[CII] line intensity mapping the epoch of reionization with the Prime-Cam on FYST

Aims. We predict the three-dimensional intensity power spectrum (PS) of the [CII] 158 μm line throughout the epoch of (and post) reionization at redshifts from ≈3.5 to 8. We study the detectability of the PS in a line intensity mapping (LIM) survey with the Prime-Cam spectral-imager on the Fred Young Submillimeter Telescope (FYST). Methods. We created mock [CII] tomographic scans in redshift bins at z ≈ 3.7, 4.3, 5.8, and 7.4 using the Illustris TNG300-1 ΛCDM simulation and adopting a relation between the star formation activity and the [CII] luminosity (L[CII]) of galaxies. A star formation rate (SFR) was assigned to a dark matter halo in the Illustris simulation in two ways: (i) we adopted the SFR computed in the Illustris simulation and, (ii) we matched the abundance of the halos with the SFR traced by the observed dust-corrected ultraviolet luminosity function of high-redshift galaxies. The L[CII] is related to the SFR from a semi-analytic model of galaxy formation, from a hydrodynamical simulation of a high-redshift galaxy, or from a high-redshift [CII] galaxy survey. The [CII] intensity PS was computed from mock tomographic scans to assess its detectability with the anticipated observational capability of the FYST. Results. The amplitude of the predicted [CII] intensity power spectrum varies by more than a factor of 10, depending on the choice of the halo-to-galaxy SFR and the SFR-to-L[CII] relations. In the planned 4° ×4° FYST LIM survey, we expect a detection of the [CII] PS up to z ≈ 5.8, and potentially even up to z ≈ 7.4. The design of the envisioned FYST LIM survey enables a PS measurement not only in small ( 50 Mpc) clustering-dominated scales making it the first LIM experiment that will place constraints on the SFR-to-L[CII] and the halo-to-galaxy SFR relations simultaneously

Quantitative cardiac MRI

Cardiac MRI has become an indispensable imaging modality in the investigation of patients with suspected heart disease. It has emerged as the gold standard test for cardiac function, volumes, and mass and allows noninvasive tissue characterization and the assessment of myocardial perfusion. Quantitative MRI already has a key role in the development and incorporation of machine learning in clinical imaging, potentially offering major improvements in both workflow efficiency and diagnostic accuracy. As the clinical applications of a wide range of quantitative cardiac MRI techniques are being explored and validated, we are expanding our capabilities for earlier detection, monitoring, and risk stratification of disease, potentially guiding personalized management decisions in various cardiac disease models. In this article we review established and emerging quantitative techniques, their clinical applications, highlight novel advances, and appraise their clinical diagnostic potential

Prevalence and characterization of exercise oscillatory ventilation in apparently healthy individuals at variable risk for cardiovascular disease : a subanalysis of the EURO-EX trial

Introduction: There has been a greater appreciation of several variables obtained by cardiopulmonary exercise testing (CPX). Exercise oscillatory ventilation (EOV) is a CPX pattern that has gained recognition as an ominous marker of poor prognosis in cardiac patients. The purpose of the present study is to characterize whether such an abnormal ventilatory pattern may also be detected in apparently healthy subjects and determine its clinical significance. Methods: The study involved 510 subjects (mean age 60 14 years; 49% male) with a broad cardiovascular (CV) risk factor profile who underwent CPX. Results: The population was divided into two groups according to the presence (17%) or absence of EOV. Subjects with EOV were significantly older and a higher percentage was female. Risk factor profile and medication use was significantly different between subgroups, indicating subjects with EOV had a worse CV risk factor profile and were prescribed CVfocused preventive medications at a significantly higher frequency. Subjects with EOV had comparatively poorer CPX performance and gas exchange phenotype. Multivariate binary logistic regression analysis found being female was the strongest predictor of EOV (odds ratio: 2.77, 95% confidence interval (CI): 1.66-4.61, p < 0.001). A diagnosis of diabetes (odds ratio: 2.40, 95% CI: 1.34\u20134.15.2, p < 0.001) added significant value for predicting EOV and was retained in the regression. The likelihood for EOV for subjects who were female and diagnosed with diabetes was 3.71 (95% CI 1.88\u2013 7.30, p < 0.001). Conclusions: This is the first study to examine EOV prevalence and characterization in apparently healthy persons with results supporting an in-depth definition of abnormal exercise phenotypes