ABCD of IA: A multi-scale agent-based model of T cell activation in inflammatory arthritis.

Biomaterial-based agents have been demonstrated to regulate the function of immune cells in models of autoimmunity. However, the complexity of the kinetics of immune cell activation can present a challenge in optimizing the dose and frequency of administration. Here, we report a model of autoreactive T cell activation, which are key drivers in autoimmune inflammatory joint disease. The model is termed a multi-scale Agent-Based, Cell-Driven model of Inflammatory Arthritis (ABCD of IA). Using kinetic rate equations and statistical theory, ABCD of IA simulated the activation and presentation of autoantigens by dendritic cells, interactions with cognate T cells and subsequent T cell proliferation in the lymph node and IA-affected joints. The results, validated with in vivo data from the T cell driven SKG mouse model, showed that T cell proliferation strongly correlated with the T cell receptor (TCR) affinity distribution (TCR-ad), with a clear transition state from homeostasis to an inflammatory state. T cell proliferation was strongly dependent on the amount of antigen in antigenic stimulus event (ASE) at low concentrations. On the other hand, inflammation driven by Th17-inducing cytokine mediated T cell phenotype commitment was influenced by the initial level of Th17-inducing cytokines independent of the amount of arthritogenic antigen. The introduction of inhibitory artificial antigen presenting cells (iaAPCs), which locally suppress T cell activation, reduced T cell proliferation in a dose-dependent manner. The findings in this work set up a framework based on theory and modeling to simulate personalized therapeutic strategies in IA.

Direct measurements of methane emissions from key facilities in Alberta's oil and gas supply chain.

The Canadian government aims to achieve a 40-45 % reduction of oil and gas (O&G) methane (CH4) emissions by 2025, and 75 % by 2030, although recent studies consistently show that Canada's federal inventory underestimates emissions by a factor of 1.4 to 2.0. We conducted aerial mass balance measurements at sixteen upstream O&G facilities in Alberta between September 29 and November 6, 2021, and our measurements revealed that emissions were, on average, 1.7 (standard deviation (SD): 0.6) times higher than the reported emissions for the same year. On a subsequent campaign from August 12 to September 27, 2022, we focused on understudied O&G sectors covering 24 midstream and end-use facilities. These sites were found to be emitting, on average, 3.4 (SD: 1.1) times more CH4 than reported. By extrapolating our measurements to Alberta, we found that underground gas storage contributed to 1.6 % of provincial O&G emissions, followed by natural gas power stations/refineries less than 1.0 %. The widespread underreporting of CH4 emissions highlights the necessity for more empirical measurements of midstream and end-use facilities.

Attribution of climate forcing to economic sectors.

A much-cited bar chart provided by the Intergovernmental Panel on Climate Change displays the climate impact, as expressed by radiative forcing in watts per meter squared, of individual chemical species. The organization of the chart reflects the history of atmospheric chemistry, in which investigators typically focused on a single species of interest. However, changes in pollutant emissions and concentrations are a symptom, not a cause, of the primary driver of anthropogenic climate change: human activity. In this paper, we suggest organizing the bar chart according to drivers of change-that is, by economic sector. Climate impacts of tropospheric ozone, fine aerosols, aerosol-cloud interactions, methane, and long-lived greenhouse gases are considered. We quantify the future evolution of the total radiative forcing due to perpetual constant year 2000 emissions by sector, most relevant for the development of climate policy now, and focus on two specific time points, near-term at 2020 and long-term at 2100. Because sector profiles differ greatly, this approach fosters the development of smart climate policy and is useful to identify effective opportunities for rapid mitigation of anthropogenic radiative forcing.

Emissions credits: opportunity to promote integrated nitrogen management in the wastewater sector.

Relatively little attention has been paid to integrating gaseous N(2)O generated by wastewater treatment into overall reactive nitrogen (Nr) pollution reduction. We propose that there is potential for substantial reductions in N(2)O emissions through the addition of denitrification processes to existing nitrifying wastewater treatment plants (WWTPs), which are designed to lower ammonia levels but currently do not reduce overall Nr. In addition to providing the benefit of reducing total nitrogen concentrations in the effluent, this kind of WWTP upgrade has been demonstrated to reduce energy consumption and fossil CO(2) emissions. We show that the creation of a greenhouse gas (GHG) crediting system for the wastewater sector could provide a potentially sizable economic incentive on the order of $10 million to $600 million per year in the U.S. for upgrading of nitrifying WWTPs that results in N(2)O reductions, with an ancillary benefit of another $30-100 million per year from electricity savings. Even if biological nitrogen removal (BNR) treatment were mandated by existing and future water quality regulations, a GHG crediting system could still be created to promote BNR design and operation that drive N(2)O emissions below a baseline to even lower levels. In this case GHG credits could offset around 0.5-70% of the operating and maintenance cost for the BNR.

A Global Synthesis Inversion Analysis of Recent Variability in CO2 Fluxes Using GOSAT and In Situ Observations.

The precise contribution of the two major sinks for anthropogenic CO2 emissions, terrestrial vegetation and the ocean, and their location and year-to-year variability are not well understood. Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 are expected to benefit from the increasing measurement density brought by recent in situ and remote CO2 observations. We uniquely apply a batch Bayesian synthesis inversion at relatively high resolution to in situ surface observations and bias-corrected GOSAT satellite column CO2 retrievals to deduce the global distributions of natural CO2 fluxes during 2009-2010. Our objectives include evaluating bottom-up prior flux estimates, assessing the value added by the satellite data, and examining the impacts of inversion technique and assumptions on posterior fluxes and uncertainties. The GOSAT inversion is generally better constrained than the in situ inversion, with smaller posterior regional flux uncertainties and correlations, because of greater spatial coverage, except over North America and high-latitude ocean. Complementarity of the in situ and GOSAT data enhances uncertainty reductions in a joint inversion; however, spatial and temporal gaps in sampling still limit the ability to accurately resolve fluxes down to the sub-continental scale. The GOSAT inversion produces a shift in the global CO2 sink from the tropics to the north and south relative to the prior, and an increased source in the tropics of ~2 Pg C y-1 relative to the in situ inversion, similar to what is seen in studies using other inversion approaches. This result may be driven by sampling and residual retrieval biases in the GOSAT data, as suggested by significant discrepancies between posterior CO2 distributions and surface in situ and HIPPO mission aircraft data. While the shift in the global sink appears to be a robust feature of the inversions, the partitioning of the sink between land and ocean in the inversions using either in situ or GOSAT data is found to be sensitive to prior uncertainties because of negative correlations in the flux errors. The GOSAT inversion indicates significantly less CO2 uptake in summer of 2010 than in 2009 across northern regions, consistent with the impact of observed severe heat waves and drought. However, observations from an in situ network in Siberia imply that the GOSAT inversion exaggerates the 2010-2009 difference in uptake in that region, while the prior CASA-GFED model of net ecosystem production and fire emissions reasonably estimates that quantity. The prior, in situ posterior, and GOSAT posterior all indicate greater uptake over North America in spring to early summer of 2010 than in 2009, consistent with wetter conditions. The GOSAT inversion does not show the expected impact on fluxes of a 2010 drought in the Amazon; evaluation of posterior mole fractions against local aircraft profiles suggests that time-varying GOSAT coverage can bias estimation of flux interannual variability in this region.