Notes

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Precursor frequency and affinity interdependently influenced responses. Taken together, these experiments utilizing authentic naive human VRC01-class BCRs validate a central tenet of germline-targeting vaccine design and extend the overall concept of the reverse vaccinology approach to vaccine development.Channelrhodopsins are light-gated ion channels widely used to control neuronal firing with light (optogenetics). We report two previously unknown families of anion channelrhodopsins (ACRs), one from the heterotrophic protists labyrinthulea and the other from haptophyte algae. Four closely related labyrinthulea ACRs, named RubyACRs here, exhibit a unique retinal-binding pocket that creates spectral sensitivities with maxima at 590 to 610 nm, the most red-shifted channelrhodopsins known, long-sought for optogenetics, and more broadly the most red-shifted microbial rhodopsins thus far reported. We identified three spectral tuning residues critical for the red-shifted absorption. Photocurrents recorded from the RubyACR from Aurantiochytrium limacinum (designated AlACR1) under single-turnover excitation exhibited biphasic decay, the rate of which was only weakly voltage dependent, in contrast to that in previously characterized cryptophyte ACRs, indicating differences in channel gating mechanisms between the two ACR families. Moreover, in A. limacinum we identified three ACRs with absorption maxima at 485, 545, and 590 nm, indicating color-sensitive photosensing with blue, green, and red spectral variation of ACRs within individual species of the labyrinthulea family. We also report functional energy transfer from a cytoplasmic fluorescent protein domain to the retinal chromophore bound within RubyACRs.Avian-origin influenza viruses overcome the bottleneck of the interspecies barrier and infect humans through the evolution of variants toward more efficient replication in mammals. The dynamic adaptation of the genetic substitutions and the correlation with the virulence of avian-origin influenza virus in patients remain largely elusive. Here, based on the one-health approach, we retrieved the original virus-positive samples from patients with H7N9 and their surrounding poultry/environment. The specimens were directly deep sequenced, and the subsequent big data were integrated with the clinical manifestations. Unlike poultry/environment-derived samples with the consistent dominance of avian signature 627E of H7N9 polymerase basic protein 2 (PB2), patient specimens had diverse ratios of mammalian signature 627K, indicating the rapid dynamics of H7N9 adaptation in patients during the infection process. check details In contrast, both human- and poultry/environment-related viruses had constant dominance of avian signature PB2-701D. The intrahost dynamic adaptation was confirmed by the gradual replacement of 627E by 627K in H7N9 in the longitudinally collected specimens from one patient. These results suggest that host adaptation for better virus replication to new hosts, termed "genetic tuning," actually occurred in H7N9-infected patients in vivo. Notably, our findings also demonstrate the correlation between rapid host adaptation of H7N9 PB2-E627K and the fatal outcome and disease severity in humans. The feature of H7N9 genetic tuning in vivo and its correlation with the disease severity emphasize the importance of testing for the evolution of this avian-origin virus during the course of infection.Bacterial species are hosts to horizontally acquired mobile genetic elements (MGEs), which encode virulence, toxin, antimicrobial resistance, and other metabolic functions. The bipartite genome of Vibrio cholerae harbors sporadic and conserved MGEs that contribute in the disease development and survival of the pathogens. For a comprehensive understanding of dynamics of MGEs in the bacterial genome, we engineered the genome of V. cholerae and examined in vitro and in vivo stability of genomic islands (GIs), integrative conjugative elements (ICEs), and prophages. Recombinant vectors carrying the integration module of these GIs, ICE and CTXΦ, helped us to understand the efficiency of integrations of MGEs in the V. cholerae chromosome. We have deleted more than 250 acquired genes from 6 different loci in the V. cholerae chromosome and showed contribution of CTX prophage in the essentiality of SOS response master regulator LexA, which is otherwise not essential for viability in other bacteria, including Escherichia coli In addition, we observed that the core genome-encoded RecA helps CTXΦ to bypass V. cholerae immunity and allow it to replicate in the host bacterium in the presence of similar prophage in the chromosome. Finally, our proteomics analysis reveals the importance of MGEs in modulating the levels of cellular proteome. This study engineered the genome of V. cholerae to remove all of the GIs, ICEs, and prophages and revealed important interactions between core and acquired genomes.Terrorist attacks often fuel online hate and increase the expression of xenophobic and antiminority messages. Previous research has focused on the impact of terrorist attacks on prejudiced attitudes toward groups linked to the perpetrators as the cause of this increase. We argue that social norms can contain the expression of prejudice after the attacks. We report the results of a combination of a natural and a laboratory-in-the-field (lab-in-the-field) experiment in which we exploit data collected about the occurrence of two consecutive Islamist terrorist attacks in Germany, the Würzburg and Ansbach attacks, in July 2016. The experiment compares the effect of the terrorist attacks in hate speech toward refugees in contexts where a descriptive norm against the use of hate speech is evidently in place to contexts in which the norm is ambiguous because participants observe antiminority comments. Hate toward refugees, but not toward other minority groups, increased as a result of the attacks only in the absence of a strong norm. These results imply that attitudinal changes due to terrorist attacks are more likely to be voiced if norms erode.Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing.
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