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Members of the order Methanomicrobiales are abundant, and sometimes dominant, hydrogenotrophic (H2-CO2 utilizing) methanoarchaea in a broad range of anoxic habitats. Despite their key roles in greenhouse gas emissions and waste conversion to methane, little is known about the physiological and genomic bases for their widespread distribution and abundance. In this study, we compared the genomes of nine diverse Methanomicrobiales strains, examined their pangenomes, reconstructed gene flow and identified genes putatively mediating their success across different habitats. Most strains slowly increased gene content whereas one, Methanocorpusculum labreanum, evidenced genome downsizing. Peat-dwelling Methanomicrobiales showed adaptations centered on improved transport of scarce inorganic nutrients and likely use H+ rather than Na+ transmembrane chemiosmotic gradients during energy conservation. In contrast, other Methanomicrobiales show the potential to concurrently use Na+ and H+ chemiosmotic gradients. Analyses also revealed that the Methanomicrobiales lack a canonical electron bifurcation system (MvhABGD) known to produce low potential electrons in other orders of hydrogenotrophic methanogens. Additional putative differences in anabolic metabolism suggest that the dynamics of interspecies electron transfer from Methanomicrobiales syntrophic partners can also differ considerably. Altogether, these findings suggest profound differences in electron trafficking in the Methanomicrobiales compared with other hydrogenotrophs, and warrant further functional evaluations.

Light-induced oxidation of water by photosystem II (PS II) in plants, algae and cyanobacteria has generated most of the dioxygen in the atmosphere. PS II, a membrane-bound multi-subunit pigment protein complex, couples the one-electron photochemistry at the reaction centre with the four-electron redox chemistry of water oxidation at the Mn4CaO5 cluster in the oxygen-evolving complex (OEC). Under illumination, the OEC cycles through five intermediate S-states (S0 to S4), in which S1 is the dark-stable state and S3 is the last semi-stable state before O-O bond formation and O2 evolution. A detailed understanding of the O-O bond formation mechanism remains a challenge, and will require elucidation of both the structures of the OEC in the different S-states and the binding of the two substrate waters to the catalytic site. Here we report the use of femtosecond pulses from an X-ray free electron laser (XFEL) to obtain damage-free, room temperature structures of dark-adapted (S1), two-flash illuminated (2F; S3-enriched), and ammonia-bound two-flash illuminated (2F-NH3; S3-enriched) PS II. Although the recent 1.95 Å resolution structure of PS II at cryogenic temperature using an XFEL provided a damage-free view of the S1 state, measurements at room temperature are required to study the structural landscape of proteins under functional conditions, and also for in situ advancement of the S-states. To investigate the water-binding site(s), ammonia, a water analogue, has been used as a marker, as it binds to the Mn4CaO5 cluster in the S2 and S3 states. Since the ammonia-bound OEC is active, the ammonia-binding Mn site is not a substrate water site. This approach, together with a comparison of the native dark and 2F states, is used to discriminate between proposed O-O bond formation mechanisms.

The mechanical properties of human brain tissue remain far from being fully understood. One aspect that has gained more attention recently is their regional dependency, as the brain's microstructure varies significantly from one region to another. Understanding the correlation between tissue components and the mechanical behavior is an important step toward better understanding how human brain tissue properties change in space and time and to develop highly spatially resolved constitutive models for large-scale brain simulations. Here, we analyze the correlation between human brain tissue components quantified through enzyme-linked immunosorbent assays (ELISA) and material parameters obtained through an inverse parameter identification scheme based on a hyperelastic Ogden model and multimodal mechanical testing data for eight regions of the brain. We use neural networks as a metamodel to save computational costs. The networks are trained on finite element simulation outputs and are able to replace the simulations in the initial optimization step. We identified strong dependencies between mechanical properties and Iba1 associated with microglia cells, collagen VI, GFAP associated with astrocytes, and collagen IV. These results advance our understanding of microstructure-mechanics relations in human brain tissue and will contribute to the development of highly spatially resolved microstructure-informed constitutive models. [ABSTRACT FROM AUTHOR]

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Gamification can increase motivation in learning, and intelligent virtual assistants (IVAs) can support foreign language learning at home. However, there is a lack of design concepts to motivate learners to practice with their IVA. This study combines both concepts and analyzes if audio-gamification can increase engagement to address this research gap. To this end, a one-year long-term field experiment with 230 subjects using a German language learning skill for Amazon Alexa was conducted. A between-subjects design determined differences in learning behavior and learning outcomes between a control group and two gamified groups (achievements and leaderboard). The findings reveal a positive effect on the number of translated vocabulary and learning success. However, only in the group with a leaderboard was a statistically significant effect on the number of translated vocabulary found. These findings imply that audio-gamification can be a helpful tool for increasing motivation to use IVAs for foreign language learning. [ABSTRACT FROM AUTHOR]