We run self-consistent simulations of Milky Way-sized, isolated disk galaxies to study the formation and evolution of a stellar bar as well as a nuclear ring in the presence of gas. We consider two sets of models with cold or warm disks that differ in the radial velocity dispersions, and vary the gas fraction by fixing the total disk mass. A bar forms earlier and more strongly in the cold disks with larger , while gas progressively delays the bar formation in the warm disks. The bar formation enhances a central mass concentration, which in turn causes the bar to decay temporarily, after which it regrows in size and strength, eventually becoming stronger in models with smaller . Although all bars rotate fast in the beginning, they rapidly turn to slow rotators. In our models, only the gas-free, warm disk undergoes rapid buckling instability, while other disks thicken more gradually via vertical heating. The gas driven inward by the bar potential readily forms a star-forming nuclear ring. The ring is very small when it first forms and grows in size over time. The ring star formation rate is episodic and bursty due to feedback, and is well correlated with the mass inflow rate to the ring. Some expanding shells produced by star formation feedback are sheared out in the bar regions and collide with dust lanes to appear as filamentary interbar spurs. The bars and nuclear rings formed in our simulations have properties similar to those in the Milky Way.

We introduce a new approach to classify barred galaxies that utilizes the transverse-to-radial force ratio map (ratio map, hereafter) in a different manner from previous studies. When we display the ratio map in polar coordinates, barred galaxies appear as four aligned, horizontal thick slabs. This characteristic feature enables us to successfully classify barred and nonbarred galaxies with an accuracy of 87%. It yields a bar fraction of 53%, including both SBs and SABs, when applied to 884 nearby (z < 0.01) spiral galaxies from the Sloan Digital Sky Survey/DR7. It also provides the bar strength and length measurements, in particular, separated from the spiral arms. They show good correlations with the measures estimated from ellipse fitting and Fourier analysis. However, we find different tendencies of the bar strength measurements in terms of the Hubble sequence: as the Hubble sequence increases (toward late types), the bar strength and bar ellipticity increase, whereas the dipole Fourier amplitude decreases. We show that the bulge affects the estimation of the bar strength differently, depending on the classification methods. The bulge causes the bar length to be overestimated in all three methods. Meanwhile, we find that barred galaxies show two types of radial profiles of the angle-averaged force ratio: one has a maximum peak (type M) and the other a plateau (type P). Comparison with numerical simulations suggests that type-M bars are more mature than type-P bars in terms of evolutionary stage.

We use hydrodynamic simulations to study the effect of spiral arms on the star formation rate (SFR) in nuclear rings of barred-spiral galaxies. We find that spiral arms can be an efficient means of gas transport from the outskirts to the central parts, provided that the arms are rotating slower than the bar. While the ring star formation in models with no arms or corotating arms is active only during around the bar growth phase, arm-driven gas accretion both significantly enhances and prolongs the ring star formation in models with slow-rotating arms. The arm-enhanced SFR is larger by a factor of ~3-20 than in the no-arm model, with larger values corresponding to stronger and slower arms. Arm-induced mass inflows also make dust lanes stronger. Nuclear rings in slow-arm models are ~45% larger than in the no-arm counterparts. Star clusters that form in a nuclear ring exhibit an age gradient in the azimuthal direction only when the SFR is small, whereas no notable age gradient is found in the radial direction for models with arm-induced star formation.

Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. Results: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.

Lee SW, et al. J Korean Med Sci. 2024 Jul;39:e241. https://doi.org/10.3346/jkms.2024.39.e241

Background: Bronchospasm is a rare but potentially life-threatening complication during anesthesia that requires prompt recognition and management. Traditional auscultation plays a key role but is limited in objective interpretation and continuous monitoring. Case: We report a case of intraoperative bronchospasm during laparoscopic surgery, detected early through real-time acoustic visualization using a digitalized esophageal stethoscope. The visualization of lung sounds facilitated the rapid identification of expiratory wheezing and abnormal spectrogram patterns characteristic of bronchospasm. Immediate intervention with a bronchodilator resolved the condition without further complications. Conclusions: This case demonstrates the utility of visual stethoscopes in enhancing perioperative respiratory management. Real-time visualization and quantification of lung sounds offer anesthesiologists a valuable tool for early diagnosis and collaborative decision-making during critical respiratory events.