Comparative structural investigations of nuclear waste glass alteration layers and sol-gel synthesized aerogels
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Abstract Streptomycetes are highly metabolically gifted bacteria with the abilities to produce bioproducts that have profound economic and societal importance. These bioproducts are produced by metabolic pathways including those for the biosynthesis of secondary metabolites and catabolism of plant biomass constituents. Advancements in genome sequencing technologies have revealed a wealth of untapped metabolic potential from Streptomyces genomes. Here, we report the largest Streptomyces pangenome generated by using 205 complete genomes. Metabolic potentials of the pangenome and individual genomes were analyzed, revealing degrees of conservation of individual metabolic pathways and strains potentially suitable for metabolic engineering. Of them, Streptomyces bingchenggensis was identified as a potent degrader of plant biomass. Polyketide, non-ribosomal peptide, and gamma-butyrolactone biosynthetic enzymes are primarily strain specific while ectoine and some terpene biosynthetic pathways are highly conserved. A large number of transcription factors associated with secondary metabolism are strain-specific while those controlling basic biological processes are highly conserved. Although the majority of genes involved in morphological development are highly conserved, there are strain-specific varieties which may contribute to fine tuning the timing of cellular differentiation. Overall, these results provide insights into the metabolic potential, regulation and physiology of streptomycetes, which will facilitate further exploitation of these important bacteria.
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Abstract Little is known at the transcriptional level about microbial eukaryotic adaptations to short-term salinity change. Arctic microalgae are exposed to low salinity due to sea-ice melt and higher salinity with brine channel formation during freeze-up. Here, we investigate the transcriptional response of an ice-associated microalgae over salinities from 45 to 8. Our results show a bracketed response of differential gene expression when the cultures were exposed to progressively decreasing salinity. Key genes associated with salinity changes were involved in specific metabolic pathways, transcription factors and regulators, protein kinases, carbohydrate active enzymes, and inorganic ion transporters. The pelagophyte seemed to use a strategy involving overexpression of Na + -H + antiporters and Na + -Pi symporters as salinity decreases, but the K + channel complex at higher salinities. Specific adaptation to cold saline arctic conditions was seen with differential expression of several antifreeze proteins, an ice-binding protein and an acyl-esterase involved in cold adaptation.
Abstract Diverse members of early-diverging Mucoromycota, including mycorrhizal taxa and soil-associated Mortierellaceae, are known to harbor Mollicutes-related endobacteria (MRE). It has been hypothesized that MRE were acquired by a common ancestor and transmitted vertically. Alternatively, MRE endosymbionts could have invaded after the divergence of Mucoromycota lineages and subsequently spread to new hosts horizontally. To better understand the evolutionary history of MRE symbionts, we generated and analyzed four complete MRE genomes from two Mortierellaceae genera: Linnemannia (MRE-L) and Benniella (MRE-B). These genomes include the smallest known of fungal endosymbionts and showed signals of a tight relationship with hosts including a reduced functional capacity and genes transferred from fungal hosts to MRE. Phylogenetic reconstruction including nine MRE from mycorrhizal fungi revealed that MRE-B genomes are more closely related to MRE from Glomeromycotina than MRE-L from the same host family. We posit that reductions in genome size, GC content, pseudogene content, and repeat content in MRE-L may reflect a longer-term relationship with their fungal hosts. These data indicate Linnemannia and Benniella MRE were likely acquired independently after their fungal hosts diverged from a common ancestor. This work expands upon foundational knowledge on minimal genomes and provides insights into the evolution of bacterial endosymbionts.
A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.
We consider the problem of computing a sparse binary representation of an image. Given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse binary vector indicating the minimal set of basis vectors that when added together best reconstruct the given input. We formulate this problem with an L 2 loss on the reconstruction error, and an L 0 loss on the binary vector enforcing sparsity. First, we solve the sparse representation QUBOs by solving them both on a D-Wave quantum annealer with Pegasus chip connectivity, as well as on the Intel Loihi 2 spiking neuromorphic processor using a stochastic Non-equilibrium Boltzmann Machine (NEBM). Second, using Quantum Evolution Monte Carlo with Reverse Annealing and iterated warm starting on Loihi 2 to evolve the solution quality from the respective machines. We demonstrate that both quantum annealing and neuromorphic computing are suitable for solving binary sparse coding QUBOs.
Site-specific vibrational probes were used to elucidate the interfacial solvation structure between catalytic active sites and inactive sites on a Au electrode to reveal a unique, opposing cation-dependent double layer structure at active sites.
Fe–N–C catalyst production outperforms Pt/C from an enviro-economic viewpoint, but further Fe–N–C activity development is required before deployment in PEMFCs.
Fractional viscoelastic models provide an excellent description of rheological data for polymer systems with power-law behaviour. However, the physical interpretation of their model parameters, which carry fractional units of time, often remains elusive. We show that for poly(ethylene oxide) (PEO) solutions, the fractional Maxwell model (FMM) requires much fewer model parameters than the classical generalized Maxwell model for a good description of the data and that it can be applied consistently to solutions with varying degrees of viscoelasticity. Despite their fractional units, the parameters exhibit scaling laws similar to classical parameters as a function of polymer concentration.
Through scanning electrochemical microscopy and computational simulation, we show that hydrogen functionalization on graphitic carbon electrodes increases electron transfer kinetics to redox-active species in non-aqueous media.
While hydropower scheduling is a well-defined problem, there are institutional differences that need to be identified to promote constructive and synergistic research. We study how established toolchains of computer models are organized to assist operational hydropower scheduling in Brazil, Norway, and the United States’ Colorado River System (CRS). These three systems have vast hydropower resources, with numerous, geographically widespread, and complex reservoir systems. Although the underlying objective of hydropower scheduling is essentially the same, the systems are operated in different market contexts and with different alternative uses of water, where the stakeholders’ objectives clearly differ. This in turn leads to different approaches when it comes to the scope, organization, and use of models for operational hydropower scheduling and the information flow between the models. Here, we describe these hydropower scheduling toolchains, identify the similarities and differences, and shed light on the original ideas that motivated their creation. We then discuss the need to improve and extend the current toolchains and the opportunities to synergistic research that embrace those contextual differences.
An outstanding challenge in developing a complete equation of state for materials at elevated pressure and temperature is a robust method of determining the bulk temperature state under dynamic conditions. In metals, the determination of bulk temperature states by optical pyrometry is complicated by the small optical depth and thermal conduction effects. These effects lead to observed temperatures differing by 20% or more from the bulk temperature state. In this work, we show the presence of thermal conduction effects in temperature measurements of tin and iron coatings during dynamic compression experiments. We demonstrate that tin, in contrast to iron, coatings can fail to converge to a bulk temperature source over the time scale of the experiment, requiring the experimenter to modify assumptions, design, or analysis. This work bounds thermal transport at shocked conditions.
Fine-structure x-ray spectra have been measured from foils with embedded tracer layers at two laser facilities. In this work, a suite of layered foils with thin Ti tracers under varied tamper layers was studied at both the Titan and the ALEPH 400 nm laser facilities, where Ti Heα emission was recorded using a high-resolution Bragg crystal spectrometer. Several indicators of plasma parameters are examined in the spectra, including temperature- and density-dependent line ratios and line broadening from Stark and opacity effects. Spectra indicate that (1) the plasma density at ALEPH is significantly higher than at Titan and (2) the electron temperature is high for near-surface layers at both facilities but drops more quickly with depth at ALEPH. These inferences of plasma conditions are consistent with differing levels of temporal contrast at each laser facility.
We report the dependence of the magnetization dynamics in a square artificial spin-ice lattice on the in-plane magnetic field angle. Using two complementary measurement techniques—broadband ferromagnetic resonance and micro-focused Brillouin light scattering spectroscopy—we systematically study the evolution of the lattice dynamics, for both a coherent radio frequency excitation and an incoherent thermal excitation of spin dynamics. We observe a splitting of modes facilitated by inter-element interactions that can be controlled by the external field angle and magnitude. Detailed time-dependent micromagnetic simulations reveal that the split modes are localized in different regions of the square network. Furthermore, this observation suggests that it is possible to disentangle modes with different spatial profiles by tuning the external field configuration.
Ab Initio Multiple Spawning (AIMS) simulates the excited-state dynamics of molecular systems by representing nuclear wavepackets in a basis of coupled traveling Gaussian functions, called trajectory basis functions (TBFs). New TBFs are spawned when nuclear wavepackets enter regions of strong nonadiabaticity, permitting the description of non-Born–Oppenheimer processes. The spawning algorithm is simultaneously the blessing and the curse of the AIMS method: it allows for an accurate description of the transfer of nuclear amplitude between different electronic states, but it also dramatically increases the computational cost of the AIMS dynamics as all TBFs are coupled. Recently, a strategy coined stochastic-selection AIMS (SSAIMS) was devised to limit the ever-growing number of TBFs and tested on simple molecules. In this work, we use the photodynamics of three different molecules—cyclopropanone, fulvene, and 1,2-dithiane—to investigate (i) the potential of SSAIMS to reproduce reference AIMS results for challenging nonadiabatic dynamics, (ii) the compromise achieved by SSAIMS in obtaining accurate results while using the smallest average number of TBFs as possible, and (iii) the performance of SSAIMS in comparison to the mixed quantum/classical method trajectory surface hopping (TSH)—both in terms of its accuracy and computational cost. We show that SSAIMS can accurately reproduce the AIMS results for the three molecules considered at a much cheaper computational cost, often close to that of TSH. We deduce from these tests that an overlap-based criterion for the stochastic-selection process leads to the best agreement with the reference AIMS dynamics for the smallest average number of TBFs.