PhyloGFN: Phylogenetic inference with generative flow networks

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods

An Evaluation of the Impact of Mental Illness on Postoperative Breast Reconstruction Revision Surgery

Background:. Breast cancer impacts millions of people yearly affecting various aspects of their lives—including but not limited to mental health. Patients with a known psychiatric history, specifically generalized anxiety disorder (GAD) and/or depression, have previously been shown to have an increased number of revisions after breast reconstruction. Methods:. A commercially available database of 91 million unique patients, PearlDiver, was used to identify patients with breast cancer who underwent autologous free flap breast reconstruction. An average number of revisions were calculated from each group of patients—those with a history of anxiety and/or depression and patients without a history of anxiety and/or depression. A logistic regression was performed to determine risk factors associated with patients undergoing revision surgery. Results:. A total of 39,683 patients with a history of breast cancer underwent autologous breast reconstruction between 2010 and 2020, of which 6308 (15.9%) patients had a history of GAD and/or depression before autologous reconstruction. A total of 13,422 (33.8%) patients received at least one revision surgery. Patients with GAD only, depression only, and concomitant GAD and depression received 1.40 revisions each with no significant differences between the control and any of the study groups (P = 0.956). Logistic regression did not find psychiatric history to be associated with patients undergoing revision surgery (OR, 0.94; 95% CI, 0.89–1.00). Conclusion:. Patients who underwent autologous reconstruction for breast cancer demonstrated no difference in rates of secondary surgical revision, regardless of a concurrent mental health history