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Abstract Food webs govern interactions among organisms and drive energy fluxes within ecosystems. With an increasing appreciation for the role of symbiotic microbes in host metabolism and development, it is imperative to understand the extent to which microbes conform to, and potentially influence, canonical food web efficiencies and structures. Here, we investigate whether bacteria and their taxa and functional genes are compositionally nested within a simple model food web hierarchy, and the extent to which this is predicted by the trophic position of the host. Using shotgun and amplicon sequencing of discrete food web compartments within replicate tank bromeliads, we find that both taxonomy and function are compositionally nested and largely mirror the pyramid-shaped distribution of food webs. Further, nearly the entirety of bacterial taxa and functional genes associated with hosts are contained within host-independent environmental samples. Community composition of bacterial taxa did not significantly correlate with that of functional genes, indicating a high likelihood of functional redundancy. Whereas bacterial taxa were shaped by both location and trophic position of their host, functional genes were not spatially structured. Our work illustrates the advantages of applying food web ecology to predict patterns of overlapping microbiome composition among unrelated hosts and distinct habitats. Because bacterial symbionts are critical components of host metabolic potential, this result raises important questions about whether bacterial consortia are shaped by the same energetic constraints as hosts, and whether they play an active role in food web efficiency. 

Abstract We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere ${\mathbb{S}}^2$, giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For $40$keV electrons in a 70% He 30% CO₂gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26^∘) compared to 0.556 (64^∘) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors. 

Abstract The National Science Foundation’s Daniel K. Inouye Solar Telescope (DKIST) will provide high-resolution, multiline spectropolarimetric observations that are poised to revolutionize our understanding of the Sun. Given the massive data volume, novel inference techniques are required to unlock its full potential. Here, we provide an overview of our “SPIn4D” project, which aims to develop deep convolutional neural networks (CNNs) for estimating the physical properties of the solar photosphere from DKIST spectropolarimetric observations. We describe the magnetohydrodynamic (MHD) modeling and the Stokes profile synthesis pipeline that produce the simulated output and input data, respectively. These data will be used to train a set of CNNs that can rapidly infer the four-dimensional MHD state vectors by exploiting the spatiotemporally coherent patterns in the Stokes profile time series. Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Six cases with different mean magnetic fields have been explored; each case covers six solar-hours, totaling 109 TB in data volume. The simulation domain covers at least 25 × 25 × 8 Mm, with 16 × 16 × 12 km spatial resolution, extending from the upper convection zone up to the temperature minimum region. The outputs are stored at a 40 s cadence. We forward model the Stokes profile of two sets of Feilines at 630 and 1565 nm, which will be simultaneously observed by DKIST and can better constrain the parameter variations along the line of sight. The MHD model output and the synthetic Stokes profiles are publicly available, with 13.7 TB in the initial release. 

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. 

Advances in metagenomic sequencing have provided an unprecedented view of the microbial world, but untangling the web of microbe interdependencies and the complex relationship between microbiome and host is a major challenge in biology. New statistical methods are needed to analyze metagenomic data and infer these relationships. Focusing on amplicon sequencing data, we present methods for leveraging phylogenetic information in deep neural network models and for transfer learning from large data repositories. This approach is demonstrated in experiments using data from the Earth Microbiome Project (EMP) and a dataset of 1500 samples from Waimea Valley on the island of Oahu, Hawaii. 

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. 

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.