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At least 667 records · Page 37

Transcript and metabolite network perturbations in lignin biosynthetic mutants of Arabidopsis

Abstract Lignin, one of the most abundant polymers in plants, is derived from the phenylpropanoid pathway, which also gives rise to an array of metabolites that are essential for plant fitness. Genetic engineering of lignification can cause drastic changes in transcription and metabolite accumulation with or without an accompanying development phenotype. To understand the impact of lignin perturbation, we analyzed transcriptome and metabolite data from the rapidly lignifying stem tissue in 13 selected phenylpropanoid mutants and wild-type Arabidopsis (Arabidopsis thaliana). Our dataset contains 20,974 expressed genes, of which over 26% had altered transcript levels in at least one mutant, and 18 targeted metabolites, all of which displayed altered accumulation in at least one mutant. We found that lignin biosynthesis and phenylalanine supply via the shikimate pathway are tightly co-regulated at the transcriptional level. The hierarchical clustering analysis of differentially expressed genes (DEGs) grouped the 13 mutants into 5 subgroups with similar profiles of mis-regulated genes. Functional analysis of the DEGs in these mutants and correlation between gene expression and metabolite accumulation revealed system-wide effects on transcripts involved in multiple biological processes.

Plant Sciences↗

Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms

The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.

58 GEOSCIENCES↗

The Atacama Cosmology Telescope: Summary of DR4 and DR5 Data Products and Data Access

Two recent large data releases for the Atacama Cosmology Telescope (ACT), called DR4 and DR5, are available for public access. These data include temperature and polarization maps that cover nearly half the sky at arcminute resolution in three frequency bands; lensing maps and component-separated maps covering ~ 2,100 deg 2 of sky; derived power spectra and cosmological likelihoods; a catalog of over 4,000 galaxy clusters; and supporting ancillary products including beam functions and masks. The data and products are described in a suite of ACT papers; here we provide a summary. In order to facilitate ease of access to these data we present a set of Jupyter IPython notebooks developed to introduce users to DR4, DR5, and the tools needed to analyze these data. The data products (excluding simulations) and the set of notebooks are publicly available on the NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA); simulation products are available on the National Energy Research Scientific Computing Center (NERSC).

79 ASTRONOMY AND ASTROPHYSICS↗

The impact of urban configuration types on urban heat islands, air pollution, CO 2 emissions, and mortality in Europe: a data science approach

The world is becoming increasingly urbanized. As cities around the world continue to grow, it is important for urban planners and policymakers to understand how different urban configuration patterns affect the environment and human health. We aimed at identifying European urban configuration types, based on the Local Climate Zones categories and street design variables from Open Street Map, and evaluating their association with motorized traffic flows, Surface Urban Heat Island (SUHI) intensities, tropospheric nitrogen dioxide (NO 2 ), CO 2 per capita emissions and age-standardized mortality. We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal–Wallis test followed by a post-hoc Dunn's test. We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low-density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low-density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO 2 exposure were significantly higher in compact high density and open low rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low-density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO 2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The unusual symmetry of hexafluoro-o-xylene—A microwave spectroscopy and computational study

The rotational constants and quartic centrifugal distortion coefficients of hexafluoro-o-xylene were precisely derived from the 8 GHz to 18 GHz gas phase microwave spectrum. In addition, the rotational constants of all singly substituted 13 C isotopologues were determined. Instead of the intuitively expected symmetry of C 2v , as in o-xylene, calculations with a variety of methods (B3LYP, CAM-B3LYP, ωB97XD, MP2, and coupled-cluster singles, doubles, and perturbative triples) predict a C 2 symmetry structure in which the two CF 3 groups rotate in opposite directions by about 16°. While the experimental results in this study are not capable of proving this unusual symmetry, they can support future microwave, gas phase electron diffraction or nuclear magnetic resonance studies. From the presented data, a preliminary r 0 structure was determined, reproducing the experimental rotational constants with deviations of no more than 15 kHz. Analysis of the interactions between the two CF 3 groups using an effective fragment potential approach identifies two major contributions to their interaction, due to exchange-repulsion and electrostatic repulsion, with electrostatic repulsion responsible for the barrier at the C 2v geometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Muon track reconstruction in a segmented bolometric array using multi-objective optimization

Recent advances in segmented solid-state detector arrays for rare-event searches have allowed the technology to approach the ton-scale in detector mass and the scale of meters in size. Often focused around searches for neutrinoless double-beta decay or direct dark matter detection, such experiments also have the capability to search for exotic particles that leave track-like signatures across their volume. However, the segmented nature of such detector arrays often sets the spatial resolution and makes the problem of reconstructing track-like paths non-trivial. Here, in this paper, we present an algorithm that improves reconstruction of track-like events in segmented detectors using multi-objective optimization — a computational technique that optimizes more than one cost function at a time without specifying a quantitative weighting between them. Such a technique allows the reconstruction of tracks through a detector and the determination of path-lengths through individual elements. When combined with the reconstructed energy depositions in each element this allows for a calculation of the stopping power of track-like particles and opens the door to searches for particles with abnormal stopping power like monopoles or lightly-ionizing particles (LIPs). Results are presented which evaluate the precision of the reconstruction tools as they currently stand against Monte Carlo generated data. The algorithm is presented in the context of the CUORE experiment, but has applications to other segmented calorimeter detectors.

47 OTHER INSTRUMENTATION↗

First test of the consistency relation for the large-scale structure using the anisotropic three-point correlation function of BOSS DR12 galaxies

ABSTRACT We present, for the first time, an observational test of the consistency relation for the large-scale structure (LSS) of the Universe through a joint analysis of the anisotropic two- and three-point correlation functions (2PCF and 3PCF) of galaxies. We parameterize the breakdown of the LSS consistency relation in the squeezed limit by Es, which represents the ratio of the coefficients of the shift terms in the second-order density and velocity fluctuations. Es ≠ 1 is a sufficient condition under which the LSS consistency relation is violated. A novel aspect of this work is that we constrain Es by obtaining information about the non-linear velocity field from the quadrupole component of the 3PCF without taking the squeezed limit. Using the galaxy catalogues in the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12, we obtain $E_{\rm s} = -0.92_{-3.26}^{+3.13}$, indicating that there is no violation of the LSS consistency relation in our analysis within the statistical errors. Our parameterization is general enough that our constraint can be applied to a wide range of theories, such as multicomponent fluids, modified gravity theories, and their associated galaxy bias effects. Our analysis opens a new observational window to test the fundamental physics using the anisotropic higher-order correlation functions of galaxy clustering.

79 ASTRONOMY AND ASTROPHYSICS↗

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗

Neutrino Properties with Ground-based Millimeter-wavelength Line Intensity Mapping

Line intensity mapping (LIM) is emerging as a powerful technique to map the cosmic large-scale structure and to probe cosmology over a wide range of redshifts and spatial scales. We perform Fisher forecasts to determine the optimal design of wide-field ground-based millimeter-wavelength LIM surveys for constraining properties of neutrinos and light relics. We consider measuring the auto-power spectra of several CO rotational lines (from J = 2–1 to J = 6–5) and the [C II ] fine-structure line in the redshift range of 0.25 < z < 12. We study the constraints with and without interloper lines as a source of noise in our analysis, and for several one-parameter and multiparameter extensions of ΛCDM. We show that LIM surveys deployable this decade, in combination with existing cosmic microwave background (CMB; primary) data, could achieve order-of-magnitude improvements over Planck constraints on N eff and M ν . Compared to next-generation CMB and galaxy surveys, a LIM experiment of this scale could achieve bounds that are a factor of ~3 better than those forecasted for surveys such as EUCLID (galaxy clustering), and potentially exceed the constraining power of CMB-S4 by a factor of ~1.5 and ~3 for N eff and M ν , respectively. Finally, we show that the forecasted constraints are not substantially affected when enlarging the parameter space, and additionally demonstrate that such a survey could also be used to measure ΛCDM parameters and the dark energy equation of state exquisitely well.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Amorphous dispersions of flubendazole in hydroxypropyl methylcellulose: Formulation stability assisted by pair distribution function analysis

Here, we use X-ray pair distribution function (PDF) analysis applied to high-energy synchrotron X-ray powder diffraction data to evaluate the amorphous solid dispersions interactions and their aging stability. The obtained systems are based on hydroxypropyl methylcellulose (hypromellose) derivatives and flubendazole (FBZ) drug dispersions prepared using a spray-dryer technique. We carry out stability studies under aging parameters (40 °C/75% relative humidity) to tune the systems’ recrystallization. The results reveal that ion-base interactions between the drug-polymer matrix are responsible for reducing clustering processes yielding slower recrystallization and different ordering in the hypromellose phthalate (HPMCP/FBZ) and hypromellose acetate succinate (HPMC-AS/FBZ) systems and complete drug clustering in hypromellose (HPMC-E3/FBZ). The structural ordering was accessed using differential X-ray PDFs that revealed the region between 3.5 Å and 5.0 Å could be related to FBZ intermolecular interactions and is more ordered for the least stable system (HPMC-E3/FBZ) and less ordered for the most stable system (HPMCP/FBZ). These results show that the ion-base interactions between drug and matrix occur at these intermolecular distances.

60 APPLIED LIFE SCIENCES↗

Atomic arrangements in an amorphous CoFeB ribbon extracted via an analysis of radial distribution functions

Here we discuss the atomic structure of amorphous ferromagnetic FeCoB alloys, which are used widely in spintronics applications. Specifically, we obtain the pair-distribution functions for various atomic pairs based on high-energy x-ray diffraction data taken from an amorphous Co 20 Fe 61 B 19 specimen. We start our reverse Monte Carlo cycles to determine the disordered structure with a two-phase model in which a small amount of cobalt is mixed with Fe 23 B 6 as a second phase. The structure of the alloy is found to be heterogeneous, where the boron atoms drive disorder through the random occupation of the atomic network. Our analysis also indicates the presence of small cobalt clusters that are embedded in the iron matrix and percolating the latter throughout the structure. This morphology can explain the enhanced spin polarization observed in amorphous magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Do blue galaxy-clusters have hot intracluster gas?

Abstract We present herein a systematic X-ray analysis of blue galaxy clusters at z = 0.84 discovered by the Subaru telescope. The sample consisted of 43 clusters identified by combining red-sequence and blue-cloud surveys, covering a wide range of emitter fractions (i.e., 0.3–0.8). The spatial extent of the over-density region of emitter galaxies was approximately 1 Mpc in radius. The average cluster mass was estimated as 0.6(<1.5) × 1014 M⊙ from the stacked weak-lensing measurement. We analyzed the XMM-Newton archival data, and measured the X-ray luminosity of the hot intracluster medium. As a result, diffuse X-ray emission was marginally detected in 14 clusters, yielding an average luminosity of 5 × 1042 erg s−1. To the contrary, it was not significant in 29 clusters. The blue clusters were significantly fainter than the red-dominated clusters, and the X-ray luminosity did not show any meaningful correlation either with emitter fraction or richness. The X-ray surface brightness was low, but the amount of gas mass was estimated to be comparable to that observed in the 1013–1014 M⊙ cluster. Based on the results, we suggest that the blue clusters are at the early formation stage, and the gas is yet to be compressed and heated up to produce appreciable X-rays. Follow-up spectroscopic measurements are essential to clarify the dynamical status and co-evolution of galaxies and hot gas in the blue clusters.

Misato, Rana↗

Accelerating cosmological inference with Gaussian processes and neural networks – an application to LSST Y1 weak lensing and galaxy clustering

ABSTRACT Studying the impact of systematic effects, optimizing survey strategies, assessing tensions between different probes and exploring synergies of different data sets require a large number of simulated likelihood analyses, each of which cost thousands of CPU hours. In this paper, we present a method to accelerate cosmological inference using emulators based on Gaussian process regression and neural networks. We iteratively acquire training samples in regions of high posterior probability which enables accurate emulation of data vectors even in high dimensional parameter spaces. We showcase the performance of our emulator with a simulated 3×2 point analysis of LSST-Y1 with realistic theoretical and systematics modelling. We show that our emulator leads to high-fidelity posterior contours, with an order of magnitude speed-up. Most importantly, the trained emulator can be re-used for extremely fast impact and optimization studies. We demonstrate this feature by studying baryonic physics effects in LSST-Y1 3×2 point analyses where each one of our MCMC runs takes approximately 5 min. This technique enables future cosmological analyses to map out the science return as a function of analysis choices and survey strategy.

Astronomy & Astrophysics↗

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗

Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions

Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately accounting for all interactions requires robust quantum-mechanical (QM) benchmarks, which are scarce for ligand-pocket systems. Additionally, disagreement between “gold standard” Coupled Cluster (CC) and Quantum Monte Carlo (QMC) methods casts doubt on many benchmarks for larger non-covalent systems. We introduce the “QUantum Interacting Dimer” (QUID) benchmark framework containing 170 non-covalent (non-)equilibrium systems modeling chemically and structurally diverse ligand-pocket motifs. Symmetry-adapted perturbation theory shows that QUID broadly covers non-covalent binding motifs and energetic contributions. Robust binding energies are obtained using complementary CC and QMC methods, achieving agreement of 0.5 kcal/mol. The benchmark data analysis reveals that several dispersion-inclusive density functional approximations provide accurate energy predictions, though their atomic van der Waals forces differ in magnitude and orientation. Contrarily, semiempirical methods and empirical force fields require improvements in capturing non-covalent interactions (NCIs) for out-of-equilibrium geometries. The wide span of NCIs, highly accurate interaction energies, and analysis of molecular properties take QUID beyond the “gold standard” for QM benchmarks of ligand-protein systems.

Puleva, Mirela [University of Luxembourg, Luxembou↗

The HST See Change Program. I. Survey Design, Pipeline, and Supernova Discoveries*

The See Change survey was designed to make z > 1 cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with the Hubble Space Telescope (HST) spanning the redshift range z = 1.13–1.75, discovering 57 likely transients and 27 likely SNe Ia at z ~ 0.8–2.3. As in similar previous surveys, this proved to be a highly efficient use of HST for supernova observations; the See Change survey additionally tested the feasibility of maintaining, or further increasing, the efficiency at yet higher redshifts, where we have less detailed information on the expected cluster masses and star formation rates. Furthermore, we find that the resulting number of SNe Ia per orbit is a factor of ~8 higher than for a field search, and 45% of our orbits contained an active SN Ia within 22 rest-frame days of peak, with one of the clusters by itself yielding 6 of the SNe Ia. We present the survey design, pipeline, and supernova discoveries. Novel features include fully blinded supernova searches, the first random forest candidate classifier for undersampled IR data (with a 50% detection threshold within 0.05 mag of human searchers), real-time forward-modeling photometry of candidates, and semi-automated photometric classifications and follow-up forecasts. We also describe the spectroscopic follow-up, instrumental in measuring host galaxy redshifts. The cosmology analysis of our sample will be presented in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5µm 3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250µm 3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. In conclusion, this work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

36 MATERIALS SCIENCE↗