8 research outputs found

    SARS-CoV-2 Beta variant infection elicits potent lineage-specific and cross-reactive antibodies

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    SARS-CoV-2 Beta variant of concern (VOC) resists neutralization by major classes of antibodies from COVID-19 patients and vaccinated individuals. Here, serum of Beta-infected patients revealed reduced cross-neutralization of wildtype virus. From these patients, we isolated Beta-specific and cross-reactive receptor-binding domain (RBD) antibodies. The Beta-specificity results from recruitment of VOC-specific clonotypes and accommodation of mutations present in Beta and Omicron into a major antibody class that is normally sensitive to these mutations. The Beta-elicited cross-reactive antibodies share genetic and structural features with wildtype-elicited antibodies, including a public VH1-58 clonotype targeting the RBD ridge. These findings advance our understanding of the antibody response to SARS-CoV-2 shaped by antigenic drift with implications for design of next-generation vaccines and therapeutics

    Atomic force microscopy for surface imaging and characterization of supported nanostructures

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    This chapter presents an overview of Atomic Force Microscopy (AFM) principles followed by details on AFM instrumentation. In particular, the frequency modulation method of the non-contact AFM (NC-AFM) used in ultra-high vacuum conditions is explained in detail. Then, applications of NC-AFM for an atomic-scale range characterization of semiconductor and isolator surfaces as well as supported nanostructures are introduced

    Non-Standard Errors

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    In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants
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