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DOE OSTI2022

Dynamic, Adaptive, Systems and Materials: Complex, Simple and Emergent Behaviors

This program has been funded by DoE/BES for twenty years. It has moved into and out of various subjects as it has developed, but it has retained its focus on complexity and complex systems. The project has evolved in the following way: Self-Assembly and Biomimetic Self-Assembly: All self-assembling systems depend upon a minimum of two types of interaction: a repulsion and an attraction. For the familiar molecular systems, attractive interactions are typically hydrogen bonds and electrostatic interactions. Repulsive interactions include steric effects, hydrophobic effects (in biological systems), and charge-charge repulsion. We have expanded this repertoire to include surface interactions, magnetic interactions, and others. I list these systems in the order in which we have explored them: i) A key emphasis in current work is in understanding how the movement of ions in a magnetic field (the Lorentz effect) interacts with catalytic systems. We have demonstrated that an acceleration in rate of reduction of CO 2 to CO can be accomplished by applying an external magnetic field. This acceleration is largely due to the application of the Lorentz effect on mass transport at the catalyst’s surface. ii) We have also extensively explored the influence of electrostatics, as exhibited in self-assembling systems, by tribocharging. iii) Another key system involves surface tension effects; examples include interactions between heavy particles floating at a liquid-air interface, and interacting by changes in surface area; interactions of bubbles and bubble rafts, behaviors of bubble trains in microfluidic networks, and behaviors of microorganisms in constraining environments. iv) This work has intentionally de-emphasized biological systems; but it does include some work on protein-ligand interactions and interactions among microorganisms. v) We have also explored applications of some of these effects, these explorations include bubble rafts as diffraction gratings, exploration of the structures that can be obtained by tribocharging and uses of these structures in exploring nucleation and melting of crystals. vi) Although not a major focus of this work, several other topics have emerged and offer opportunities for future work. These include the behavior of bubble trains and bubble rafts in microfluidic systems. A particularly interesting example is the formation of bubble trains that repeat in the alteration of large and small bubbles according to rules we do not presently understand, but are uniquely large-period oscillating systems. These systems offer a new route into understanding the instabilities of the type represented by oscillations. vii) We have also begun exploratory projects on magnetic levitation (especially to determine molecular density), and information storage (in molecules). Magnetic Levitation: Self-assembling and biomimetic systems require both attraction and repulsion. We have used electrostatics (tribocharging), interfacial free-energies (surface tension and related forces) and others. Potential uses include reconfigurable diffraction gratings and liquid lenses; exploration of mechanisms in tribocharging; tunneling in EGaIn junctions; and bubble trains (especially in micro-fluidic systems). Examples of systems representing these topics is included in the following papers: Complexity: Disks rotating at a water-air interface; Benard-Marangoni effects; Vortex-Crystals from spinning magnetic disks (Marangoni effects); EGaIn Electrode to study quantum tunneling; Self-Assembly of electrostatically-charged metallic spheres (electrets); Dynamically reconfigurable lens; Using computational designs of ligands for enzymes; Electrostatic self-assembly by tribocharging; Monodisperse bubble trains in microchannel systems; Inverted dripping faucet; Flames; Printing of micro-organisms to regenerate the “ink” of printing device; Using micro-organisms to move loads (“microoxen”); Motion of bacterial swarms near surfaces; Making monodisperse particles in microfluidic systems; Coding/decoding of information stored in droplet trains in microfluidic networks; Magnetic levitation; and Information storage. i) Tribocharging. The change in focus of this work on electrets from the fundamentals of charging to applications of these materials in studying self-assembly using electrostatic interactions. ii) Bubbles in Microchannels. The realization that systems of bubbles in microchannels represented a major opportunity to study complexity in a very tractable system, and the development of a semi-quantitative theory of this subject. iii) Flames. The growth of “flames” remains an exploratory subject for the research, although their currently relatively little active work involving it ongoing. iv) Systems with Microorganisms. The removal of work in biological systems from this project. Based on work supported in this program, we now have a significant project on the development of microfluidic tools for studying C. elegans (a nematode), but this work was not appropriate for a program focused on complexity, and we developed separate support for it. (It is, however, an example of successful seeding of a new area by BES.) The work on electrets has gone through a period in which a part of the program was the subject of a MURI; the focus of this work was to develop materials that did not charge electrostatically on friction or contact. The MURI is now over, and the work on dynamic self-assembly (supported by BES) is the major focus. “Flames” has also enjoyed synergistic support, in terms of a project supported by DARPA on flame suppression (in the absence of extinguishing agents, using acoustic and electrostatic interactions). This work was helpful in understanding some of the basics of flames, but is entirely distinct from the BES focus in complexity. A growing interest is in the Lorentz effect. The Lorentz effect is the force exerted on charged particles (electrons, ions, charged molecules) when they move through a perpendicular magnetic field. The Lorentz effect is almost ubiquitous in modern technology: examples of applications include electric motors, dynamos, cathode ray tubes, many batteries, and most systems that control electrical currents with magnetic forces. We have begun to explore the Lorentz effect in electrochemical systems and heterogeneous catalytic systems involving charged organic species and inorganic ions. This work is still at an early stage, but initial studies that Lorentz effects can be large when ions move through magnetic fields, or magnetic fields move in the presence of ions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Bi-Level Dynamic Optimization with Feedback

This paper considers a bi-level real-time algorithmic framework for networked systems, consisting of several local controllers and a central controller. The central controller issues setpoints to the local controllers to optimize their operational objectives while satisfying system-wide constraints. In this context, the paper develops an online algorithm for tracking the optimal solution of the underlying dynamic optimization problem. The design of the algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements (i.e., feedback). Optimality claims are established in terms of the dynamic regret of the algorithm; the latter is a natural performance criterion in nonstationary environments associated with real-time control problems. Finally, the application of the algorithm to real-time control of power setpoints in an electrical grid is illustrated.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Design for a Soft Error Resilient Dynamic Task-Based Runtime, In: 2015 IEEE International Parallel and Distributed Processing Symposium

As the scale of modern computing systems grows, failures will happen more frequently. On the way to Exactable a generic, low-overhead, resilient extension becomes a desired aptitude of any programming paradigm. In this paper we explore three additions to a dynamic task-based runtime to build a generic framework providing soft error resilience to task-based programming paradigms. The first recovers the application by re-executing the minimum required sub-DAG, the second takes critical checkpoints of the data flowing between tasks to minimize the necessary re-execution, while the last one takes advantage of algorithmic properties to recover the data without re-execution. These mechanisms have been implemented in the PaRSEC task-based runtime framework. Experimental results validate our approach and quantify the overhead introduced by such mechanisms.

Computer Science↗

DOE OSTI2023

Computer-controlled finishing via dynamically constraint position-velocity-time scheduler

In a Computer Numerical Controlled (CNC) finishing process, the target material removal from an optical surface is guided by the convolution between the influence function of a machine tool and its dwell time at certain points over the surface. To reduce dynamics stressing and increase machining efficiency, the dwell time must be converted to varying velocities, which are the actual inputs to the machine tool controller. Conventionally, the conversion assumed constant acceleration and relied on linear motion interpolation, which caused discontinuities in velocities. This unsmooth motion affects the material removal distribution, and, thus, the accuracy of the finished surface shape. Many modern CNC machines support the smoother, cubic-polynomial interpolated Position-Velocity-Time (PVT) motion mode; however, the conventional scheduler may fail to provide suitable velocities for the PVT. Here in this study answers this challenge by proposing a novel PVT-based velocity scheduler that achieves smooth motion while considering CNC dynamic limits. Firstly, the principle of the PVT is explained, and the PVT-based velocity scheduler is formulated. Secondly, a quadratic programming is used to optimize the velocities by imposing the CNC dynamic constraints and the C 1 continuities (zeroth and first derivatives are continuous) simultaneously. Thirdly, the smoothness and accuracy of the scheduled velocities are studied on different kinds of tool paths via simulation. Finally, a sub-0.3 nm level surface finishing experiment using ion beam figuring is demonstrated to verify the feasibility of the proposed method. The PVT-based scheduler and simulator code is open-sourced.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Towards the Development of Molten Salt Reactor Multiphysics Modeling Capabilities: Depletion, Thermochemistry, Reactor Dynamics, and Species Tracking

This report documents the simulation and modeling work performed under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, utilizing the tools and codes developed under the program. The focus in this Fiscal Year (FY) is on the depletion, thermochemistry, reactor dynamics, and species transport in MSRs. The fission products travelling in the entire primary loop is unique in MSRs. There are many physical/chemical mechanisms that can significantly affect the fission products concentration in the fuel salt, e.g., diffusion into gas bubbles, diffusion in the core moderators, deposition on the surface of structures/pipes, etc. The fission product concentration in the fuel salt can potentially affect the neutronics, system dynamics, chemical potentials, etc., so accurately predicting the fission products distribution in the primary loop is important. Under the main objective, different activities are pursued to assess the NEAMS tools applicability for a variety of areas on species transport, including the depletion analyses using Griffin (fission product source), the redox potential assessment at different depletion points, the assessment of xenon transport effect on the neutronics and reactor dynamics, a survey of correlations for calculating the mass transfer coefficient of different fission products, etc.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Dynamics Informed Optimization for Resilient Energy Systems

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the Western System Coordinating Council (WSCC) 9-bus test system using synthetic multi-device outage scenarios. Towards integrating our modeling framework with real threats and more realistic power systems, we explore applying hybrid dynamics to power systems. Our work is applied to basic RL circuits with the ultimate goal of using the methodology to model protective tripping schemes in the grid. Finally, we survey mitigation techniques for HEMP threats and describe a GIS application developed to create threat scenarios in a grid with geographic detail.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Final Technical Report for SISGR: Ultrafast Molecular Scale Chemical Imaging

The Northwestern-Argonne SISGR program utilized newly developed instrumentation and techniques including integrated ultra-high vacuum tip-enhanced Raman spectroscopy/scanning tunneling microscopy (UHV-TERS/STM) and surface-enhanced femtosecond stimulated Raman scattering (SE-FSRS) to advance the spatial and temporal resolution of chemical imaging for the study of photoinduced dynamics of molecules on plasmonically active surfaces. An accompanying theory program addressed modeling of charge transfer processes using constrained density functional theory (DFT) in addition to modeling of SE-FSRS, thereby providing a detailed description of the excited state dynamics. This interdisciplinary and highly collaborative research resulted in 62 publications with ~ 48% of them being co-authored by multiple SISGR team members. A summary of the scientific accomplishments from this SISGR program is provided in this final technical report.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Identifying Aspects of the Post-Transcriptional Program Governing the Proteome of the Green Alga Micromonas pusilla

Micromonas is a unicellular green alga that belongs to the prasinophytes, a sister lineage to land plants. This picoeukaryotic (<2 μm diameter) alga is widespread in the marine environment but still not understood at the cellular level. Here, we examine the mRNA and protein level changes that take place over the course of the day-night cycle using mid-exponential nutrient replete cultures of Micromonas pusilla CCMP1545 grown and analyzed in biological triplicate. During the experiment, samples were collected at key transition points during the diel for evaluation using high-throughput LC-MS proteomics. We also sequenced matched mRNA samples from the same time points, using pair-ended directional Illumina RNA-Seq to investigate the dynamics and relationship between the mRNA and protein expression programs of M. pusilla. Similar to a prior study of the marine cyanobacterium Prochlorococcus, we found significant divergence in the mRNA and proteomics expression dynamics in response to the light:dark cycle. Additionally, expressional responses of genes and the proteins they encoded could also be variable within the same metabolic pathway, such as the oxygenic photosynthesis pathway. A regression framework was used to predict protein levels using both mRNA expression and gene-specific sequence-based features. Several features in the genome sequence were found to influence protein abundance including the codon usage and the length of the 3’ UTR. Collectively, our studies provide insights into the regulation of the proteome over a diel as relationships between the transcriptional and translational programs in the widespread marine green alga Micromonas.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Design of voice coil motor dynamic focusing unit for a laser scanner

Laser scanning systems have been used for material processing tasks such as welding, cutting, marking, and drilling. However, applications have been limited by the small range of motion and slow speed of the focusing unit, which carries the focusing optics. To overcome these limitations, a dynamic focusing system with a long travel range and high speed is needed. In this study, a dynamic focusing unit for a laser scanning system with a voice coil motor (VCM) mechanism is proposed to enable fast speed and a wide focusing range. The VCM has finer precision and higher speed than conventional step motors and a longer travel range than earlier lead zirconium titanate actuators. The system has a hollow configuration to provide a laser beam path. This also makes it compact and transmission-free and gives it low inertia. The VCM's magnetics are modeled using a permeance model. Its design parameters are determined by optimization using the Broyden–Fletcher–Goldfarb–Shanno method and a sequential quadratic programming algorithm. After the VCM is designed, the dynamic focusing unit is fabricated and assembled. The permeance model is verified by a magnetic finite element method simulation tool, Maxwell 2D and 3D, and by measurement data from a gauss meter. The performance is verified experimentally. The results show a resolution of 0.2 μm and travel range of 16 mm. These are better than those of conventional focusing systems; therefore, this focusing unit can be applied to laser scanning systems for good machining capability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

INTERFACES. A Program for Determining the 3D Structures of Surfaces Sites Using NMR Data

Dynamic nuclear polarization surface enhanced NMR spectroscopy has enabled the determination of high-resolution structures from surface-supported molecules, including singlesite heterogeneous catalysts. Structure determinations have largely mimicked the approaches used in biomolecular NMR spectroscopy, namely, using distance measurements to constrain a conformational search. These early demonstrations made use of purpose-built software, which has limited the adoption of the technique. Herein, we describe the open-source program INTERFACES (Interpret NMR to Elucidate or Reconstruct the Full Atomistic Configurations of External Surfaces) which automates the analysis of RE(SP)DOR data as well as the structure determination for surface sites. Distances, angles, dihedral angles, complex orientation, and distance from the support can all be sampled to find all structures that agree with the experimental data. A χ 2 metric is used to define the error ranges of the REDOR fits and produce structures with an arbitrary level of confidence. Structural solutions are then provided as both overlays and ORTEP-like probability ellipsoids.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Evolution of the Design and Fabrication of Astrophysics Targets for Turbulent Dynamo (TDYNO) Experiments on OMEGA

Although the overall function of a campaign’s primary target design may remain unchanged, the components and structure often evolve from one shot day to the next to better meet experimental goals. The target fabrication engineer’s involvement in this evolution can be important for advising modifications in order to improve and simplify assembly at the same time. Highly complex targets are constructed by General Atomics (GA) for astrophysics experiments conducted by the University of Chicago at the OMEGA laser facility. Several novel target components are fabricated, precision-assembled, and extensively measured in support of this campaign, and have evolved over the last three years to improve both the science and assembly. Examples include unique laser machined polyimide grids to enhance plasma mixing at target center, precision micromachined cylindrical shields that also act as component spacers, drawn glass target supports to suspend physics packages at critical distances, and tilted pinholes for collimated proton radiography. Target component fabrication and evolution details for this turbulent dynamics (TDYNO) campaign are presented, along with precision-assembly techniques, metrology methods, and considerations for future TDYNO experiments on OMEGA.

42 ENGINEERING↗

DOE OSTI2023

Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data

Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined basis in the states and finds the expansion coefficients through linear regression with sparsity constraints. This approach requires an accurate estimation of the state time derivatives, which is not necessarily possible in the high-noise regime without additional constraints. We present an approach called Derivative-based SINDy (DSINDy) that combines two novel methods to improve ODE recovery at high-noise levels. First, we denoise the state variables by applying a projection operator that leverages the assumed basis for the system dynamics. Second, we use a second order cone program (SOCP) to find the derivative and governing equations simultaneously. We derive theoretical results for the projection-based denoising step, which allow us to estimate the values of hyperparameters used in the SOCP formulation. This underlying theory helps limit the number of required user-specified parameters. Finally, we present results demonstrating that our approach leads to improved system recovery for the Van der Pol oscillator, the Duffing oscillator, the Rössler attractor, and the Lorenz 96 model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Shape-directed dynamics of active colloids powered by induced-charge electrophoresis

Significance Despite recent advances in the ability to “program” the self-assembly of colloidal components, the resulting structures are often static and therefore incapable of performing dynamic functions such as the ability to actuate, heal, replicate, and compute. The realization of colloidal machines that organize in space and time to perform such functions requires new strategies for encoding the dynamic behaviors of colloidal components. Focusing on active colloids powered by induced-charge electrophoresis, we use theory and simulation to show how the shape of a colloidal particle can be rationally tailored to specify complex motions powered by simple energy inputs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

High Fidelity, “Faster than Real-Time” Simulator for Predicting Power System Dynamic Behavior - Final Technical Report

The “High Fidelity, Faster than RealTime Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of largescale power system dynamics simulation, including (1) a validated faster than real time simulation of both stable and unstable transient dynamics in a largescale positive sequence transmission grid model, (2) a threephase unbalanced simulation platform for modeling new grid devices, such as independently controlled singlephase static var compensators (SVCs), (3) the world’s first high fidelity threephase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a firstofits kind implementation of a singlephase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the longterm, the simulator will form the backbone of the newly conceived hybrid realtime protection and control architecture that will coordinate local controls, widearea measurements, widearea controls and advanced realtime prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the fasterthanrealtime simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three phase unbalanced simulator’s ability to model threephase and single phase networks and devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Microbial spies and bloggers: programming cells to convert environmental information into discernible signals

Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.

analyte↗

DOE OSTI2024

Intra- and inter-subtype HIV diversity between 1994 and 2018 in southern Uganda: a longitudinal population-based study

There is limited data on human immunodeficiency virus (HIV) evolutionary trends in African populations. We evaluated changes in HIV viral diversity and genetic divergence in southern Uganda over a 24-year period spanning the introduction and scale-up of HIV prevention and treatment programs using HIV sequence and survey data from the Rakai Community Cohort Study, an open longitudinal population-based HIV surveillance cohort. Gag (p24) and env (gp41) HIV data were generated from people living with HIV (PLHIV) in 31 inland semi-urban trading and agrarian communities (1994–2018) and four hyperendemic Lake Victoria fishing communities (2011–2018) under continuous surveillance. HIV subtype was assigned using the Recombination Identification Program with phylogenetic confirmation. Inter-subtype diversity was evaluated using the Shannon diversity index, and intra-subtype diversity with the nucleotide diversity and pairwise TN93 genetic distance. Genetic divergence was measured using root-to-tip distance and pairwise TN93 genetic distance analyses. Demographic history of HIV was inferred using a coalescent-based Bayesian Skygrid model. Evolutionary dynamics were assessed among demographic and behavioral population subgroups, including by migration status. 9931 HIV sequences were available from 4999 PLHIV, including 3060 and 1939 persons residing in inland and fishing communities, respectively. In inland communities, subtype A1 viruses proportionately increased from 14.3% in 1995 to 25.9% in 2017 (P < .001), while those of subtype D declined from 73.2% in 1995 to 28.2% in 2017 (P < .001). The proportion of viruses classified as recombinants significantly increased by nearly four-fold from 12.2% in 1995 to 44.8% in 2017. Inter-subtype HIV diversity has generally increased. While intra-subtype p24 genetic diversity and divergence leveled off after 2014, intra-subtype gp41 diversity, effective population size, and divergence increased through 2017. Intra- and inter-subtype viral diversity increased across all demographic and behavioral population subgroups, including among individuals with no recent migration history or extra-community sexual partners. This study provides insights into population-level HIV evolutionary dynamics following the scale-up of HIV prevention and treatment programs. Continued molecular surveillance may provide a better understanding of the dynamics driving population HIV evolution and yield important insights for epidemic control and vaccine development.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Co-benefits of global and regional greenhouse gas mitigation for US air quality in 2050

Policies to mitigate greenhouse gas (GHG) emissions will not only slow climate change but can also have ancillary benefits of improved air quality. Here we examine the co-benefits of both global and regional GHG mitigation for US air quality in 2050 at fine resolution, using dynamical downscaling methods, building on a previous global co-benefits study (West et al., 2013). The co-benefits for US air quality are quantified via two mechanisms: through reductions in co-emitted air pollutants from the same sources and by slowing climate change and its influence on air quality, following West et al. (2013). Additionally, we separate the total co-benefits into contributions from domestic GHG mitigation vs. mitigation in foreign countries. We use the Weather Research and Forecasting (WRF) model to dynamically downscale future global climate to the regional scale and the Sparse Matrix Operator Kernel Emissions (SMOKE) program to directly process global anthropogenic emissions to the regional domain, and we provide dynamical boundary conditions from global simulations to the regional Community Multi-scale Air Quality (CMAQ) model. The total co-benefits of global GHG mitigation from the RCP4.5 scenario compared with its reference are estimated to be higher in the eastern US (ranging from 0.6 to 1.0 µg m -3 ) than the west (0–0.4 µg m -3 ) for fine particulate matter (PM 2.5 ), with an average of 0.47 µg m -3 over the US; for O 3 , the total co-benefits are more uniform at 2–5 ppb, with a US average of 3.55 ppb. Comparing the two mechanisms of co-benefits, we find that reductions in co-emitted air pollutants have a much greater influence on both PM 2.5 (96 % of the total co-benefits) and O 3 (89 % of the total) than the second co-benefits mechanism via slowing climate change, consistent with West et al. (2013). GHG mitigation from foreign countries contributes more to the US O 3 reduction (76 % of the total) than that from domestic GHG mitigation only (24 %), highlighting the importance of global methane reductions and the intercontinental transport of air pollutants. For PM 2.5 , the benefits of domestic GHG control are greater (74 % of total). Since foreign contributions to co-benefits can be substantial, with foreign O 3 benefits much larger than those from domestic reductions, previous studies that focus on local or regional co-benefits may greatly underestimate the total co-benefits of global GHG reductions. We conclude that the US can gain significantly greater domestic air quality co-benefits by engaging with other nations to control GHGs.

54 ENVIRONMENTAL SCIENCES↗