In sub‐Saharan Africa, Kaposi's sarcoma‐associated herpesvirus (KSHV) is endemic, and Kaposi's sarcoma (KS) is a significant public health problem. Until recently, KSHV genotype analysis was performed using variable gene regions, representing a small fraction of the genome, and thus the contribution of sequence variation to viral transmission or pathogenesis are understudied. We performed near full‐length KSHV genome sequence analysis on samples from 43 individuals selected from a large Cameroonian KS case‐control study. KSHV genomes were obtained from 21 KS patients and 22 control participants. Phylogenetic analysis of the K1 region indicated the majority of sequences were A5 or B1 subtypes and all three K15 alleles were represented. Unique polymorphisms in the KSHV genome were observed including large gene deletions. We found evidence of multiple distinct KSHV genotypes in three individuals. Additionally, our analyses indicate that recombination is prevalent suggesting that multiple KSHV infections may not be uncommon overall. Most importantly, a detailed analysis of KSHV genomes from KS patients and control participants did not find a correlation between viral sequence variations and disease. Our study is the first to systematically compare near full‐length KSHV genome sequences between KS cases and controls in the same endemic region to identify possible sequence variations associated with disease risk.

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Traditional depth sensors generate accurate real world depth estimates that surpass even the most advanced learning approaches trained only on simulation domains. Since ground truth depth is readily available in the simulation domain but quite difficult to obtain in the real domain, we propose a method that leverages the best of both worlds. In this paper we present a new framework, ActiveZero, which is a mixed domain learning solution for active stereovision systems that requires no real world depth annotation. First, we demonstrate the transferability of our method to out-ofdistribution real data by using a mixed domain learning strategy. In the simulation domain, we use a combination of supervised disparity loss and self-supervised losses on a shape primitives dataset. By contrast, in the real domain, we only use self-supervised losses on a dataset that is outof-distribution from either training simulation data or test real data. Second, our method introduces a novel selfsupervised loss called temporal IR reprojection to increase the robustness and accuracy of our reprojections in hardto-perceive regions. Finally, we show how the method can be trained end-to-end and that each module is important for attaining the end result. Extensive qualitative and quantitative evaluations on real data demonstrate state of the art results that can even beat a commercial depth sensor.

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