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At least 253 records · Page 14

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. Finally, a synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation

60 APPLIED LIFE SCIENCES↗

Site-specific plan-view (S)TEM sample preparation from thin films using a dual-beam FIB-SEM

To fully evaluate the atomic structure, and associated properties of materials using transmission electron microscopy, examination of samples from three non-collinear orientations is needed. This is particularly challenging for thin films and nanoscale devices built on substrates due to limitations with plan-view sample preparation. In this work, a new method for preparation of high-quality, site-specific, plan-view TEM samples from thin-films grown on substrates, is presented and discussed. Here, it is based on using a dual-beam focused ion beam scanning electron microscope (FIB-SEM) system. To demonstrate the method, the samples were prepared from thin films of perovskite oxide BaSnO 3 grown on a SrTiO 3 substrate and metal oxide IrO 2 on a TiO 2 substrate, ranging from 20–80 nm in thicknesses using molecular beam epitaxy. While the method is optimized for the thin films, it can be extended to other site-specific plan-view samples and devices build on wafers. Aberration-corrected STEM was used to evaluate the quality of the samples and their applicability for atomic-resolution imaging and analysis.

BaSnO3↗

Design and feasibility of a novel program of cervical screening in Nigeria: self-sampled HPV testing paired with visual triage

Background: Accelerated global control of cervical cancer would require primary prevention with human papillomavirus (HPV) vaccination in addition to novel screening program strategies that are simple, inexpensive, and effective. We present the feasibility and outcome of a community-based HPV self-sampled screening program. Methods: In Ile Ife, Nigeria, 9406 women aged 30–49 years collected vaginal self-samples, which were tested for HPV in the local study laboratory using Hybrid Capture-2 (HC2) (Qiagen). HPV-positive women were referred to the colposcopy clinic. Gynecologist colposcopic impression dictated immediate management; biopsies were taken when definite acetowhitening was present to produce a histopathologic reference standard of precancer (and to determine final clinical management). Retrospective linkage to the medical records identified 442 of 9406 women living with HIV (WLWH). Results: With self-sampling, it was possible to screen more than 100 women per day per clinic. Following an audio-visual presentation and in-person instructions, overall acceptability of self-sampling was very high (81.2% women preferring self-sampling over clinician collection). HPV positivity was found in 17.3% of women. Intensive follow-up contributed to 85.9% attendance at the colposcopy clinic. Of those referred, 8.2% were initially treated with thermal ablation and 5.6% with large loop excision of transformation zone (LLETZ). Full visibility of the squamocolumnar junction, necessary for optimal visual triage and ablation, declined from 68.5% at age 30 to 35.4% at age 49. CIN2+ and CIN3+ (CIN- Cervical intraepithelial neoplasia), including five cancers, were identified by histology in 5.9 and 3.2% of the HPV-positive women, respectively (0.9 and 0.5% of the total screening population), leading to additional treatment as indicated. The prevalences of HPV infection and CIN2+ were substantially higher (40.5 and 2.5%, respectively) among WLWH. Colposcopic impression led to over- and under-treatment compared to the histopathology reference standard. Conclusion: A cervical cancer screening program using self-sampled HPV testing, with colposcopic immediate management of women positive for HPV, proved feasible in Nigeria. Based on the collected specimens and images, we are now evaluating the use of a combination of partial HPV typing and automated visual evaluation (AVE) of cervical images to improve the accuracy of the screening program.

59 BASIC BIOLOGICAL SCIENCES↗

Robust Importance Sampling for Bayesian Model Calibration with Spatio-Temporal Data

This paper addresses two challenges in Bayesian calibration: 1) computational speed of existing sampling algorithms, and 2) calibration with spatio-temporal responses. The commonly used Markov Chain Monte Carlo (MCMC) approaches require many sequential model evaluations making the computational expense prohibitive. This paper proposes an efficient sampling algorithm: iterative importance sampling with genetic algorithm (IISGA). While iterative importance sampling enables computational efficiency, the genetic algorithm enables robustness by preventing sample degeneration and avoids getting stuck in multimodal search spaces. An inflated likelihood further enables robustness in high-dimensional parameter spaces by enlarging the target distribution. Spatio-temporal data complicate both surrogate modeling, which is necessary for expensive computational models, and the likelihood estimation. In this work, singular value decomposition is investigated for reducing the high-dimensional field data to a lower-dimensional space prior to Bayesian calibration. Then the likelihood is formulated and Bayesian inference is performed in the lower-dimension, latent space. An illustrative example is provided to demonstrate IISGA relative to existing sampling methods, and then IISGA is employed to calibrate a thermal battery model with 26 uncertain calibration parameters and spatio-temporal response data.

97 MATHEMATICS AND COMPUTING↗

Monte Carlo Simulation of Electron Interactions in an MeV-STEM for Thick Frozen Biological Sample Imaging

A variety of volume electron microscopy techniques have been developed to visualize thick biological samples. However, the resolution is limited by the sliced section thickness (>30–60 nm). To preserve biological samples in a hydrated state, cryo-focused ion beam scanning electron microscopy has been developed, providing nm resolutions. However, this method is time-consuming, requiring 15–20 h to image a 10 μm thick sample with an 8 nm slice thickness. There is a pressing need for a method that allows the rapid and efficient study of thick biological samples while maintaining nanoscale resolution. The remarkable ability of mega-electron-volt (MeV) electrons to penetrate thick biological samples, even exceeding 10 μm in thickness, while maintaining nanoscale resolution, positions MeV-STEM as a suitable microscopy tool for such applications. Our research delves into understanding the interactions between MeV electrons and frozen biological specimens through Monte Carlo simulations. Single elastic scattering, plural elastic scattering, single inelastic scattering, and plural inelastic scattering events have been simulated. The electron trajectories, the beam profile, and the intensity change of electrons in each category have been investigated. Additionally, the effects of the detector collection angle and the focal position of the electron beam were investigated. As electrons penetrated deeper into the specimen, single and plural elastic scattered electrons diminished, and plural inelastic scattered electrons became dominant, and the beam profile became wider. Even after 10 μm of the specimen, 42% of the MeV electrons were collected within 10 mrad. This confirms that MeV-STEM can be employed to study thick biological samples.

electron specimen interaction↗

The distinct conditions of atmospheric and underground nuclear tests revealed by Zn isotopic compositions of nuclear debris samples

We analyzed the Zn isotopic compositions of nuclear debris samples from atmospheric and underground nuclear tests. Samples from the site of atmospheric tests exhibit a range of Zn isotopic fractionation (δ 66 Zn JMC-Lyon = 0.23–0.86 ‰) while samples from underground tests exhibit Zn isotopic compositions with minimal variation among samples and from terrestrial igneous rock standards (δ 66 ZnJMC-Lyon = 0.28–0.39 ‰). In conclusion, the larger range of δ 66 Zn JMC-Lyon observed in atmospheric test samples relative to underground test samples is likely the result of the open system characteristics of atmospheric nuclear tests and the closed system characteristics of underground nuclear tests.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling

When sampling for Bayesian inference, one popular approach in the computational field is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS), which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice when relying on computationally expensive forward models. We propose Latent Hamiltonian neural networks (L-HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, L-HNNs do not require numerical gradients of the target density during sampling, and hence numerous evaluations of the forward computational model. Moreover, L-HNNs satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose the integration of L-HNNs in an online error monitoring scheme, in which numerical gradients of the target density are used for a few samples whenever the L-HNNs prediction errors are large. This online error monitor scheme prevents sample degeneracy in regions of low probability density and ensures robust uncertainty quantification. We demonstrate L-HNNs in NUTS with online error monitoring on several analytical examples involving complex, heavy-tailed, and high-local-curvature probability densities. We then demonstrate the applicability of L-HNNs in NUTS to two computational case studies, namely the Allen-Cahn stochastic partial differential equation and an elliptic partial differential equation with 25 and 50 inference parameters, respectively. Overall, the L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. In conclusion, compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1–2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient (which is a measure of both the sampling quality and the computational expense) by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗

An organogel library for solution NMR analysis of nanoparticle suspensions in non-aqueous samples

Surface contrast solution NMR methods (scNMR) are emerging as powerful tools to investigate the adsorption of small molecule ligands to the surface of nanoparticles (NP), returning fundamental insight into the kinetics and thermodynamics of sorption, as well as structural information on the adsorbed species. A prerequisite for the acquisition of high quality solution NMR data is the preparation of homogeneous and stable samples that return consistent NMR spectra and allow extensive signal averaging. Unfortunately, this condition does not apply to NMR samples containing NPs that often show a tendency to sediment and accumulate at the bottom of the NMR tube over the course of the experiment. We have recently shown that preparing NMR samples in an agarose gel matrix inhibits sedimentation and allows the characterization of small molecule-NP interactions by scNMR. Unfortunately, as the agarose gel only forms in aqueous solution, this sample preparation method cannot be used to stabilize NP suspensions in a non-aqueous environment. In this work, we introduce a library of 48 organogels, based on low molecular-mass organic gelators (LMOGs), to prepare NMR samples of small molecule/NP systems in a wide range of organic solvents. In addition, we present a simple method that takes advantage of 1H transverse relaxation (1H-R2) measurements to screen the library and identify the best gelator to characterize the small molecule-NP interaction of interest in the solvent of choice. We expect the results of this study will enable the preparation of homogeneous and stable samples of NPs in non-aqueous environments, therefore dramatically increasing the applicability of scNMR to the characterization of heterogeneous interactions and to the investigation of the role played by solvent molecules in regulating the kinetics and thermodynamics of sorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Trace Actinide Signatures of a Bulk Neptunium Sample

In nuclear forensic analyses, measurements of actinide elements in a sample can assist with identifying interdicted or unknown materials. While these radiochemical signatures have been extensively investigated in uranium materials, less is known about bulk neptunium samples. This paper describes the measurement of trace actinide concentrations and isotopic profiles in a 237 Np oxide sample. Uranium, plutonium, americium, and curium concentrations and isotopic profiles in the sample were determined and deemed potentially useful for distinguishing different sources of 237 Np. Furthermore, several different potential radiochronometry systems were also investigated; discordant results indicate that the Np sample was never completely purified of other actinide elements, or that subsequent contamination of the sample occurred. Few prior studies of neptunium materials have been reported, and these data suggest that trace actinide constituents could provide unique signatures to identify material out of regulatory control.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Chemistry↗

Measuring Signatures of Fuel Irradiation in Large Particle Samples

Understanding the operating history of a nuclear facility is one of the key goals in nuclear safeguards, enabling the verification of declared activities. This is accomplished via a number of mechanisms, including the analysis of samples collected at the facility. The best metrics for understanding and verifying the history of nuclear fuel are (1) actinide isotopic content and (2) actinide inter-element ratios. While techniques have long existed for measuring these values with high accuracy and precision in bulk samples, as samples become smaller – such as may be collected in environmental collection – new methods are required both for clean processing of pure samples, and for analysis of these small samples. This study describes a new extraction chromatography technique for recovering U, Pu, and Am from single small aliquots, and advanced mass spectrometry methods for high-precision and high-accuracy analysis of trace and ultra-trace levels of actinides. These techniques are applied to particle samples cut from fuel from a historic nuclear reactor. Ages calculated from Pu–Am chronometry of particles are in excellent agreement with the known operating history of the reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Elemental analysis of air-sensitive frozen molten salt samples using an inert transfer chamber for LIBS/LA-ICP-TOF-MS analysis

A novel inert sample transfer system was developed and employed to enable, for the first time, the analysis of air-sensitive salt samples in a two-volume ablation cell using simultaneous laser-induced breakdown spectroscopy (LIBS) and laser ablation (LA)-inductively coupled plasma (ICP)-time-of-flight (TOF)-mass spectrometry (MS) analysis. Molten salts are of growing interest as a medium for advanced nuclear reactors and nuclear fuel reprocessing technologies continue to be developed around their use. However, compositional analysis of molten salt samples can be challenging because of their air-sensitive nature and varying solubilities leading to inaccurate measurements when digested. LA-based analysis provides an alternate method to digestion and can provide rapid elemental information with little sample preparation. In this study, LIBS and LA-ICP-TOF-MS were used to analyze the Ce content in frozen salt samples taken from a series of electrochemical experiments. Calibrations were built for each technique, and the resulting limits of detection for Ce were estimated to be 107 and 58 µg g −1 for LIBS and LA-ICP-TOF-MS, respectively. Test samples from the electrochemical experiments were analyzed using these calibrations. The results matched bulk digestion-based ICP-optical emission spectroscopy values, and daily trends in Ce concentration changes were identified. Additionally, the LIBS and LA-ICP-TOF-MS analysis was demonstrated for identifying microgram per gram levels of components and detecting trace contaminants. The impurities detected by LIBS included Al, Mg, Ca, and Na. The impurities detected by LA-ICP-TOF-MS included W, Ag, Al, Fe, Ni, Mo, Nd, Sm, Th, and U.

Andrews, Hunter B. [Oak Ridge National Laboratory ↗

Revisiting the role of bars in AGN fuelling with propensity score sample matching

The high luminosity displayed by an active galactic nucleus (AGN) requires that gas be transported to the centre of the galaxy by some mechanism. Bar-driven processes are often pointed out in this context and a number of studies have addressed the bar–AGN connection, but with conflicting results. Some of the inconsistencies can be explained by the different spatial- and timescales involved in bar-driven gas inflows, accretion by the central black hole, and AGN emission. However, the discrepant results could also be due to sample biases, because both the AGN activity determination and the bar detection are influenced by the method employed. We revisit the bar–AGN connection in a sample of galaxies from SDSS, looking for evidence of the influence of bars on AGN activity. We determine AGN activity by emission line diagnostics and the properties of the bar were previously estimated with BUDDA, which performs 2D bulge–bar–disk decomposition. Before comparing active and inactive galaxies, we made a careful selection of the sample to minimise selection biases. We created control samples by matching them with the AGN sample using propensity score matching. This technique offers an analytical approach for creating control samples given some object parameters. We find that AGN are preferentially found in barred galaxies and that the accretion rate is higher in barred galaxies, but only when different M–σ relations are used to estimate the black hole mass M• in barred and unbarred galaxies (from the central velocity dispersion σ). On the other hand, we find no correlation between activity level and bar strength. Altogether, our results strengthen theoretical predictions that the bar is an important mechanism in disc galaxies, creating a gas reservoir to feed AGN, but they also indicate that other mechanisms can play a major role, particularly at scales ≲100 pc.

79 ASTRONOMY AND ASTROPHYSICS↗

Thermal deflection in neutron scattering sample environments at Oak Ridge National Laboratory

The neutron sources at Oak Ridge National Laboratory use a wide suite of sample environment equipment to deliver extreme conditions for a number of experiments. Much of this instrumentation focuses on extremes of temperature, such as cryostats, closed-cycle refrigerators in both low and high temperature configurations, and radiant heating furnaces. When the temperature is controlled across a large range, thermal deflection effects can notably move the sample and affect its alignment in the beam. Here, we combine these sample environments with neutron imaging and machine vision to determine the motion of a representative sample with respect to the neutron beam. We find vertical sample displacement on the order of 1–2 mm and horizontal displacement that varies from near-negligible to 1.2 mm. While these deflections are not relevant for some of the beamlines at the first target station at the spallation neutron source and the high flux isotope reactor, they will become critical for upcoming instrumentation at the second target station, as well as any instruments targeting sub-mm samples, as neutron sources and optics evolve to smaller and more focused beams. We discuss mitigation protocols and potential modifications to the environment to minimize the effect of misalignment due to thermal deflection.

47 OTHER INSTRUMENTATION↗

DESI luminous red galaxy samples for cross-correlations

We present two galaxy samples, selected from DESI Legacy Imaging Surveys (LS) DR9, with approximately 20,000 square degrees of coverage and spectroscopic redshift distributions designed for cross-correlations such as with CMB lensing, galaxy lensing, and the Sunyaev-Zel'dovich effect. The first sample is identical to the DESI Luminous Red Galaxy (LRG) sample, and the second sample is an extended LRG sample with 2–3 times the DESI LRG density. We present the improved photometric redshifts, tomographic binning and their spectroscopic redshift distributions and imaging systematics weights, and magnification bias coefficients. The catalogs and related data products will be made publicly available. The cosmological constraints using this sample and Planck lensing maps are presented in a companion paper. Here, we also make public the new set of general-purpose photometric redshifts trained using DESI spectroscopic redshifts, which are used in this work, for all galaxies in LS DR9.

79 ASTRONOMY AND ASTROPHYSICS↗

Skip Nav Destination The effect of environment on Type Ia supernovae in the Dark Energy Survey three-year cosmological sample

Analyses of Type Ia supernovae (SNe Ia) have found puzzling correlations between their standardized luminosities and host galaxy properties: SNe Ia in high-mass, passive hosts appear brighter than those in lower mass, star-forming hosts. We examine the host galaxies of SNe Ia in the Dark Energy Survey 3-yr spectroscopically confirmed cosmological sample, obtaining photometry in a series of ‘local’ apertures centred on the SN, and for the global host galaxy. We study the differences in these host galaxy properties, such as stellar mass and rest-frame U − R colours, and their correlations with SN Ia parameters including Hubble residuals. We find all Hubble residual steps to be >3σ in significance, both for splitting at the traditional environmental property sample median and for the step of maximum significance. For stellar mass, we find a maximal local step of 0.098 ± 0.018 mag; ∼0.03 mag greater than the largest global stellar mass step in our sample (0.070 ± 0.017 mag). When splitting at the sample median, differences between local and global U − R steps are small, both ∼0.08 mag, but are more significant than the global stellar mass step (0.057 ± 0.017 mag). We split the data into sub-samples based on SN Ia light-curve parameters: stretch (x_1) and colour (c), finding that redder objects (c > 0) have larger Hubble residual steps, for both stellar mass and U − R, for both local and global measurements, of ∼0.14 mag. Additionally, the bluer (star-forming) local environments host a more homogeneous SN Ia sample, with local U − R rms scatter as low as 0.084 ± 0.017 mag for blue (c < 0) SNe Ia in locally blue U − R environments.

79 ASTRONOMY AND ASTROPHYSICS↗

Probing gravity with the DES-CMASS sample and BOSS spectroscopy

ABSTRACT The DES-CMASS sample (DMASS) is designed to optimally combine the weak lensing measurements from the Dark Energy Survey (DES) and redshift-space distortions (RSD) probed by the CMASS galaxy sample from the Baryonic Oscillation Spectroscopic Survey. In this paper, we demonstrate the feasibility of adopting DMASS as the equivalent of CMASS for a joint analysis of DES and BOSS in the framework of modified gravity. We utilize the angular clustering of the DMASS galaxies, cosmic shear of the DES metacalibration sources, and cross-correlation of the two as data vectors. By jointly fitting the combination of the data with the RSD measurements from the CMASS sample and Planck data, we obtain the constraints on modified gravity parameters $\mu _0=-0.37^{+0.47}_{-0.45}$ and $\Sigma _0=0.078^{+0.078}_{-0.082}$. Our constraints of modified gravity with DMASS are tighter than those with the DES Year 1 redMaGiC sample with the same external data sets by 29 per cent for μ0 and 21 per cent for Σ0, and comparable to the published results of the DES Year 1 modified gravity analysis despite this work using fewer external data sets. This improvement is mainly because the galaxy bias parameter is shared and more tightly constrained by both CMASS and DMASS, effectively breaking the degeneracy between the galaxy bias and other cosmological parameters. Such an approach to optimally combine photometric and spectroscopic surveys using a photometric sample equivalent to a spectroscopic sample can be applied to combining future surveys having a limited overlap such as DESI and LSST.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Quantifying the poor purity and completeness of morphological samples selected by galaxy colour

The galaxy population is strongly bimodal in both colour and morphology, and the two measures correlate strongly, with most blue galaxies being late-types (spirals) and most early-types, typically ellipticals, being red. This observation has led to the use of colour as a convenient selection criterion to make samples that are then labelled by morphology. Such use of colour as a proxy for morphology results in necessarily impure and incomplete samples. In this paper, we make use of the morphological labels produced by Galaxy Zoo to measure how incomplete and impure such samples are, considering optical (ugriz), near-ultraviolet (NUV), and near-infrared (NIR; JHK) bands. The best single colour optical selection is found using a threshold of g - r = 0.742, but this still results in a sample where only 56 per cent of red galaxies are smooth and 56 per cent of smooth galaxies are red. Use of the NUV gives some improvement over purely optical bands, particularly for late-types, but still results in low purity/completeness for early-types. No significant improvement is found by adding NIR bands. With any two bands, including NUV, a sample of early-types with greater than two-thirds purity cannot be constructed. In conclusion, advances in quantitative galaxy morphologies have made colour–morphology proxy selections largely unnecessary going forward; where such assumptions are still required, we recommend studies carefully consider the implications of sample incompleteness/impurity.

79 ASTRONOMY AND ASTROPHYSICS↗