Background Zoonotically transmitted coronaviruses are responsible for three disease outbreaks since 2002, including the current COVID-19 pandemic, caused by SARS-CoV-2. Its efficient transmission and range of disease severity raise questions regarding the contributions of virus-receptor interactions. ACE2 is a host ectopeptidase and the receptor for SARS-CoV-2. Numerous reports describe ACE2 mRNA abundance and tissue distribution; however, mRNA abundance is not always representative of protein levels. Currently, there is limited data evaluating ACE2 protein and its correlation with other SARS-CoV-2 susceptibility factors. Materials and methods We systematically examined the human upper and lower respiratory tract using single-cell RNA sequencing and immunohistochemistry to determine receptor expression and evaluated its association with risk factors for severe COVID-19. Findings Our results reveal that ACE2 protein is highest within regions of the sinonasal cavity and pulmonary alveoli, sites of presumptive viral transmission and severe disease development, respectively. In the lung parenchyma, ACE2 protein was found on the apical surface of a small subset of alveolar type II cells and colocalized with TMPRSS2, a cofactor for SARS-CoV2 entry. ACE2 protein was not increased by pulmonary risk factors for severe COVID-19. Additionally, ACE2 protein was not reduced in children, a demographic with a lower incidence of severe COVID-19. Interpretation These results offer new insights into ACE2 protein localization in the human respiratory tract and its relationship with susceptibility factors to COVID-19.

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Sampling point-to-tree distances is a simple plotless technique for estimating forest density that is readily applied in modern stands and retroactively with historical surveys. Although plotless density estimators (PDEs) have been applied in over 1000 ecological publications, the accuracy and precision of the techniques remain poorly understood and depend on the statistical estimator used, the underlying spatial pattern of the forest sampled, and the tree survey methodology. The four most commonly applied PDEs are related formulations: Cottam, Pollard, Morisita, and Shanks, a family of equations that differ in the order of mathematical operations. Since the 1950s, the Cottam IV PDE has found common use as the "point-quarter method." The Pollard PDE prevails in the statistical literature. Both Cottam and Pollard PDEs are theoretically rigorous for trees distributed according to a complete spatial randomness (CSR) spatial point process. The Morisita PDE was developed in a 1957 publication, with four-tree (Morisita IV) and two-tree (Morisita II) variants, and is the basis for higher distance rank g-tree estimators. The Shanks PDE is formally described here for the first time. We review and evaluate the performance of these four PDEs on CSR and a variety of non-CSR forests using spatial patterns simulated from known spatial point processes, 14 mapped modern stands, and historical public land surveys (PLSs). We found that the Cottam and Pollard PDEs lacked accuracy for non-CSR patterns. The Morisita PDEs are appropriate for non-CSR forests, but the Morisita IV has sensitivity to local dispersion. The Morisita II PDE has high accuracy even under non-CSR distributions yielding density estimates within 10% of the true value for a variety of non-CSR patterns, but has considerable variability at small sample sizes. In conjunction with the Morisita II, the potentially biased Cottam and Pollard PDEs can be indicators of the type of non-CSR pattern. No plotless estimator is efficacious for use with small sample sizes such as found in a single stand. Morisita II PDE is recommended as a robust choice for sampling for large and non-CSR data sets such as the PLS witness tree database.

We present a gridded 8 km-resolution data product of the estimated composition of tree taxa at the time of Euro-American settlement of the northeastern United States and the statistical methodology used to produce the product from trees recorded by land surveyors. Composition is defined as the proportion of stems larger than approximately 20 cm diameter at breast height for 22 tree taxa, generally at the genus level. The data come from settlement-era public survey records that are transcribed and then aggregated spatially, giving count data. The domain is divided into two regions, eastern (Maine to Ohio) and midwestern (Indiana to Minnesota). Public Land Survey point data in the midwestern region (ca. 0.8-km resolution) are aggregated to a regular 8 km grid, while data in the eastern region, from Town Proprietor Surveys, are aggregated at the township level in irregularly-shaped local administrative units. The product is based on a Bayesian statistical model fit to the count data that estimates composition on the 8 km grid across the entire domain. The statistical model is designed to handle data from both the regular grid and the irregularly-shaped townships and allows us to estimate composition at locations with no data and to smooth over noise caused by limited counts in locations with data. Critically, the model also allows us to quantify uncertainty in our composition estimates, making the product suitable for applications employing data assimilation. We expect this data product to be useful for understanding the state of vegetation in the northeastern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can also serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data product is being made available at the NIS data portal as version 1.0.

Genome-wide chromatin accessibility and nucleosome occupancy profiles have been widely investigated, while the longrange dynamics remain poorly studied at the single-cell level. Here, we present a new experimental approach, methyltransferase treatment followed by single-molecule long-read sequencing (MeSMLR-seq), for long-range mapping of nucleosomes and chromatin accessibility at single DNA molecules and thus achieve comprehensive-coverage characterization of the corresponding heterogeneity. MeSMLR-seq offers direct measurements of both nucleosome-occupied and nucleosome-evicted regions on a single DNA molecule, which is challenging for many existing methods. We applied MeSMLR-seq to haploid yeast, where single DNA molecules represent single cells, and thus we could investigate the combinatorics of many (up to 356) nucleosomes at long range in single cells. We illustrated the differential organization principles of nucleosomes surrounding the transcription start site for silent and actively transcribed genes, at the single-cell level and in the long-range scale. The heterogeneous patterns of chromatin status spanning multiple genes were phased. Together with single-cell RNAseq data, we quantitatively revealed how chromatin accessibility correlated with gene transcription positively in a highly heterogeneous scenario. Moreover, we quantified the openness of promoters and investigated the coupled chromatin changes of adjacent genes at single DNA molecules during transcription reprogramming. In addition, we revealed the coupled changes of chromatin accessibility for two neighboring glucose transporter genes in response to changes in glucose concentration.