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Contrary to prior published studies, the current data suggest that blastocyst morphologic grading and particularly inner cell mass grade is a useful predictor of OPR per euploid embryo. Morphologic grading should be used to help in the selection among euploid blastocysts.

We consider the following detection problem: given a realization of a symmetric matrix X of dimension n, distinguish between the hypothesis that all upper triangular variables are i.i.d. Gaussians variables with mean 0 and variance 1 and the hypothesis where X is the sum of such matrix and an independent rank-one perturbation.This setup applies to the situation where under the alternative, there is a planted principal submatrix B of size L for which all upper triangular variables are i.i.d. Gaussians with mean 1 and variance 1, whereas all other upper triangular elements of X not in B are i.i.d. Gaussians variables with mean 0 and variance 1. We refer to this as the 'Gaussian hidden clique problem.'When L = (1 + ǫ) √ n (ǫ > 0), it is possible to solve this detection problem with probability 1 − o n (1) by computing the spectrum of X and considering the largest eigenvalue of X. We prove that this condition is tight in the following sense: when L < (1 − ǫ) √ n no algorithm that examines only the eigenvalues of X can detect the existence of a hidden Gaussian clique, with error probability vanishing as n → ∞.We prove this result as an immediate consequence of a more general result on rank-one perturbations of k-dimensional Gaussian tensors. In this context we establish a lower bound on the critical signal-to-noise ratio below which a rank-one signal cannot be detected.

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.

Long-term hormonal contraception use is an independent risk factor for suboptimal response to GnRH-agonist trigger. Patients with very low endogenous serum LH levels on the day of LH trigger are at increased risk for a suboptimal GnRH-agonist trigger response. Understanding the at-risk phenotype and using trigger day LH as a marker for increased risk of suboptimal GnRH-agonist trigger response can be helpful for individualizing treatment and selecting a safe and efficacious trigger medication for patients undergoing IVF.

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