Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and apply a 2D convolutional neural network to simulated noiseless lensing maps covering 96 different cosmological models over a range of {Ωm, σ8}. Using the area of the confidence contour in the {Ωm, σ8} plane as a figure-of-merit, derived from simulated convergence maps smoothed on a scale of 1.0 arcmin, we show that the neural network yields ≈ 5× tighter constraints than the power spectrum, and ≈ 4× tighter than the lensing peaks. Such gains illustrate the extent to which weak lensing data encode cosmological information not accessible to the power spectrum or even other, non-Gaussian statistics such as lensing peaks.

Choroidal vascularity index appears to be a more robust tool compared with SFCT for choroidal changes in Stargardt disease. Choroidal vascularity index can possibly be used as a surrogate marker for disease monitoring. A decrease in CVI was associated with a decrease in visual function in eyes with Stargardt disease.

OCTA reveals that eyes with Best disease have abnormal FAZ, patchy vascularity loss in the superficial and deep layers of the retina and capillary dropout with a hyporeflective centre in CC layer. Further, OCTA is superior to FA in measuring CNV.

RPE damage on FAF appears to be significantly larger than CC layer vessel loss on OCTA, which suggests that RPE damage might precede that of CC.

Patients with RP showed a significant reduction in CVI and an increase in CT as compared to normal eyes. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:191-197.].

We have investigated a recently proposed halo-based model, Camelus, for predicting weak-lensing peak counts, and compared its results over a collection of 162 cosmologies with those from N-body simulations. While counts from both models agree for peaks with S/N > 1 (where S/N is the ratio of the peak height to the r.m.s. shape noise), we find ≈ 50% fewer counts for peaks near S/N = 0 and significantly higher counts in the negative S/N tail. Adding shape noise reduces the differences to within 20% for all cosmologies. We also found larger covariances that are more sensitive to cosmological parameters. As a result, credibility regions in the {Ωm, σ8} are ≈ 30% larger. Even though the credible contours are commensurate, each model draws its predictive power from different types of peaks. Low peaks, especially those with 2 < S/N < 3, convey important cosmological information in N-body data, as shown in [1, 2], but Camelus constrains cosmology almost exclusively from high significance peaks (S/N > 3). Our results confirm the importance of using a cosmology-dependent covariance with at least a 14% improvement in parameter constraints. We identified the covariance estimation as the main driver behind differences in inference, and suggest possible ways to make Camelus even more useful as a highly accurate peak count emulator.

Background The US COVID-19 epidemic impacted counties differently across space and time, though large-scale transmission dynamics are unclear. The study's objective was to group counties with similar trajectories of COVID-19 cases and deaths and identify county-level correlates of the distinct trajectory groups. Methods Daily COVID-19 cases and deaths were obtained from 3141 US counties from January through June 2020. Clusters of epidemic curve trajectories of COVID-19 cases and deaths per 100,000 people were identified with Proc Traj. We utilized polytomous logistic regression to estimate Odds Ratios for trajectory group membership in relation to county-level demographics, socioeconomic factors, school enrollment, employment and lifestyle data. Results Six COVID-19 case trajectory groups and five death trajectory groups were identified. Younger counties, counties with a greater proportion of females, Black and Hispanic populations, and greater employment in private sectors had higher odds of being in worse case and death trajectories. Percentage of counties enrolled in grades 1–8 was associated with earlier-start case trajectories. Counties with more educated adult populations had lower odds of being in worse case trajectories but were generally not associated with worse death trajectories. Counties with higher poverty rates, higher uninsured, and more living in non-family households had lower odds of being in worse case and death trajectories. Counties with higher smoking rates had higher odds of being in worse death trajectory counties. Discussion In the absence of clear guidelines and personal protection, smoking, racial and ethnic groups, younger populations, social, and economic factors were correlated with worse COVID-19 epidemics that may reflect population transmission dynamics during January–June 2020. After vaccination of high-risk individuals, communities with higher proportions of youth, communities of color, smokers, and workers in healthcare, service and goods industries can reduce viral spread by targeting vaccination programs to these populations and increasing access and education on non-pharmaceutical interventions.

No abstract