Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process

In this work we implement approximate Bayesian computational methods to improve the design of a wound-healing assay used to quantify cell-cell interactions. This is important as cell-cell interactions, such as adhesion and repulsion, have been shown to play a role in cell migration. Initially, we demonstrate with a model of an unrealistic experiment that we are able to identify model parameters that describe agent motility and adhesion, given we choose appropriate summary statistics for our model data. Following this, we replace our model of an unrealistic experiment with a model representative of a practically realisable experiment. We demonstrate that, given the current (and commonly used) experimental set-up, our model parameters cannot be accurately identified using approximate Bayesian computation methods. We compare new experimental designs through simulation, and show more accurate identification of model parameters is possible by expanding the size of the domain upon which the experiment is performed, as opposed to increasing the number of experimental replicates. The results presented in this work therefore describe time and cost-saving alterations for a commonly performed experiment for identifying cell motility parameters. Moreover, this work will be of interest to those concerned with performing experiments that allow for the accurate identification of parameters governing cell migratory processes, especially cell migratory processes in which cell-cell adhesion or repulsion are known to play a significant role

Photometry of Kuiper belt object (486958) Arrokoth from New Horizons LORRI

On January 1st 2019, the New Horizons spacecraft flew by the classical Kuiper belt object (486958) Arrokoth (provisionally designated 2014 MU69), possibly the most primitive object ever explored by a spacecraft. The I/F of Arrokoth is analyzed and fit with a photometric function that is a linear combination of the Lommel-Seeliger (lunar) and Lambert photometric functions. Arrokoth has a geometric albedo of p_v = 0.21_(−0.04)^(+0.05) at a wavelength of 550 nm and ≈0.24 at 610 nm. Arrokoth's geometric albedo is greater than the median but consistent with a distribution of cold classical Kuiper belt objects whose geometric albedos were determined by fitting a thermal model to radiometric observations. Thus, Arrokoth's geometric albedo adds to the orbital and spectral evidence that it is a cold classical Kuiper belt object. Maps of the normal reflectance and hemispherical albedo of Arrokoth are presented. The normal reflectance of Arrokoth's surface varies with location, ranging from ≈0.10–0.40 at 610 nm with an approximately Gaussian distribution. Both Arrokoth's extrema dark and extrema bright surfaces are correlated to topographic depressions. Arrokoth has a bilobate shape and the two lobes have similar normal reflectance distributions: both are approximately Gaussian, peak at ≈0.25 at 610 nm, and range from ≈0.10–0.40, which is consistent with co-formation and co-evolution of the two lobes. The hemispherical albedo of Arrokoth varies substantially with both incidence angle and location, the average hemispherical albedo at 610 nm is 0.063 ± 0.015. The Bond albedo of Arrokoth at 610 nm is 0.062 ± 0.015

Photometry of Kuiper belt object (486958) Arrokoth from New Horizons LORRI

On January 1st 2019, the New Horizons spacecraft flew by the classical Kuiper belt object (486958) Arrokoth (provisionally designated 2014 MU69), possibly the most primitive object ever explored by a spacecraft. The I/F of Arrokoth is analyzed and fit with a photometric function that is a linear combination of the Lommel-Seeliger (lunar) and Lambert photometric functions. Arrokoth has a geometric albedo of p_v = 0.21_(−0.04)^(+0.05) at a wavelength of 550 nm and ≈0.24 at 610 nm. Arrokoth's geometric albedo is greater than the median but consistent with a distribution of cold classical Kuiper belt objects whose geometric albedos were determined by fitting a thermal model to radiometric observations. Thus, Arrokoth's geometric albedo adds to the orbital and spectral evidence that it is a cold classical Kuiper belt object. Maps of the normal reflectance and hemispherical albedo of Arrokoth are presented. The normal reflectance of Arrokoth's surface varies with location, ranging from ≈0.10–0.40 at 610 nm with an approximately Gaussian distribution. Both Arrokoth's extrema dark and extrema bright surfaces are correlated to topographic depressions. Arrokoth has a bilobate shape and the two lobes have similar normal reflectance distributions: both are approximately Gaussian, peak at ≈0.25 at 610 nm, and range from ≈0.10–0.40, which is consistent with co-formation and co-evolution of the two lobes. The hemispherical albedo of Arrokoth varies substantially with both incidence angle and location, the average hemispherical albedo at 610 nm is 0.063 ± 0.015. The Bond albedo of Arrokoth at 610 nm is 0.062 ± 0.015

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography

Aims: To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. Methods and results SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and −17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69–0.87) to 0.83 (0.74–0.91) or 0.84 (0.75–0.92), respectively. Conclusion AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI