Monkeypox is a viral zoonotic disease endemic in Central and West Africa. In May 2022, dozens of non-endemic countries reported hundreds of monkeypox cases, most with no epidemiological link to Africa. We identified two lineages of monkeypox virus (MPXV) among two 2021 and seven 2022 US monkeypox cases: the major 2022 outbreak variant called B.1 and a minor contemporaneously sampled variant called A.2. Analyses of mutations among these two variants revealed an extreme preference for GA-to-AA mutations indicative of human APOBEC3 cytosine deaminase activity among Clade IIb MPXV (previously West African, Nigeria) sampled since 2017. Such mutations were not enriched within other MPXV clades. These findings suggest that APOBEC3 editing may be a recurrent and a dominant driver of MPXV evolution within the current outbreak.

Summary: We have developed an analysis pipeline to facilitate real-time mutation tracking in SARS-CoV-2, focusing initially on the Spike (S) protein because it mediates infection of human cells and is the target of most vaccine strategies and antibody-based therapeutics. To date we have identified thirteen mutations in Spike that are accumulating. Mutations are considered in a broader phylogenetic context, geographically, and over time, to provide an early warning system to reveal mutations that may confer selective advantages in transmission or resistance to interventions. Each one is evaluated for evidence of positive selection, and the implications of the mutation are explored through structural modeling. The mutation Spike D614G is of urgent concern; it began spreading in Europe in early February, and when introduced to new regions it rapidly becomes the dominant form. Also, we present evidence of recombination between locally circulating strains, indicative of multiple strain infections. These finding have important implications for SARS-CoV-2 transmission, pathogenesis and immune interventions.

The goal of seismic inversion is to obtain subsurface properties from surface measurements. Seismic images have proven valuable, even crucial, for a variety of applications, including subsurface energy exploration, earthquake early warning, carbon capture and sequestration, estimating pathways of subsurface contaminant transport, etc. These subsurface properties (such as wave speed, density, and elastic velocities) influence the transmission of seismic waves through the subsurface media, and well-understood physics models (so-called “forward models”) can be used to predict what surface measurements would be made for any given subsurface configuration.

The central problem in hyperspectral remote sensing is characterizing the material components of a scene based on the spectral radiance observed in the image pixels. What makes this challenging is the fact that the spectral response for a given material exhibits considerable variability from a variety of causes: intrinsic (depending on the composition or morphology of the material), extrinsic (depending on the size of an object or the concentration of the material), or environmental (due to illumination, atmospheric distortion, and so on). In this article, we survey many of the causes of spectral variability, describe spectral models for this variability, and outline some signal processing and target detection strategies for analyzing hyperspectral data in a way that is more robust to this variability.

Simulations applied to hyperspectral imagery from the AVIRIS sensor are employed to quantitatively evaluate the performance of anomalous change detection algorithms. The evaluation methodology reflects the aim of these algorithms, which is to distinguish actual anomalous changes in a pair of images from the incidental differences that pervade the entire scene. By simulating both the anomalous changes and the pervasive differences, accurate and plentiful ground truth is made available, and statistical estimates of detection and false alarm rates can be made. Comparing the receiver operating characteristic (ROC) curves that encapsulate these rates provides a way to identify which algorithms work best under which conditions.

The SARS-CoV-2 B.1.621 (Mu) variant emerged in January 2021 and was categorized as a variant of interest by the World Health Organization in August 2021. This designation prompted us to study the sensitivity of this variant to antibody neutralization. In a live virus neutralization assay with serum samples from individuals vaccinated with the Pfizer/BioNTech or Moderna mRNA vaccines, we measured neutralization antibody titers against B.1.621, an early isolate (spike 614D), and a variant of concern (B.1.351, Beta variant). We observed reduced neutralizing antibody titers against the B.1.621 variant (3.4- to 7-fold reduction, depending on the serum sample and time after the second vaccination) compared to the early isolate and a similar reduction when compared to B.1.351. Likewise, convalescent serum from hamsters previously infected with an early isolate neutralized B.1.621 to a lower degree. Despite this antibody titer reduction, hamsters could not be efficiently rechallenged with the B.1.621 variant, suggesting that the immune response to the first infection is adequate to provide protection against a subsequent infection with the B.1.621 variant.