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At least 325 records · Page 18

Identifying Spectral Descriptors for Protonation in BaZr 0.8 Y 0.2 O 3-x with Electron Energy Loss Spectroscopy

Hydrogen economy is of paramount importance in the global transition to a sustainable, clean energy source that contributes to decarbonization efforts. In particular, the proton conducting proton ceramic fuel cells (PCFCs) play a crucial role in promoting hydrogen energy technology [1-5]. In a PCFC, the electrolyte is typically a solid oxide material that enables proton transport and can operate at temperatures lower than those of traditional oxygen ion conducting fuel cells [6]. The reduced operating temperature of PCFCs contributes to their durability, scalability, and efficiency [7,8]. BaZr 0.8 Y 0.2 O 3-x (BZY) is a promising proton conducting solid oxide electrolyte [9,10]. The emphasis on proton conductivity entails the importance of understanding proton content in the system. However, previous studies have largely relied on bulk techniques such as electrochemical impedance spectroscopy [7,11], Karl-Fischer titration [12,13], and thermogravimetric analysis [14] to obtain proton concentration. This is due to the small atomic size and light mass of hydrogen species making direct detection challenging. Bulk methods may be useful in estimating the proton content; however, it overlooks possible proton concentration gradient or segregation that may occur across or within a nanoparticle, especially when proton incorporation occurs nonuniformly through steam exposure on powder samples. In this paper, BZY is protonated at an estimated 0.15 mol of protons via steam exposure. Electron energy loss spectroscopy (EELS) with nanoscale spatial resolution is explored to identify proxy signals for proton detection using a JEOL ARM300 microscope operated at 300 kV with a Gatan GIF Continuum detector.

08 HYDROGEN↗

Using machine learning to identify extragalactic globular cluster candidates from ground-based photometric surveys of M87

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87’s well-studied GC system to implement supervised machine learning (ML) classification algorithms – specifically random forest and neural networks – to identify GCs from foreground stars and background galaxies, using ground-based photometry from the Canada–France–Hawaii Telescope (CFHT). We compare these two ML classification methods to studies of ‘human-selected’ GCs and find that the best-performing random forest model can reselect 61.2 per cent ± 8.0 per cent of GCs selected from HST data (ACSVCS) and the best-performing neural network model reselects 95.0 per cent ± 3.4 per cent. When compared to human-classified GCs and contaminants selected from CFHT data – independent of our training data – the best-performing random forest model can correctly classify 91.0 per cent ± 1.2 per cent and the best-performing neural network model can correctly classify 57.3 per cent ± 1.1 per cent. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming data set yields promising results.

79 ASTRONOMY AND ASTROPHYSICS↗

Star formation characteristics of CNN-identified post-mergers in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS)

ABSTRACT The importance of the post-merger epoch in galaxy evolution has been well documented, but post-mergers are notoriously difficult to identify. While the features induced by mergers can sometimes be distinctive, they are frequently missed by visual inspection. In addition, visual classification efforts are highly inefficient because of the inherent rarity of post-mergers (~1 per cent in the low-redshift Universe), and non-parametric statistical merger selection methods do not account for the diversity of post-mergers or the environments in which they appear. To address these issues, we deploy a convolutional neural network (CNN) that has been trained and evaluated on realistic mock observations of simulated galaxies from the IllustrisTNG simulations, to galaxy images from the Canada France Imaging Survey, which is part of the Ultraviolet Near Infrared Optical Northern Survey. We present the characteristics of the galaxies with the highest CNN-predicted post-merger certainties, as well as a visually confirmed subset of 699 post-mergers. We find that post-mergers with high CNN merger probabilities [p(x) > 0.8] have an average star formation rate that is 0.1 dex higher than a mass- and redshift-matched control sample. The SFR enhancement is even greater in the visually confirmed post-merger sample, a factor of 2 higher than the control sample.

79 ASTRONOMY AND ASTROPHYSICS↗

The Pristine survey – XX. GTC follow-up observations of extremely metal-poor stars identified from Pristine and LAMOST

Ultra-metal-poor stars (⁠[Fe/H] < –4.0⁠) are very rare, and finding them is a challenging task. Both narrow-band photometry and low-resolution spectroscopy have been useful tools for identifying candidates, and in this work, we combine both approaches. We cross-matched metallicity-sensitive photometry from the Pristine survey with the low-resolution spectroscopic Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) data base, and re-analyzed all LAMOST spectra with ⁠[Fe/H] Pristine < –2.5⁠. We find that ~1/3rd of this sample (selected without ⁠[Fe/H] Pristine quality cuts) also have spectroscopic ⁠[Fe/H] < –2.5⁠. From this sample, containing many low signal-to-noise ratio (S/N) spectra, we selected 11 stars potentially having ⁠[Fe/H] < –4.0 or [Fe/H] < –3.0 with very high carbon abundances, and we performed higher S/N medium-resolution spectroscopic follow-up with the Optical System for Imaging and low Resolution Integrated Spectroscopy (OSIRIS) on the 10.4-m Gran Telescopio Canarias (GTC). We confirm their extremely low metallicities, with a mean of [Fe/H] < –3.4⁠, and the most metal-poor star having [Fe/H] < –3.8⁠. Three of these are clearly carbon-enhanced metal-poor (CEMP) stars with +1.65 < [C/Fe] < +2.45⁠. The two most carbon-rich stars are either among the most metal-poor CEMP-s stars or the most carbon-rich CEMP-no stars known, the third is likely a CEMP-no star. We derived orbital properties for the OSIRIS sample and find that only one of our targets can be confidently associated with known substructures/accretion events, and that three out of four inner halo stars have prograde orbits. Large spectroscopic surveys may contain many hidden extremely and ultra-metal-poor stars, and adding additional information from e.g. photometry as in this work can uncover them more efficiently and confidently.

79 ASTRONOMY AND ASTROPHYSICS↗

Investigation of oscillations above the acoustic cut-off frequency for a sample of Kepler stars. II. Identifying temporal frequency shifts

ABSTRACT Stellar oscillations with frequencies greater than the acoustic cut-off frequency are not trapped within the star’s interior. Geometric interference between these high-frequency waves produces a peak-like structure in the power spectrum of the star, known as the pseudo-modes. In this study, we aim to analyse 51 stars previously identified (from a sample of 91 Kepler stars) to contain statistically significant evidence for pseudo-modes, to determine if the pseudo-mode frequencies vary in time. We obtained these frequency variations and the uncertainty by the resampled periodogram approach, where periodogram realisations were created from successive, overlapping 90-d time segments, and a cross-correlation function was generated between them. The computed shifts were compared against temporal p-mode frequency shifts and a stellar magnetic activity proxy, $S_{\mathrm{ph}}$. We found that for 16 per cent of the stars pseudo-mode frequency shifts were significantly anticorrelated with p-mode shifts, as is the case for the Sun. However, we also found pseudo-mode and p-mode shifts to be significantly correlated in-phase in 8 per cent of our sample. The remainder showed no significant correlation. We also searched for trends between the maximum variation of pseudo-mode frequency shifts and stellar parameters. Whilst we found no correlation between the maximum variation and stellar photospheric and chromospheric proxies, we found frequency variations to have a greater amplitude for cooler and slower rotating stars, which is in opposition to p-mode frequency shift variation. Temporal changes to pseudo-mode frequencies may be attributable to variations in stellar magnetic activity, and therefore can contribute to our understanding of stellar activity cycles.

Millson, L. J. (ORCID:0009000342542676)↗

Potential pathogenicity determinants identified from structural proteomics of SARS-CoV and SARS-CoV-2

Despite SARS-CoV and SARS-CoV-2 being equipped with highly similar protein arsenals, the corresponding zoonoses have spread among humans at extremely different rates. The specific characteristics of these viruses that led to such distinct outcomes remain unclear. Here, we apply proteome-wide comparative structural analysis aiming to identify the unique molecular elements in the SARS-CoV-2 proteome that may explain the differing consequences. By combining protein modeling and molecular dynamics simulations, we suggest non-conservative substitutions in functional regions of the spike glycoprotein (S), nsp1, and nsp3 that are contributing to differences in virulence. Particularly, we explain why the substitutions at the receptor-binding domain of S affect the structure-dynamics behavior in complexes with putative host receptors. Conservation of functional protein regions within the two taxa is also noteworthy. We suggest that the highly conserved main protease, nsp5, of SARS-CoV and SARS-CoV-2 is part of their mechanism of circumventing the host interferon antiviral response. Overall, most substitutions occur on the protein surfaces and may be modulating their antigenic properties and interactions with other macromolecules. Our results imply that the striking difference in the pervasiveness of SARS-CoV-2 and SARS-CoV among humans seems to significantly derive from molecular features that modulate the efficiency of viral particles in entering the host cells and blocking the host immune response.

59 BASIC BIOLOGICAL SCIENCES↗

The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the “first-infection tree,” a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.

59 BASIC BIOLOGICAL SCIENCES↗

Strategies to identify and edit improvements in synthetic genome segments episomally

Genome engineering projects often utilize bacterial artificial chromosomes (BACs) to carry multi-kilobase DNA segments at low copy number. However, all stages of whole-genome engineering have the potential to impose mutations on the synthetic genome that can reduce or eliminate the fitness of the final strain. Here, we describe improvements to a multiplex automated genome engineering (MAGE) protocol to improve recombineering frequency and multiplexability. This protocol was applied to recoding an Escherichia coli strain to replace seven codons with synonymous alternatives genome wide. Ten 44 402–47 179 bp de novo synthesized DNA segments contained in a BAC from the recoded strain were unable to complement deletion of the corresponding 33–61 wild-type genes using a single antibiotic resistance marker. Next-generation sequencing (NGS) was used to identify 1–7 non-recoding mutations in essential genes per segment, and MAGE in turn proved a useful strategy to repair these mutations on the recoded segment contained in the BAC when both the recoded and wild-type copies of the mutated genes had to exist by necessity during the repair process. Finally, two web-based tools were used to predict the impact of a subset of non-recoding missense mutations on strain fitness using protein structure and function calls.

59 BASIC BIOLOGICAL SCIENCES↗

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens

Abstract Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3–29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%–97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%–94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or “molecular tipping points” that are predictive of later-life health outcomes.

Hill, III,, Thomas↗

Modulation of polar auxin transport identifies the molecular determinants of source–sink carbon relationships and sink strength in poplar

Abstract Source-to-sink carbon (C) allocation driven by the sink strength, i.e., the ability of a sink organ to import C, plays a central role in tissue growth and biomass productivity. However, molecular drivers of sink strength have not been thoroughly characterized in trees. Auxin, as a major plant phytohormone, regulates the mobilization of photoassimilates in source tissues and elevates the translocation of carbohydrates toward sink organs, including roots. In this study, we used an ‘auxin-stimulated carbon sink’ approach to understand the molecular processes involved in the long-distance source–sink C allocation in poplar. Poplar cuttings were foliar sprayed with polar auxin transport modulators, including auxin enhancers (AE) (i.e., IBA and IAA) and auxin inhibitor (AI) (i.e., NPA), followed by a comprehensive analysis of leaf, stem and root tissues using biomass evaluation, phenotyping, C isotope labeling, metabolomics and transcriptomics approaches. Auxin modulators altered root dry weight and branching pattern, and AE increased photosynthetically fixed C allocation from leaf to root tissues. The transcriptome analysis identified highly expressed genes in root tissue under AE condition including transcripts encoding polygalacturonase and β-amylase that could increase the sink size and activity. Metabolic analyses showed a shift in overall metabolism including an altered relative abundance levels of galactinol, and an opposite trend in citrate levels in root tissue under AE and AI conditions. In conclusion, we postulate a model suggesting that the source–sink C relationships in poplar could be fueled by mobile sugar alcohols, starch metabolism-derived sugars and TCA-cycle intermediates as key molecular drivers of sink strength.

59 BASIC BIOLOGICAL SCIENCES↗

Spirochaete genome identified in red abalone sample represents a novel genus Candidatus Haliotispira gen. nov. within the order Spirochaetales

A fully assembled spirochaete genome was identified as a contaminating scaffold in our red abalone ( Haliotis rufescens ) genome assembly. In this paper, we describe the analysis of this bacterial genome. The assembled spirochaete genome is 3.25 Mb in size with 48.5 mol% G+C content. The proteomes of 38 species were compared with the spirochaete genome and it was discovered to form an independent branch within the family Spirochaetaceae on the phylogenetic tree. The comparison of 16S rRNA sequences and average nucleotide identity scores between the spirochaete genome with known species of different families in Spirochaetia indicate that it is an unknown species. Further, the percentage of conserved proteins compared to neighbouring taxa confirm that it does not belong to a known genus within Spirochaetaceae . We propose the name Candidatus Haliotispira prima gen. nov., sp. nov. based on its taxonomic placement and origin. We also tested for the presence of this species in different species of abalone and found that it is also present in white abalone ( Haliotis sorenseni ). In addition, we highlight the need for better classification of taxa within the class Spirochaetia .

Microbiology↗

Charged-hadron and identified-hadron (𝐾$^{0}_{𝑆}$, Λ, $Ξ$ − ) yield measurements in photonuclear Pb + Pb and 𝑝 + Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with ATLAS

This paper presents the measurement of charged-hadron and identified-hadron (𝐾$^{0}_{𝑆}$, Λ, $Ξ$ − ) yields in photonuclear collisions using 1.7nb −1 of $\sqrt{s_{NN}}$ = 5.02 TeV Pb + Pb data collected in 2018 with the ATLAS detector at the Large Hadron Collider. Candidate photonuclear events are selected using a combination of tracking and calorimeter information, including the zero-degree calorimeter. The yields as a function of transverse momentum and rapidity are measured in these photonuclear collisions as a function of charged-particle multiplicity. These photonuclear results are compared with 0.1 nb −1 of $\sqrt{s_{NN}}$ = 5.02 TeV p + Pb data collected in 2016 by ATLAS using similar charged-particle multiplicity selections. These photonuclear measurements shed light on potential quark-gluon plasma formation in photonuclear collisions via observables sensitive to radial flow, enhanced baryon-to-meson ratios, and strangeness enhancement. The results are also compared with the Monte Carlo generator and hydrodynamic calculations to test whether such photonuclear collisions may produce small droplets of quark-gluon plasma that flow collectively.

H & He induced nuclear reactions↗

Identifying Majorana vortex modes via nonlocal transport

The combination of two-dimensional Dirac surface states with s-wave superconductivity is expected to generate localized topological Majorana zero modes in vortex cores. Putative experimental signatures of these modes have been reported for heterostructures of proximitized topological insulators, iron-based superconductors or certain transition metal dichalcogenides. Despite these efforts, the Majorana nature of the observed excitation is still under debate. In this work, we propose to identify the presence of Majorana vortex modes using a nonlocal transport measurement protocol originally employed for one-dimensional settings. In the case of an isolated subgap state, the protocol provides a spatial map of the ratio of local charge- and probability-density which offers a clear distinction between Majorana and ordinary fermionic modes. We show that these distinctive features survive in the experimentally relevant case of hybridizing vortex core modes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Centrality and transverse-momentum dependence of higher-order flow harmonics of identified hadrons in Au+Au collisions at s N N = 200 GeV

Here we present high-precision measurements of elliptic, triangular, and quadrangular flow v 2 , v 3 , and v 4 , respectively, at midrapidity for identified hadrons π, p, K, φ, K s , Λ as a function of centrality and transverse momentum in Au+Au collisions at the center-of-mass energy √ s NN = 200 GeV. We observe similar v n trends between light and strange mesons which indicates that the heavier strange quarks flow as strongly as the lighter up and down quarks. The number-of-constituent-quark scaling for v 2 , v 3 , and v 4 is found to hold within statistical uncertainty for 0–10%, 10–40%, and 40–80% collision centrality intervals. The results are compared to several viscous hydrodynamic calculations with varying initial conditions, and could serve as an additional constraint to the development of hydrodynamic models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Probing initial baryon stopping and equation of state with rapidity-dependent directed flow of identified particles

In this work, using a (3 + 1)-dimensional hybrid framework with parametric initial conditions, we study the rapidity-dependent directed flow $v_1 (y)$ of identified particles, including pions, kaons, protons, and lambdas in heavy-ion collisions. Cases involving Au + Au collisions are considered, performed at $\sqrt {^SNN}$ ranging from 7.7 to 200 GeV. The dynamics in the beam direction is constrained using the measured pseudo-rapidity distribution of charged particles and the net proton rapidity distribution. Within this framework, the directed flow of mesons is driven by the sideward pressure gradient from the tilted source, and that of baryons mainly due to the initial asymmetric baryon distribution with respect to the beam axis driven by the transverse expansion. Our approach successfully reproduces the rapidity- and beam energy-dependence of $v_1$ for both mesons and baryons. We find that the $v_1 (y)$ of baryons has strong constraining power on the initial baryon stopping, and together with that of mesons, the directed flow probes the equation of state of the dense nuclear matter at finite chemical potentials.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Learning to identify electrons

In this report we investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared to the performance of typical features, revealing a ≈ 5% gap which indicates that these lower-level data do contain untapped classification power. To reveal the nature of this unused information, we use a recently developed technique to map the deep network into a space of physically interpretable observables. We identify two simple calorimeter observables which are not typically used for electron identification, but which mimic the decisions of the convolutional network and nearly close the performance gap.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Using Disorder to Identify Bogoliubov Fermi-Surface States

We argue that a superconducting state with a Fermi surface of Bogoliubov quasiparticles, a Bogoliubov Fermi surface (BG-FS), can be identified by the dependence of physical quantities on disorder. In particular, we show that a linear dependence of the residual density of states at weak disorder distinguishes a BG-FS state from other nodal superconducting states. We further demonstrate the stability of supercurrent against impurities and a characteristic Drude-like behavior of the optical conductivity. Our results can be directly applied to electron irradiation experiments on candidate materials of BG-FSs, including Sr 2 RuO 4 , FeSe 1-x S x , and UBe 13 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Using Direct Laboratory Measurements of Electron Temperature Anisotropy to Identify the Heating Mechanism in Electron-Only Guide Field Magnetic Reconnection

Anisotropic electron heating during electron-only magnetic reconnection with a large guide magnetic field is directly measured in a laboratory plasma through in situ measurements of electron velocity distribution functions. Electron heating preferentially parallel to the magnetic field is localized to one separatrix, and anisotropies of 1.5 are measured. The mechanism for electron energization is identified as the parallel reconnection electric field because of the anisotropic nature of the heating and spatial localization. These characteristics are reproduced in a 2D particle-in-cell simulation and are also consistent with numerous magnetosheath observations. A measured increase in the perpendicular temperature along both separatrices is not reproduced by our 2D simulations. Here, this work has implications for energy partition studies in magnetosheath and laboratory reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗