High-fiber diet mitigates maternal obesity-induced cognitive and social dysfunction in the offspring via gut-brain axis

Microalgae are capable of producing sustainable bioproducts and biofuels by using carbon dioxide or other carbon substances in various cultivation modes. It is of great significance to exploit microalgae for the economical viability of biofuels and the revenues from high-value bioproducts. However, the industrial performance of microalgae is still challenged with potential conflict between cost of microalgae cultivation and revenues from them, which is mainly ascribed to the lack of comprehensive understanding of carbon metabolism and energy conversion. In this review, we provide an overview of the recent advances in carbon and energy fluxes of light-dependent reaction, Calvin–Benson–Bassham cycle, tricarboxylic acid cycle, glycolysis pathway and processes of product biosynthesis in microalgae, with focus on the increased photosynthetic and carbon efficiencies. Recent strategies for the enhanced production of bioproducts and biofuels from microalgae are discussed in detail. Approaches to alter microbial physiology by controlling light, nutrient and other environmental conditions have the advantages of increasing biomass concentration and product yield through the efficient carbon conversion. Engineering strategies by regulating carbon partitioning and energy route are capable of improving the efficiencies of photosynthesis and carbon conversion, which consequently realize high-value biomass. The coordination of carbon and energy fluxes is emerging as the potential strategy to increase efficiency of carbon fixation and product biosynthesis. To achieve more desirable high-value products, coordination of multi-stage cultivation with engineering and stress-based strategies occupies significant positions in a long term.

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There is currently much interest in fucoxanthin due to its broad beneficial health effects. The major commercial source of fucoxanthin is marine seaweed, which has many shortcomings, and has thus restricted its large-scale production and more diversified applications. In this study, growth characteristics and fucoxanthin accumulation were evaluated to explore the potential of the marine diatom Nitzschia laevis in fucoxanthin production. The results suggested that heterotrophic culture was more effective for cell growth, while the mixotrophic culture was favorable for fucoxanthin accumulation. A two-stage culture strategy was consequently established. A model of exponential fed-batch culture led to a biomass concentration of 17.25 g/L. A mix of white and blue light significantly increased fucoxanthin content. These outcomes were translated into a superior fucoxanthin productivity of 16.5 mg/(L·d), which was more than 2-fold of the best value reported thus far. The culture method established herein therefore represents a promising strategy to boost fucoxanthin production in N. laevis, which might prove to be a valuable natural source of commercial fucoxanthin.

Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems but cannot solve complex systems, creating a major bottleneck in high-throughput materials discovery. Herein we show how to automate crystal-structure phase mapping. We formulate phase mapping as an unsupervised pattern demixing problem and describe how to solve it using Deep Reasoning Networks (DRNets). DRNets combine deep learning with constraint reasoning for incorporating scientific prior knowledge and consequently require only a modest amount of (unlabeled) data. DRNets compensate for the limited data by exploiting and magnifying the rich prior-knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization. DRNets are designed with an interpretable latent space for encoding prior-knowledge domain constraints and seamlessly integrate constraint reasoning into neural network optimization. DRNets surpass previous approaches on crystal-structure phase mapping, unraveling the Bi-Cu-V oxide phase diagram, and aiding the discovery of solar-fuels materials.