Enrichment of r-process elements in dwarf spheroidal galaxies in chemo-dynamical evolution model

The rapid neutron-capture process (r-process) is a major process to synthesize elements heavier than iron, but the astrophysical site(s) of r-process is not identified yet. Neutron star mergers (NSMs) are suggested to be a major r-process site from nucleosynthesis studies. Previous chemical evolution studies however require unlikely short merger time of NSMs to reproduce the observed large star-to-star scatters in the abundance ratios of r-process elements relative to iron, [Eu/Fe], of extremely metal-poor stars in the Milky Way (MW) halo. This problem can be solved by considering chemical evolution in dwarf spheroidal galaxies (dSphs) which would be building blocks of the MW and have lower star formation efficiencies than the MW halo. We demonstrate that enrichment of r-process elements in dSphs by NSMs using an N-body/smoothed particle hydrodynamics code. Our high-resolution model reproduces the observed [Eu/Fe] by NSMs with a merger time of 100 Myr when the effect of metal mixing is taken into account. This is because metallicity is not correlated with time up to ~ 300 Myr from the start of the simulation due to low star formation efficiency in dSphs. We also confirm that this model is consistent with observed properties of dSphs such as radial profiles and metallicity distribution. The merger time and the Galactic rate of NSMs are suggested to be <~ 300 Myr and ~ $10^{-4}$ yr$^{-1}$, which are consistent with the values suggested by population synthesis and nucleosynthesis studies. This study supports that NSMs are the major astrophysical site of r-process.Comment: 16 pages, 16 figures, accepted for publication in Ap

SIRIUS Project. IV. The formation history of the Orion Nebula Cluster driven by clump mergers

The Orion Nebula Cluster (ONC) is an excellent example for understanding the formation of star clusters. Recent studies have shown that ONC has three distinct age populations and anisotropy in velocity dispersions, which are key characteristics for understanding the formation history of the ONC. In this study, we perform a smoothed-particle hydrodynamics/$N$-body simulation of star cluster formation from a turbulent molecular cloud. In this simulation, stellar orbits are integrated using a high-order integrator without gravitational softening; therefore, we can follow the collisional evolution of star clusters. We find that hierarchical formation causes episodic star formation that is observed in the ONC. In our simulation, star clusters evolve due to mergers of subclumps. The mergers bring cold gas with the clumps into the forming cluster. This enhances the star formation in the cluster centre. The dense cold gas in the cluster centre continues to form stars until the latest time. This explains the compact distribution of the youngest stars observed in the ONC. Subclump mergers also contribute to the anisotropy in the velocity dispersions and the formation of runaway stars. However, the anisotropy disappears within 0.5 Myr. The virial ratio of the cluster also increases after a merger due to the runaways. These results suggest that the ONC recently experienced a clump merger. We predict that most runaways originated from the ONC have already been found, but walkaways have not.Comment: 15 pages, 21 figures, and 3 tables, accepted for MNRA

3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations

Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for SNe are serious bottlenecks. This is an urgent issue that needs to be resolved for future higher-resolution galaxy simulations. One possible solution would be to use the Hamiltonian splitting method, in which regions requiring short timesteps are integrated separately from the entire system. To apply this method to the particles affected by SNe in a smoothed-particle hydrodynamics simulation, we need to detect the shape of the shell on and within which such SN-affected particles reside during the subsequent global step in advance. In this paper, we develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion. Trained on turbulent cloud simulations with particle mass $m_{\rm gas}=1$M$_\odot$, the model accurately reproduces the anisotropic shell shape, where densities decrease by over 10 per cent by the explosion. We also demonstrate that the model properly predicts the shell radius in the uniform medium beyond the training dataset of inhomogeneous turbulent clouds. We conclude that our model enables the forecast of the shell and its interior where SN-affected particles will be present.Comment: 14 pages, 14 figures, 3 tables, accepted for MNRA

Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations

Some stars are known to explode at the end of their lives, called supernovae (SNe). The substantial amount of matter and energy that SNe release provides significant feedback to star formation and gas dynamics in a galaxy. SNe release a substantial amount of matter and energy to the interstellar medium, resulting in significant feedback to star formation and gas dynamics in a galaxy. While such feedback has a crucial role in galaxy formation and evolution, in simulations of galaxy formation, it has only been implemented using simple {\it sub-grid models} instead of numerically solving the evolution of gas elements around SNe in detail due to a lack of resolution. We develop a method combining machine learning and Gibbs sampling to predict how a supernova (SN) affects the surrounding gas. The fidelity of our model in the thermal energy and momentum distribution outperforms the low-resolution SN simulations. Our method can replace the SN sub-grid models and help properly simulate un-resolved SN feedback in galaxy formation simulations. We find that employing our new approach reduces the necessary computational cost to $\sim$ 1 percent compared to directly resolving SN feedback.Comment: 11 pages, 9 figures, Accepted for the NeurIPS 2023 AI4Science Worksho

National survey of the association of depressive symptoms with the number of off duty and on-call, and sleep hours among physicians working in Japanese hospitals: a cross sectional study

<p>Abstract</p> <p>Background</p> <p>Physicians' mental health may be adversely affected by the number of days of work and time spent on-call, and improved by sleep and days-off. The aim of this study was to determine the associations of depressive symptoms with taking days of off duty, hours of sleep, and the number of days of on-call and overnight work among physicians working in Japanese hospitals.</p> <p>Methods</p> <p>A cross-sectional study as a national survey was conducted by mail. The study population was 10,000 randomly selected physicians working in hospitals who were also members of the Japan Medical Association (response rate 40.5%). Self-reported anonymous questionnaire was sent to assess the number of days off-duty, overnight work, and on-calls, and the average number of sleep hours on days not working overnight in the previous one month. Depressive state was determined by the Japanese version of the Quick Inventory of Depressive Symptomatology. Logistic regression analysis was used to explore the associations between depressive symptoms and the studied variables.</p> <p>Results</p> <p>Among the respondents, 8.3% of men and 10.5% of women were determined to be depressed. For both men and women, depressive state was associated with having no off-duty days and averaging less than 5 hours of sleep on days not doing overnight work. Depressive state was positively associated with being on-call more than 5 days per month for men, and more than 8 days per month for women, and was negatively associated with being off-duty more than 8 days per month for men.</p> <p>Conclusion</p> <p>Some physicians need some support to maintain their mental health. Physicians who do not take enough days-off, who reduced sleep hours, and who have certain number of days on-calls may develop depressive symptoms.</p

Cutaneous T-cell-attracting chemokine as a novel biomarker for predicting prognosis of idiopathic pulmonary fibrosis: a prospective observational study

[Background] Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease that leads to respiratory failure and death. Although there is a greater understanding of the etiology of this disease, accurately predicting the disease course in individual patients is still not possible. This study aimed to evaluate serum cytokines/chemokines as potential biomarkers that can predict outcomes in IPF patients. [Methods] A multi-institutional prospective two-stage discovery and validation design using two independent cohorts was adopted. For the discovery analysis, serum samples from 100 IPF patients and 32 healthy controls were examined using an unbiased, multiplex immunoassay of 48 cytokines/chemokines. The serum cytokine/chemokine values were compared between IPF patients and controls; the association between multiplex measurements and survival time was evaluated in IPF patients. In the validation analysis, the cytokines/chemokines identified in the discovery analysis were examined in serum samples from another 81 IPF patients to verify the ability of these cytokines/chemokines to predict survival. Immunohistochemical assessment of IPF-derived lung samples was also performed to determine where this novel biomarker is expressed. [Results] In the discovery cohort, 18 cytokines/chemokines were significantly elevated in sera from IPF patients compared with those from controls. Interleukin-1 receptor alpha (IL-1Rα), interleukin-8 (IL-8), macrophage inflammatory protein 1 alpha (MIP-1α), and cutaneous T-cell-attracting chemokine (CTACK) were associated with survival: IL-1Rα, hazard ratio (HR) = 1.04 per 10 units, 95% confidence interval (95% CI) 1.01–1.07; IL-8, HR = 1.04, 95% CI 1.01–1.08; MIP-1α, HR = 1.19, 95% CI 1.00–1.36; and CTACK, HR = 1.12 per 100 units, 95% CI 1.02–1.21. A replication analysis was performed only for CTACK because others were previously reported to be potential biomarkers of interstitial lung diseases. In the validation cohort, CTACK was associated with survival: HR = 1.14 per 100 units, 95% CI 1.01–1.28. Immunohistochemistry revealed the expression of CTACK and CC chemokine receptor 10 (a ligand of CTACK) in airway and type II alveolar epithelial cells of IPF patients but not in those of controls. [Conclusions] CTACK is a novel prognostic biomarker of IPF