Conditional Injective Flows for Bayesian Imaging

Most deep learning models for computational imaging regress a single reconstructed image. In practice, however, ill-posedness, nonlinearity, model mismatch, and noise often conspire to make such point estimates misleading or insufficient. The Bayesian approach models images and (noisy) measurements as jointly distributed random vectors and aims to approximate the posterior distribution of unknowns. Recent variational inference methods based on conditional normalizing flows are a promising alternative to traditional MCMC methods, but they come with drawbacks: excessive memory and compute demands for moderate to high resolution images and underwhelming performance on hard nonlinear problems. In this work, we propose C-Trumpets -- conditional injective flows specifically designed for imaging problems, which greatly diminish these challenges. Injectivity reduces memory footprint and training time while low-dimensional latent space together with architectural innovations like fixed-volume-change layers and skip-connection revnet layers, C-Trumpets outperform regular conditional flow models on a variety of imaging and image restoration tasks, including limited-view CT and nonlinear inverse scattering, with a lower compute and memory budget. C-Trumpets enable fast approximation of point estimates like MMSE or MAP as well as physically-meaningful uncertainty quantification.Comment: 23 pages, 23 figure

Viral resistance burden and APOBEC editing correlate with virological response in heavily treatment-experienced people living with multi-drug resistant HIV

Background: The impact of drug resistance mutational load and APOBEC editing in heavily treatment-experienced (HTE) people living with multidrug-resistant HIV has not been investigated. Material and methods: We explored the HIV-DNA and HIV-RNA mutational load of drug resistance and APOBEC-related mutations through next-generation sequencing (NGS, Illumina MiSeq) in 20 failing HTE persons enrolled in the PRESTIGIO registry. Results: Patients showed high levels of both HIV-DNA (4.5 [4.0-5.2] log10 copies/106 T-CD4+ cell) and HIV-RNA (4.5 [4.1-5.0] log10 copies/mL) with complex resistance patterns in both compartments. Among the 255 drug resistance mutations found, 66.3% were concordantly detected in both HIV-DNA and HIV-RNA; 71.3% of mutations were already present in historical Sanger genotypes. At intra-patient frequency >5%, a considerable proportion of mutations detected through DNA-NGS were found in historical genotypes but not through RNA-NGS, and few patients had APOBEC-related mutations. Of 14 individuals who switched therapy, those five who failed treatment had DNA resistance with higher intra-patient frequency, higher DNA/RNA mutational load in a context of tendentially less pronounced APOBEC editing compared to those who responded. Conclusions: Using NGS in HIV-DNA and HIV-RNA together with APOBEC editing evaluation might help to identify HTE individuals with MDR who are more prone to experience virological failure