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  1. Abstract

    Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R2= 0.78; MAE = 55 mm month−1; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month−1; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP;http://www.hawaii.edu/climate-data-portal).

    Significance Statement

    A new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output.

     
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  2. Abstract

    We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot-evolution patterns. We convolved these simulated light curves with real TESS light curves containing minimal intrinsic astrophysical variability to allow the network to learn TESS systematics and estimate rotation periods despite them. In addition to periods, we predict uncertainties via heteroskedastic regression to estimate the credibility of the period predictions. In the most credible half of the test data, we recover 10% accurate periods for 46% of the targets, and 20% accurate periods for 69% of the targets. Using our trained network, we successfully recover periods of real stars with literature rotation measurements, even past the 13.7 day limit generally encountered by TESS rotation searches using conventional period-finding techniques. Our method also demonstrates resistance to half-period aliases. We present the neural network and simulated training data, and introduce the softwarebutterpyused to synthesize the light curves using realistic starspot evolution.

     
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  3. Abstract

    We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.

     
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  4. Abstract

    Starting from the planar molecule 1,3,5-trihydroxybenzene, Duet al.reported synthesizing one of a couple of possible 2D materials: graphenylene or 3-carbophene.[1] 3-carbophene is a member of a novel class of two-dimensional covalent organic framework, [N]-carbophenes (carbophenes). Using a high throughput method, we computed the formation energies and conduction properties of 3-, 4-, 5-, and 6-carbophenes with hydroxyl (OH), carbonyl (CO), nitro (NO2), amine (NH2), carboxyl (COOH) functional groups replacing hydrogen terminating agents. Five hundred and nine structures with randomly picked motifs, with functionalizations from a single functional group per cell to fully functionalized were studied. Our results demonstrate a negatively sloped linear relationship between the degree of functionalization and formation energy when the type of functional group and type of carbophene are held constant. The decrease in formation energy with functionalization makes Du’s synthesis of functionalized 3-carbophene more creditable. The type of carbophene, type of functional group, and the degree of functionalization all play a role in the band structure of the materials. For example, CO functional groups may lead to a mid- gap state pinned to the Fermi level, whereas the other functional groups studied keep the semiconducting nature of pristine carbophene. Thus, because carbonyl functional groups are often present in defected carbon systems, care should be taken to limit the amount of oxygen in carbophene devices where the band gap is important. Thus, this work strengthens the hypothesis of Junkermeier et al.’s hypothesis thatDu et al.synthesized 3-carbophene and not graphenylene.[2]

     
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  5. Abstract

    The plant genus Bidens (Asteraceae or Compositae; Coreopsidae) is a species-rich and circumglobally distributed taxon. The 19 hexaploid species endemic to the Hawaiian Islands are considered an iconic example of adaptive radiation, of which many are imperiled and of high conservation concern. Until now, no genomic resources were available for this genus, which may serve as a model system for understanding the evolutionary genomics of explosive plant diversification. Here, we present a high-quality reference genome for the Hawaiʻi Island endemic species B. hawaiensis A. Gray reconstructed from long-read, high-fidelity sequences generated on a Pacific Biosciences Sequel II System. The haplotype-aware, draft genome assembly consisted of ~6.67 Giga bases (Gb), close to the holoploid genome size estimate of 7.56 Gb (±0.44 SD) determined by flow cytometry. After removal of alternate haplotigs and contaminant filtering, the consensus haploid reference genome was comprised of 15 904 contigs containing ~3.48 Gb, with a contig N50 value of 422 594. The high interspersed repeat content of the genome, approximately 74%, along with hexaploid status, contributed to assembly fragmentation. Both the haplotype-aware and consensus haploid assemblies recovered >96% of Benchmarking Universal Single-Copy Orthologs. Yet, the removal of alternate haplotigs did not substantially reduce the proportion of duplicated benchmarking genes (~79% vs. ~68%). This reference genome will support future work on the speciation process during adaptive radiation, including resolving evolutionary relationships, determining the genomic basis of trait evolution, and supporting ongoing conservation efforts.

     
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  6. Abstract SARS-CoV-2 worldwide spread and evolution has resulted in variants containing mutations resulting in immune evasive epitopes that decrease vaccine efficacy. We acquired SARS-CoV-2 positive clinical samples and compared the worldwide emerged spike mutations from Variants of Concern/Interest, and developed an algorithm for monitoring the evolution of SARS-CoV-2 in the context of vaccines and monoclonal antibodies. The algorithm partitions logarithmic-transformed prevalence data monthly and Pearson’s correlation determines exponential emergence of amino acid substitutions (AAS) and lineages. The SARS-CoV-2 genome evaluation indicated 49 mutations, with 44 resulting in AAS. Nine of the ten most worldwide prevalent (>70%) spike protein changes have Pearson’s coefficient r  > 0.9. The tenth, D614G, has a prevalence >99% and r -value of 0.67. The resulting algorithm is based on the patterns these ten substitutions elucidated. The strong positive correlation of the emerged spike protein changes and algorithmic predictive value can be harnessed in designing vaccines with relevant immunogenic epitopes. Monitoring, next-generation vaccine design, and mAb clinical efficacy must keep up with SARS-CoV-2 evolution, as the virus is predicted to remain endemic. 
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  7. Abstract Background Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors. Methods Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs. Results The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79. Conclusion This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort. 
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  8. Silicon monoxide (SiO) is classified as a key precursor and fundamental molecular building block to interstellar silicate nanoparticles, which play an essential role in the synthesis of molecular building blocks connected to the Origins of Life. In the cold interstellar medium, silicon monoxide is of critical importance in initiating a series of elementary chemical reactions leading to larger silicon oxides and eventually to silicates. To date, the fundamental formation mechanisms and chemical dynamics leading to gas phase silicon monoxide have remained largely elusive. Here, through a concerted effort between crossed molecular beam experiments and electronic structure calculations, it is revealed that instead of forming highly-stable silicon dioxide (SiO 2 ), silicon monoxide can be formed via a barrierless, exoergic, single-collision event between ground state molecular oxygen and atomic silicon involving non-adiabatic reaction dynamics through various intersystem crossings. Our research affords persuasive evidence for a likely source of highly rovibrationally excited silicon monoxide in cold molecular clouds thus initiating the complex chain of exoergic reactions leading ultimately to a population of silicates at low temperatures in our Galaxy. 
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  9. Microbes are found in nearly every habitat and organism on the planet, where they are critical to host health, fitness, and metabolism. In most organisms, few microbes are inherited at birth; instead, acquiring microbiomes generally involves complicated interactions between the environment, hosts, and symbionts. Despite the criticality of microbiome acquisition, we know little about where hosts’ microbes reside when not in or on hosts of interest. Because microbes span a continuum ranging from generalists associating with multiple hosts and habitats to specialists with narrower host ranges, identifying potential sources of microbial diversity that can contribute to the microbiomes of unrelated hosts is a gap in our understanding of microbiome assembly. Microbial dispersal attenuates with distance, so identifying sources and sinks requires data from microbiomes that are contemporary and near enough for potential microbial transmission. Here, we characterize microbiomes across adjacent terrestrial and aquatic hosts and habitats throughout an entire watershed, showing that the most species-poor microbiomes are partial subsets of the most species-rich and that microbiomes of plants and animals are nested within those of their environments. Furthermore, we show that the host and habitat range of a microbe within a single ecosystem predicts its global distribution, a relationship with implications for global microbial assembly processes. Thus, the tendency for microbes to occupy multiple habitats and unrelated hosts enables persistent microbiomes, even when host populations are disjunct. Our whole-watershed census demonstrates how a nested distribution of microbes, following the trophic hierarchies of hosts, can shape microbial acquisition. 
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  10. With an increasing prevalence of electronic cigarette (e-cigarette) use, especially among youth, there is an urgent need to better understand the biological risks and pathophysiology of health conditions related to e-cigarettes. A majority of e-cigarette aerosols are in the submicron size and would deposit in the alveolar region of the lung, where they must first interact with the endogenous pulmonary surfactant. To date, little is known whether e-cigarette aerosols have an adverse impact on the pulmonary surfactant. We have systematically studied the effect of individual e-cigarette ingredients on an animal-derived clinical surfactant preparation, bovine lipid extract surfactant, using a combination of biophysical and analytical techniques, including in vitro biophysical simulations using constrained drop surfactometry, molecular imaging with atomic force microscopy, chemical assays using carbon nuclear magnetic resonance and circular dichroism, and in silico molecular dynamics simulations. All data collectively suggest that flavorings used in e-cigarettes, especially menthol, play a predominant role in inhibiting the biophysical function of the surfactant. The mechanism of biophysical inhibition appears to involve menthol interactions with both phospholipids and hydrophobic proteins of the natural surfactant. These results provide novel insights into the understanding of the health impact of e-cigarettes and may contribute to better regulation of e-cigarette products. 
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