Search for: All records

Abstract Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease. 

We present the discovery of TOI-1518b -- an ultra-hot Jupiter orbiting a bright star $V = 8.95$. The transiting planet is confirmed using high-resolution optical transmission spectra from EXPRES. It is inflated, with $R_p = 1.875\pm0.053\,R_{\rm J}$, and exhibits several interesting properties, including a misaligned orbit (${240.34^{+0.93}_{-0.98}}$ degrees) and nearly grazing transit ($b =0.9036^{+0.0061}_{-0.0053}$). The planet orbits a fast-rotating F0 host star ($T_{\mathrm{eff}} \simeq 7300$ K) in 1.9 days and experiences intense irradiation. Notably, the TESS data show a clear secondary eclipse with a depth of $364\pm28$ ppm and a significant phase curve signal, from which we obtain a relative day-night planetary flux difference of roughly 320 ppm and a 5.2$\sigma$ detection of ellipsoidal distortion on the host star. Prompted by recent detections of atomic and ionized species in ultra-hot Jupiter atmospheres, we conduct an atmospheric cross-correlation analysis. We detect neutral iron (${5.2\sigma}$), at $K_p = 157^{+68}_{-44}$ km s$^{-1}$ and $V_{\rm sys} = -16^{+2}_{-4}$ km s$^{-1}$, adding another object to the small sample of highly irradiated gas-giant planets with Fe detections in transmission. Detections so far favor particularly inflated gas giants with radii $rsim 1.78\,R_{\rm J}$; although this may be due to observational bias. With an equilibrium temperature of $T_{\rm eq}=2492\pm38$ K and a measured dayside brightness temperature of $3237\pm59$ K (assuming zero geometric albedo), TOI-1518b is a promising candidate for future emission spectroscopy to probe for a thermal inversion. 

The case‐control design is widely used in retrospective database studies, often leading to spectacular findings. However, results of these studies often cannot be replicated, and the advantage of this design over others is questionable. To demonstrate the shortcomings of applications of this design, we replicate two published case‐control studies. The first investigates isotretinoin and ulcerative colitis using a simple case‐control design. The second focuses on dipeptidyl peptidase‐4 inhibitors and acute pancreatitis, using a nested case‐control design. We include large sets of negative control exposures (where the true odds ratio is believed to be 1) in both studies. Both replication studies produce effect size estimates consistent with the original studies, but also generate estimates for the negative control exposures showing substantial residual bias. In contrast, applying a self‐controlled design to answer the same questions using the same data reveals far less bias. Although the case‐control design in general is not at fault, its application in retrospective database studies, where all exposure and covariate data for the entire cohort are available, is unnecessary, as other alternatives such as cohort and self‐controlled designs are available. Moreover, by focusing on cases and controls it opens the door to inappropriate comparisons between exposure groups, leading to confounding for which the design has few options to adjust for. We argue that this design should no longer be used in these types of data. At the very least, negative control exposures should be used to prove that the concerns raised here do not apply.