Bi-fidelity Variational Auto-encoder for Uncertainty Quantification

Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical accuracy. To address this trade-off, we propose a novel bi-fidelity formulation of variational auto-encoders (BF-VAE) designed to estimate the uncertainty associated with a QoI from low-fidelity (LF) and high-fidelity (HF) samples of the QoI. This model allows for the approximation of the statistics of the HF QoI by leveraging information derived from its LF counterpart. Specifically, we design a bi-fidelity auto-regressive model in the latent space that is integrated within the VAE's probabilistic encoder-decoder structure. An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost. Additionally, we introduce the concept of the bi-fidelity information bottleneck (BF-IB) to provide an information-theoretic interpretation of the proposed BF-VAE model. Our numerical results demonstrate that BF-VAE leads to considerably improved accuracy, as compared to a VAE trained using only HF data, when limited HF data is available

Genomically Complex Human Angiosarcoma and Canine Hemangiosarcoma Establish Convergent Angiogenic Transcriptional Programs Driven by Novel Gene Fusions

Sporadic angiosarcomas are aggressive vascular sarcomas whose rarity and genomic complexity present significant obstacles in deciphering the pathogenic significance of individual genetic alterations. Numerous fusion genes have been identified across multiple types of cancers, but their existence and significance remain unclear in sporadic angiosarcomas. In this study, we leveraged RNA-sequencing data from 13 human angiosarcomas and 76 spontaneous canine hemangiosarcomas to identify fusion genes associated with spontaneous vascular malignancies. Ten novel protein-coding fusion genes, including TEX2-PECAM1 and ATP8A2-FLT1, were identified in seven of the 13 human tumors, with two tumors showing mutations of TP53. HRAS and NRAS mutations were found in angiosarcomas without fusions or TP53 mutations. We found 15 novel protein-coding fusion genes including MYO16-PTK2, GABRA3-FLT1, and AKT3-XPNPEP1 in 11 of the 76 canine hemangiosarcomas; these fusion genes were seen exclusively in tumors of the angiogenic molecular subtype that contained recurrent mutations in TP53, PIK3CA, PIK3R1, and NRAS. In particular, fusion genes and mutations of TP53 cooccurred in tumors with higher frequency than expected by random chance, and they enriched gene signatures predicting activation of angiogenic pathways. Comparative transcriptomic analysis of human angiosarcomas and canine hemangiosarcomas identified shared molecular signatures associated with activation of PI3K/AKT/mTOR pathways. Our data suggest that genome instability induced by TP53 mutations might create a predisposition for fusion events that may contribute to tumor progression by promoting selection and/or enhancing fitness through activation of convergent angiogenic pathways in this vascular malignancy. IMPLICATIONS: This study shows that, while drive events of malignant vasoformative tumors of humans and dogs include diverse mutations and stochastic rearrangements that create novel fusion genes, convergent transcriptional programs govern the highly conserved morphologic organization and biological behavior of these tumors in both species