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Calibration of stochastic agent-based model with Gaussian process surrogate and Stein variational inference
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Variability of moisture in the upper troposphere as inferred from TOVS satellite observations and the ECMWF model analyses in 1989
Large-scale variability of moisture in the upper troposphere is examined using TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) satellite observations and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses from 1989 in the latitude band from 40 deg N to 40 S. To compare these dissimilar datasets, upwelling radiances were computed for the 6-7 micrometer water vapor band from the ECMWF temperature and moisture analyses, and these computed radiances were compared to the corresponding TOVS satellite observations. The ECMWF-based radiances reproduce the general locations and seasonal cycle of the TOVS-observed moisture features, particularly after an improved convective parameterization scheme was adopted by ECMWF in May 1989. However, the ECMWF analysis scheme still results in much milder lateral moisture gradients and seasonal contrasts than indicated by the TOVS observations. Seasonally, the upper troposphere in each hemisphere dries in winter and moistens in summer, but there are regions in each hemisphere that run counter to this seasonal trend, apparently depending on continental- and monsoon-scale dynamics. Dynamically, the TOVS-observed regions of significant subtropical dryness are correlated with persistent subsidence indicated by ECMWF 300-mb vertical velocity analyses. The TOVS radiance observations indicate large variations in space and time of the upper-tropospheric moisture field, which are not fully captured by the ECMWF analyses.
Modeling the Atmospheric Breakup of Varied Asteroid Structures: Inferences for the Chelyabinsk Meteor
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Parameter Inference and Forward Propagation of Uncertainties in Computational Models.
Abstract not provided.
Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical-Argo Floats and the Carbon-Based Productivity Model
Estimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite-based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon-based productivity model (CbPM) to compute vertically-resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float-based, depth-resolved, bio-optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float-based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth-resolved structure in NPP from bio-optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth-reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float-informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically- integrated NPP, where the mean bias between model implementations in depth-integrated productivity south of 30◦S is reduced by 62 % when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.
How well was total ozone abundance inferred with Mariner 9?
Ozone is a key to understanding atmospheric chemistry on Mars. Over 20 photochemical models of the martian atmosphere have been published, and O3 is often used as a benchmark for these models. O3 abundance has been inferred from instrumentation on several spacecraft, with the most complete coverage provided by Mariner 9. The Mariner 9 UV spectrometer scanned from 2100 to 3500 Angstroms with a spectral resolution of 15 Angstroms and an effective field-of-view of approximately 300 sq km. The only atmospheric absorption in the 2000 to 3000 A wavelength region was previously assumed to come from the Hartley band system of ozone, which has an opacity of order unity at winter polar latitudes. Therefore, the amount of ozone was inferred by fitting this absorption feature with laboratory data of ozone absorption. Mars O3 shows strong seasonal and latitudinal variation, with column abundances ranging from 0.2 micron-atm at equatorial latitudes to 60 micron-atm over northern winter polar latitudes (1 micron-atm is a column abundance of 2.689 x 10 exp 15 molecules cm(exp -2)). However, the O3 abundance is never great enough to significantly affect atmospheric temperatures or surface temperatures and frost amounts. Some of the previously-inferred O3 abundances are shown. A radiative transfer computer model is used to re-examine the Mariner 9 UV spectra. Assuming a constant mixing ratio for O3 and no chemical or radiative interaction between O3 and clouds/dust, it is shown that when typical amounts of dust and cloud are present that significant underestimation of O3 abundance occurs. A factor of 3 times as much O3 is needed to generate the same spectrum the spacecraft would measure for a cloudy, dusty atmosphere as for a clear atmosphere. If the scattering properties of martian clouds and dust were well known, then their appearance would not be a problem, as a model would be capable of retrieving the O3 abundance. However, these properties are not well known, which raises doubts about the effectiveness of the UV reflectance spectroscopy technique for measuring O3 abundance on Mars.
Quartet-based inference is statistically consistent under the unified duplication-loss-coalescence model
Abstract Motivation The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. This has motivated an extensive body of work on phylogenetic methods that are statistically consistent under MSC. One such particularly popular method is ASTRAL, a quartet-based species tree inference method. Novel studies suggest that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). GDL is prevalent in evolutionary histories and is the first core process in the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. Results In this work, we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that the quartet-based inference approach is statistically consistent under DLCoal. Supplementary information Supplementary data are available at Bioinformatics online.
Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks
Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.
Curie isotherm surfaces inferred from high-altitude magnetic anomaly data
Two-dimensional Curie depth models along two sections across the western United States are derived using an equivalent layer magnetization model derived from Magsat data, and the results are used to constrain finite element thermal models developed by Mayhew and Majer (1980). Regional heat flow variations predicted by the models compare favorably with those inferred from direct measurements. The methodology is applied to upward-continued aeromagnetic data, and the results are found to be in good agreement with previous Curie depth estimates and average measured heat flow.
Finding Liebig’s law of the minimum
Abstract Liebig’s law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co‐limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co‐limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long‐term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.
Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.
Introduction to our COSPAR-Sponsored Session: Space-Observation Contributions Supporting Climate Action
• The challenge for us at COSPAR in putting this program together was that we are primarily a scientifically oriented organization. “Climate Action,” as it is often expressed publicly, tends to be the domain of “activists.” As scientists, on the other hand, to retain our credibility – and for the contributions we make to continue carrying the weight of scientific truth – it is critical that we remain even-handed in our analysis and our reporting of results. • COSPAR is the Committee on Space Research, and fortunately, much of what we observe from space bears objectively on the attributes of the Earth System and how it is evolving. We can measure from space changes in the cryosphere, in the oceans, the land surface and the atmosphere, frequently, on a global scale, to a degree that is generally not possible any other way. And we have been making such observations, in increasing detail, scope, and precision, for about 60 years. • These data help us characterize the current state and identify patterns of change in the Earth System that allow us to infer processes and to improve climate models. With modeling we can test and refine our inferences, and make some predictions. The results can then inform policy and decision-makers, the public in general, as well as those taking direct action. • So, for today’s session we have invited 7 15-minute presentations spanning a broad range of climate-related topics, covering observations from space that can inform climate-related decision-making – from sea-ice extent and sea-level rise, to land surface properties and land-based water resources, to interactions between land or water surfaces and the atmosphere, to severe events in the atmosphere itself. • We begin with an overview of the observations contributed by one of the premier space agencies, given by NASA Chief Scientist and Senior Climate Advisor Dr. Kate Calvin, which will be followed by talks on what we are learning about specific attributes of the Earth System from space-based measurements that can inform climate-related policy and action.
Robust inference of ecosystem soil water stress from eddy covariance data
Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. Here, we develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data.
Modeling the immune response to HIV infection
The interplay between immune response and HIV is intensely studied via mathematical modeling, with significant insights but few direct answers. Here in this short review, we highlight advances and knowledge gaps across different aspects of immunity. In particular, we identify the innate immune response and its role in priming the adaptive response as ripe for modeling. The latter have been the focus of most modeling studies, but we also synthesize key outstanding questions regarding effector mechanisms of cellular immunity and development of broadly neutralizing antibodies. Thus far, most modeling studies aimed to infer general immune mechanisms; we foresee that significant progress will be made next by detailed quantitative fitting of models to data, and prediction of immune responses.
Modeling the Formation of the Family of the Dwarf Planet Haumea
The dwarf planet (136108) Haumea has an intriguing combination of unique physical properties: near-breakup spin, two regular satellites, and an unexpectedly compact family. While these properties point toward formation by a collision, there is no self-consistent and reasonably probable formation hypothesis that can connect the unusually rapid spin and low relative velocities of Haumea family members (“Haumeans”). We explore and test the proposed formation hypotheses (catastrophic collision, graze-and-merge, and satellite collision) in detail. We flexibly parameterize the properties of the collision (e.g., the collision location) and use simple models for the unique three-dimensional velocity ejection field expected from each model to generate simulated families. These are then compared to the observed Kuiper Belt objects using Bayesian parameter inference, including a mixture model that robustly allows for interlopers from the background population. After testing our methodology, we find that the best match to the observed Haumeans is an essentially isotropic ejection field with a typical velocity of 150 m s{sup −1}. The graze-and-merge formation hypothesis—in which Haumeans are shed due to excess angular momentum—is clearly disfavored because the observed Haumeans are not oriented in a plane. The satellite collision model is also disfavored. Including these new constraints, we present a detailed discussion of the formation hypotheses, including variations, some of which are tested. Some new hypotheses are proposed (a cratering collision and a collision where Haumea’s upper layers are “missing”) and scrutinized. We do not identify a satisfactory formation hypothesis, but we do propose several avenues of additional investigation. In the process of these analyses, we identify many new candidate Haumeans and dynamically confirm seven of them as consistent with the observed family. We also confirm that Haumeans have a shallow size distribution and discuss implications for the discovery and identification of new Haumeans.
Thermal Decomposition Kinetics of 4,6‐Diamino‐5,7‐dinitro‐benzo‐furazan
This experimental study investigated the thermal decomposition kinetics of 4,6-diamino-5,7-dinitro-benzo-furazan (referred to as F1 hereafter)—an important decomposition product of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB—a prototypical insensitive high explosive). Simultaneous differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and mass spectrometry (MS) measurements were employed to determine the decomposition kinetics of F1 and to track the evolution of product gases. The DSC profiles were measured at 10 different heating rates between 0.025°C/min and 10°C/min. The measured exotherms were influenced by F1 melting at heating rates above 0.25°C/min, and corresponding changes in decomposition enthalpy and TGA mass-loss-rate profiles indicated a transition from solid-to-gas decomposition to an increasing contribution from liquid-to-gas decomposition. Analysis of low-heating-rate DSC data between 0.025°C/min and 0.17°C/min with the extended Prout–Tompkins model yielded an activation energy of 305 kJ/mol for solid-to-gas F1 decomposition, higher than previous values inferred from TATB decomposition models involving F1. This study provides the first direct experimental determination of the energy barrier for F1 decomposition. MS measurements showed that the major gaseous products matched species previously reported for TATB decomposition (e.g., CO 2 , HCN, C 2 N 2 , etc.), with water identified as the dominant product. Furthermore, these results provide important experimental constraints for improving chemical kinetics models of TATB decomposition and for predicting the reactivity, stability, and safety of TATB-based high explosives under long-term aging conditions and abnormal thermal environments.