Strengths include the well-defined sampling frame, which allowed correcting for non-response, a sample with approximately equal numbers of TF and TM participants, and the ability to combine data on HT and gender confirmation surgeries. Limitations include the cross-sectional design and the fact that participants may not be representative of the transgender population in the United States. Body-gender congruence and body image satisfaction were higher, and depression and anxiety were lower among individuals who had more extensive GCT compared to those who received less treatment or no treatment at all. Owen-Smith AA, Gerth J, Sineath RC, et al. Association Between Gender Confirmation Treatments and Perceived Gender Congruence, Body Image Satisfaction and Mental Health in a Cohort Of Transgender Individuals. J Sex Med 2018;15:591-600.

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W e consider a class of packing problems with uncertain data, which we refer to as the chance-constrained binary packing problem. In this problem, a subset of items is selected that maximizes the total profit so that a generic packing constraint is satisfied with high probability. Interesting special cases of our problem include chance-constrained knapsack and set packing problems with random coefficients. We propose a problem formulation in its original space based on the so-called probabilistic covers. We focus our solution approaches on the special case in which the uncertainty is represented by a finite number of scenarios. In this case, the problem can be formulated as an integer program by introducing a binary decision variable to represent feasibility of each scenario. We derive a computationally efficient coefficient strengthening procedure for this formulation, and demonstrate how the scenario variables can be efficiently projected out of the linear programming relaxation. We also study how methods for lifting deterministic cover inequalities can be leveraged to perform approximate lifting of probabilistic cover inequalities. We conduct an extensive computational study to illustrate the potential benefits of our proposed techniques on various problem classes.