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Summary: The SARS-CoV-2 viral pandemic has induced a global health crisis, which requires more in-depth investigation into immunological responses to develop effective treatments and vaccines. To understand protective immunity against COVID-19, we screened over 60,000 asymptomatic individuals in the Southeastern United States for IgG antibody positivity against the viral Spike protein, and approximately 3% were positive. Of these 3%, individuals with the highest anti-S or anti-RBD IgG level showed a strong correlation with inhibition of ACE2 binding and cross-reactivity against non-SARS-CoV-2 coronavirus S-proteins. We also analyzed samples from 94 SARS-CoV-2 patients and compared them with those of asymptomatic individuals. SARS-CoV-2 symptomatic patients had decreased antibody responses, ACE2 binding inhibition, and antibody cross-reactivity. Our study shows that healthy individuals can mount robust immune responses against SARS-CoV-2 without symptoms. Furthermore, IgG antibody responses against S and RBD may correlate with high inhibition of ACE2 binding in individuals tested for SARS-CoV-2 infection or post vaccination.

Abstract Genome‐wide introgression and substitution lines have been developed in many plant species, enhancing mapping precision, gene discovery, and the identification and exploitation of variation from wild relatives. Created over multiple generations of crossing and/or backcrossing accompanied by marker‐assisted selection, the resulting introgression lines are a fixed genetic resource. In this study we report the development of spring wheat (Triticum aestivum L.) chromosome segment substitution lines (CSSLs) generated to systematically capture genetic variation from tetraploid (T. turgidum ssp. dicoccoides) and diploid (Aegilops tauschii) progenitor species. Generated in a common genetic background over four generations of backcrossing, this is a base resource for the mapping and characterization of wheat progenitor variation. To facilitate further exploitation the final population was genetically characterized using a high‐density genotyping array and a range of agronomic and grain traits assessed to demonstrate the potential use of the populations for trait localization in wheat.

Review of Chris Pearson, Dogopolis: How Dogs and Humans Made Modern New York, London, and Paris. Animal Lives. Chicago: Chicago University Press, 2021. 259 pp.; 21 illus. $24.00 (pb), $95.00 (hb).

Birdsong is familiar but enigmatic: to some nothing but mechanical “instinct,” but so excessively exuberant that enthusiasts have linked the songs of birds to the development of the aesthetic sense. Where medieval scholars once saw the origins of music in the imitation of nature, by the Enlightenment birdsong was banished in the anthropocentric definition of art. In contrast to the philosophers, this paper focusses on human intervention in birdsong, focusing on the song of the domestic canary. The canary’s “contrived” song can be understood as a partnership between humans and birds, mediated by technology. Drawing on the European history of canary keeping, in Germany and Russia in particular, we turn first to the training of songbirds. Subsequently, we consider canaries as signature nonhuman performers, in spaces ranging from the home to the stage. Finally, we consider the role of technology, specifically recording and amplification, in making the canary’s song audible to audiences at a distance. The salutary hybridization of animal–human-machine contests the longstanding condescension of philosophers towards mechanical artifice and any contamination of the free beauties of nature.

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.