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1. In vitro demonstration and in planta characterization of a condensed, reverse TCA (crTCA) cycle

   https://doi.org/10.3389/fpls.2025.1556957  

   Wilson, Nathan; Smith-Moore, Caroline; Xu, Yuan; Edwards, Brianne; La_Hovary, Christophe; Li, Kai; Aslett, Denise; Ji, Mikyoung; Lin, Xuli; Vintila, Simina; et al (June 2025, Frontiers in Plant Science)

   IntroductionPlants employ the Calvin-Benson cycle (CBC) to fix atmospheric CO₂for the production of biomass. The flux of carbon through the CBC is limited by the activity and selectivity of Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase (RuBisCO). Alternative CO₂fixation pathways that do not use RuBisCO to fix CO₂have evolved in some anaerobic, autotrophic microorganisms. MethodsRather than modifying existing routes of carbon metabolism in plants, we have developed a synthetic carbon fixation cycle that does not exist in nature but is inspired by metabolisms of bacterial autotrophs. In this work, we build and characterize a condensed, reverse tricarboxylic acid (crTCA) cyclein vitroandin planta. ResultsWe demonstrate that a simple, synthetic cycle can be used to fix carbon in vitro under aerobic and mesophilic conditions and that these enzymes retain activity whenexpressed transientlyin planta. We then evaluate stable transgenic lines ofCamelina sativathat have both phenotypic and physiologic changes. TransgenicC. sativaare shorter than controls with increased rates of photosynthetic CO₂assimilation and changes in photorespiratory metabolism. DiscussionThis first iteration of a build-test-learn phase of the crTCA cycle provides promising evidence that this pathway can be used to increase photosynthetic capacity in plants. 

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2. Diel light cycles affect phytoplankton competition in the global ocean

   https://doi.org/10.1111/geb.13562  

   Tsakalakis, Ioannis; Follows, Michael J.; Dutkiewicz, Stephanie; Follett, Christopher L.; Vallino, Joseph J. (July 2022, Global Ecology and Biogeography)

   Abstract AimLight, essential for photosynthesis, is present in two periodic cycles in nature: seasonal and diel. Although seasonality of light is typically resolved in ocean biogeochemical–ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel light cycle is generally not resolved. The goal of this study is to demonstrate the impact of diel light cycles on phytoplankton competition and biogeography in the global ocean. LocationGlobal ocean. Major taxa studiedPhytoplankton. MethodsWe use a three‐dimensional global ocean model and compare simulations of high temporal resolution with and without diel light cycles. The model simulates 15 phytoplankton types with different cell sizes, encompassing two broad ecological strategies: small cells with high nutrient affinity (gleaners) and larger cells with high maximal growth rate (opportunists). Both are grazed by zooplankton and limited by nitrogen, phosphorus and iron. ResultsSimulations show that diel cycles of light induce diel cycles in limiting nutrients in the global ocean. Diel nutrient cycles are associated with higher concentrations of limiting nutrients, by 100% at low latitudes (−40° to 40°), a process that increases the relative abundance of opportunists over gleaners. Size classes with the highest maximal growth rates from both gleaner and opportunist groups are favoured by diel light cycles. This mechanism weakens as latitude increases, because the effects of the seasonal cycle dominate over those of the diel cycle. Main conclusionsUnderstanding the mechanisms that govern phytoplankton biogeography is crucial for predicting ocean ecosystem functioning and biogeochemical cycles. We show that the diel light cycle has a significant impact on phytoplankton competition and biogeography, indicating the need for understanding the role of diel processes in shaping macroecological patterns in the global ocean. 

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3. A quantitative methodology for measuring the social sustainability of pavement deterioration

   https://doi.org/10.1038/s41598-024-52655-7  

   Okte, Egemen; Boakye, Jessica; Behrend, Mark (January 2024, Scientific Reports)

   Abstract The social pillar of pavement sustainability is understudied compared to economic and environmental pillars, making it difficult to integrate into life-cycle methodologies. While methods such as social life cycle assessment (S-LCA) exist, they usually focus on social governance rather than quantifying the impact of pavement investment decisions on communities. This study introduces a methodology to quantify the impact of road condition on vulnerable communities, specifically Environmental Justice (EJ) communities. The methodology calculates the impact of road condition on residents and analyzes fuel consumption (as an example impact) for road users during recurrent home-work trips as a function of pavement condition for EJ and non-EJ communities. It was found that EJ communities in Massachusetts are twice as likely to live near poor condition roads and consume twice as much excessive fuel during recurrent home-work trips. The proposed method is designed to integrate into existing life-cycle methods and represents a significant step towards integrating equity into pavement management decisions. 

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4. Diurnal Cycles of Synthetic Microwave Sounding Lower-Stratospheric Temperatures from Radio Occultation Observations, Reanalysis, and Model Simulations

   https://doi.org/10.1175/JTECH-D-21-0071.1  

   Sweeney, Aodhan J.; Fu, Qiang (December 2021, Journal of Atmospheric and Oceanic Technology)

   Abstract An observationally based global climatology of the temperature diurnal cycle in the lower stratosphere is derived from 11 different satellites with global positioning system–radio occultation (GPS-RO) measurements from 2006 to 2020. Methods used in our analysis allow for accurate characterization of global stratospheric temperature diurnal cycles, even in the high latitudes where the diurnal signal is small but longer time-scale variability is large. A climatology of the synthetic Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) Temperature in the Lower Stratosphere (TLS) is presented to assess the accuracy of diurnal cycle climatologies for the MSU and AMSU TLS observations, which have traditionally been generated by model data. The TLS diurnal ranges are typically less than 0.4 K in all latitude bands and seasons investigated. It is shown that the diurnal range (maximum minus minimum temperature) of TLS is largest over Southern Hemisphere tropical land in the boreal winter season, indicating the important role of deep convection. The range, phase, and seasonality of the TLS diurnal cycle are generally well captured by the WACCM6 simulation and ERA5 dataset. We also present an observationally based diurnal cycle climatology of temperature profiles from 300 to 10 hPa for various latitude bands and seasons and compare the ERA5 data with the observations. 

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5. Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle

   https://doi.org/10.1029/2021MS002735  

   Howland, Michael F.; Dunbar, Oliver R. A.; Schneider, Tapio (March 2022, Journal of Advances in Modeling Earth Systems)

   Abstract Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top‐of‐atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude compared with anthropogenic climate change in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof‐of‐concept with an idealized general circulation model (GCM). UQ is performed using the calibrate‐emulate‐sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect‐model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of the non‐Gaussian posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the spread of the parameter posterior distribution leads to a reduction in the uncertainty of climate model predictions. 

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