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  1. Free, publicly-accessible full text available April 1, 2024
  2. Abstract

    Solid-oxide fuel cells (SOFCs) offer great promise for producing electricity using a wide variety of fuels such as natural gas, coal gas and gasified carbonaceous solids; however, conventional nickel-based anodes face great challenges due to contaminants in readily available fuels, especially sulphur-containing compounds. Thus, the development of new anode materials that can suppress sulphur poisoning is crucial to the realization of fuel-flexible and cost-effective SOFCs. In this work, La0.1Sr1.9Fe1.4Ni0.1Mo0.5O6–δ (LSFNM) and Pr0.1Sr1.9Fe1.4Ni0.1Mo0.5O6–δ (PSFNM) materials have been synthesized using a sol-gel method in air and investigated as anode materials for SOFCs. Metallic nanoparticle-decorated ceramic anodes were obtained by the reduction of LSFNM and PSFNM in H2 at 850°C, forming a Ruddlesden–Popper oxide with exsolved FeNi3 bimetallic nanoparticles. The electrochemical performance of the Sr2Fe1.4Ni0.1Mo0.5O6–δ ceramic anode was greatly enhanced by La doping of A-sites, resulting in a 44% decrease in the polarization resistance in reducing atmosphere. The maximum power densities of Sr- and Mg-doped LaGaO3 (LSGM) (300 μm) electrolyte-supported single cells with LSFNM as the anode reached 1.371 W cm −2 in H2 and 1.306 W cm–2 in 50 ppm H2S–H2 at 850°C. Meanwhile, PSFNM showed improved sulphur tolerance, which could be fully recovered after six cycles from H2 to 50 ppm H2S–H2 operation. This study indicates that LSFNM and PSFNM are promising high-performance anodes for SOFCs.

     
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  3. Abstract Background

    Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.

    Results

    In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database withMproteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.

    Conclusions

    The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from$$O(M^2)$$O(M2)to$$O(M\log M)$$O(MlogM)for performing an all-against-all PPI prediction for a database withMproteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

     
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  4. null (Ed.)
  5. Selection of mutants in a microbial population depends on multiple cellular traits. In serial-dilution evolution experiments, three key traits are the lag time when transitioning from starvation to growth, the exponential growth rate, and the yield (number of cells per unit resource). Here, we investigate how these traits evolve in laboratory evolution experiments using a minimal model of population dynamics, where the only interaction between cells is competition for a single limiting resource. We find that the fixation probability of a beneficial mutation depends on a linear combination of its growth rate and lag time relative to its immediate ancestor, even under clonal interference. The relative selective pressure on growth rate and lag time is set by the dilution factor; a larger dilution factor favors the adaptation of growth rate over the adaptation of lag time. The model shows that yield, however, is under no direct selection. We also show how the adaptation speeds of growth and lag depend on experimental parameters and the underlying supply of mutations. Finally, we investigate the evolution of covariation between these traits across populations, which reveals that the population growth rate and lag time can evolve a nonzero correlation even if mutations have uncorrelated effects on the two traits. Altogether these results provide useful guidance to future experiments on microbial evolution. 
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