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Current sources of fermentation feedstocks, i.e. corn, sugar cane, or plant biomass, fall short of demand for liquid transportation fuels and commodity chemicals in the United States. Aquatic phototrophs including cyanobacteria have the potential to supplement the supply of current fermentable feedstocks. In this strategy, cells are engineered to accumulate storage molecules including glycogen, cellulose, and/or lipid oils that can be extracted from harvested biomass and fed to heterotrophic organisms engineered to produce desired chemical products. In this manuscript, we examine the production of glycogen in the model cyanobacteria, Synechococcus sp. strain PCC 7002, and subsequent conversion of cyanobacterial biomass by an engineered Escherichia coli to octanoic acid as a model product. In effort to maximize glycogen production, we explored the deletion of catabolic enzymes and overexpression of GlgC, an enzyme that catalyzes the first committed step towards glycogen synthesis. We found that deletion of glgP increased final glycogen titers when cells were grown in diurnal light. Overexpression of GlgC led to a temporal increase in glycogen content but not in an overall increase in final titer or content. The best strains were grown, harvested, and used to formulate media for growth of E. coli. The cyanobacterial media was able to support the growth of an engineered E. coli and produce octanoic acid at the same titer as common laboratory media.

Abstract Background Biofuel research that aims to optimize growth conditions in microalgae is critically important. Chlamydomonas reinhardtii is a green microalga that offers advantages for biofuel production research. This study compares the effects of nitrogen-, sulfur-, and nitrogen and sulfur- deprivations on the C. reinhardtii starchless mutant cc5373-sta6. Specifically, it compares growth, lipid body accumulation, and expression levels of acetyl-CoA carboxylase (ACC) and phosphoenolpyruvate carboxylase (PEPC). Results Among nutrient-deprived cells, TAP-S cells showed significantly higher total chlorophyll, cell density, and protein content at day 6 (p < 0.05). Confocal analysis showed a significantly higher number of lipid bodies in cells subjected to nutrient deprivation than in the control over the course of six days; N deprivation for six days significantly increased the size of lipid bodies (p < 0.01). In comparison with the control, significantly higher ACC expression was observed after 8 and 24 h of NS deprivation and only after 24 h with N deprivation. On the other hand, ACC and PEPC expression at 8 and 24 h of S deprivation was not significantly different from that in the control. A significantly lower PEPC expression was observed after 8 h of N and NS deprivation (p < 0.01), but a significantly higher PEPC expression was observed after 24 h (p < 0.01). Conclusions Based on our findings, it would be optimum to cultivate cc5373-sta6 cells in nutrient deprived conditions (-N, -S or –NS) for four days; whereby there is cell growth, and both a high number of lipid bodies and a larger size of lipid bodies produced.

AbstractSpecies extinction risk is accelerating due to anthropogenic climate change, making it urgent to protect vulnerable species through legal frameworks in order to facilitate conservation actions that help mitigate risk. Here, we discuss fundamental concepts for assessing climate change risks to species using the example of the emperor penguin (Aptenodytes forsteri), currently being considered for protection under the US Endangered Species Act (ESA). This species forms colonies on Antarctic sea ice, which is projected to significantly decline due to ongoing greenhouse gas (GHG) emissions. We project the dynamics of all known emperor penguin colonies under different GHG emission scenarios using a climate‐dependent meta‐population model including the effects of extreme climate events based on the observational satellite record of colonies. Assessments for listing species under the ESA require information about how species resiliency, redundancy and representation (3Rs) will be affected by threats within the foreseeable future. Our results show that if sea ice declines at the rate projected by climate models under current energy system trends and policies, the 3Rs would be dramatically reduced and almost all colonies would become quasi‐extinct by 2100. We conclude that the species should be listed as threatened under the ESA.

AbstractWe study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables $$Q^2$$ Q 2 and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods.

To use piston engines and their pressure and temperature histories as a chemical rector to produce valuable chemicals, accurate chemical kinetics models are necessary so the engine parameters and mixtures can be designed. Using piston engines in such a way could lead to the production of target chemicals with net-negative CO2 emissions. This benefit is especially true for ethanol, a very common biofuel. To validate models, the dry reforming of ethanol and CO2 was investigated experimentally in a shock tube (50/50 ethanol/CO2 in 99.75% dilution) by following the formation of CO with a laser absorption setup. The effect of the presence of CO2 in the mixture was also investigated by comparing the results of the 50/50 ethanol/CO2 mixture with those from the pyrolysis of ethanol (without CO2) at various concentrations (99.75 and 98% dilution). The 98% dilution results compare extremely well with results from the literature, whereas the other conditions had never been investigated prior. The experimental data were compared to modern detailed kinetics mechanisms, and the experimental CO profiles were poorly predicted overall. Recent literature developments on ethanol pyrolysis were incorporated into the models, and the predictions were improved in some cases and conditions, although further improvements are still necessary. A numerical analysis (rate of production and sensitivity) was performed for the most accurate modified model to highlight the most important reactions involved in the formation of CO under our conditions.

AbstractData capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non‐forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC‐BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.