Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

Objectives People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17–April 25; T2: April 26–May 23; T3: May 24–June 25). Results The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06–0.75) during T2 and 0.14 times lower (95% CI: 0.04–0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Conclusion During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks, and/or individual site characteristics.

Correspondence between Site Amplification and Topographical, Geological Parameters: Collation of Data from Swiss and Japanese Stations, and Neural Networks-Based Prediction of Local Response

ABSTRACT We address the relation between seismic local amplification and topographical and geological indicators describing the site morphology. We focus on parameters that can be derived from layers of diffuse information (e.g., digital elevation models, geological maps) and do not require in situ surveys; we term these parameters as “indirect” proxies, as opposed to “direct” indicators (e.g., f0, VS30) derived from field measurements. We first compiled an extensive database of indirect parameters covering 142 and 637 instrumented sites in Switzerland and Japan, respectively; we collected topographical indicators at various spatial extents and focused on shared features in the geological descriptions of the two countries. We paired this proxy database with a companion dataset of site amplification factors at 10 frequencies within 0.5–20 Hz, empirically measured at the same Swiss and Japanese stations. We then assessed the robustness of the correlation between individual site-condition indicators and local response by means of statistical analyses; we also compared the proxy-site amplification relations at Swiss versus Japanese sites. Finally, we tested the prediction of site amplification by feeding ensembles of indirect parameters to a neural network (NN) structure. The main results are: (1) indirect indicators show higher correlation with site amplification in the low-frequency range (0.5–3.33 Hz); (2) topographical parameters primarily relate to local response not because of topographical amplification effects but because topographical features correspond to the properties of the subsurface, hence to stratigraphic amplification; (3) large-scale topographical indicators relate to low-frequency response, smaller-scale to higher-frequency response; (4) site amplification versus indirect proxy relations show a more marked regional variability when compared with direct indicators; and (5) the NN-based prediction of site response is the best achieved in the 1.67–5 Hz band, with both geological and topographical proxies provided as input; topographical indicators alone perform better than geological parameters.

Investigating causal factors of shallow landslides in grassland regions of Switzerland

Mountainous grassland slopes can be severely affected by soil erosion, among which shallow landslides are a crucial process, indicating instability of slopes. We determine the locations of shallow landslides across different sites to better understand regional differences and to identify their triggering causal factors. Ten sites across Switzerland located in the Alps (eight sites), in foothill regions (one site) and the Jura Mountains (one site) were selected for statistical evaluations. For the shallow-landslide inventory, we used aerial images (0.25 m) with a deep learning approach (U-Net) to map the locations of eroded sites. We used logistic regression with a group lasso variable selection method to identify important explanatory variables for predicting the mapped shallow landslides. The set of variables consists of traditional susceptibility modelling factors and climate-related factors to represent local as well as cross-regional conditions. This set of explanatory variables (predictors) are used to develop individual-site models (local evaluation) as well as an all-in-one model (cross-regional evaluation) using all shallow-landslide points simultaneously. While the local conditions of the 10 sites lead to different variable selections, consistently slope and aspect were selected as the essential explanatory variables of shallow-landslide susceptibility. Accuracy scores range between 70.2 % and 79.8 % for individual site models. The all-in-one model confirms these findings by selecting slope, aspect and roughness as the most important explanatory variables (accuracy = 72.3 %). Our findings suggest that traditional susceptibility variables describing geomorphological and geological conditions yield satisfactory results for all tested regions. However, for two sites with lower model accuracy, important processes may be under-represented with the available explanatory variables. The regression models for sites with an east–west-oriented valley axis performed slightly better than models for north–south-oriented valleys, which may be due to the influence of exposition-related processes. Additionally, model performance is higher for alpine sites, suggesting that core explanatory variables are understood for these areas.

European GNSS troposphere monitoring for meteorological applications

Near real-time GNSS double-difference network processing is a traditional method still used within the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP) for the atmosphere water vapour content monitoring in support of Numerical Weather Prediction. The standard production relies on estimating zenith tropospheric path delays (ZTDs) for GNSS ground stations with a 1-hour time resolution and a latency of 90 minutes. The Precise Point Positioning (PPP) method in real-time mode has reached the reliability and the accuracy comparable to the near real-time solution. The effectiveness of the PPP method relies on exploiting undifferenced observations from individual receivers, thus optimal use of all tracked systems, observations and signal bands, possible in-situ processing, high temporal resolution of estimated parameters and almost without any latency. The solution may implicitly include horizontal tropospheric gradients and slant tropospheric path delays for enabling the monitoring of a local asymmetry of the troposphere around each individual site. We have been estimating ZTD and gradients in real-time continuously since 2015 with a limited number of stations. Recently, the solution has been extended to a pan-European and global production consisting of approximately 200 stations. The real-time product has been assessed cross-comparing ZTDs and horizontal gradients at 11 collocated stations and by validating real-time ZTDs with respect to the final post-processing products.

946 Standardized transcriptional profiling for optimizing cellular therapies: a multi-center PICI-NanoString collaboration

BackgroundThe field of cellular therapy remains one of the most promising areas for the development of new cancer treatments. To further these improvements, it is imperative to broadly understand cell therapy products at the molecular level and to identify factors that contribute to their efficacy. NanoString and the Parker Institute for Cancer Immunotherapy (PICI) have established a ground-breaking collaboration to characterize up to 1,000 apheresis and cellular therapy infusion products with the primary goal to dissect and study molecular pathways that correlate with optimal cellular therapies.MethodsUsing a large and diverse sample cohort collected from eight PICI network Cell Therapy Centers the team will aim to study gene expression profiles (GEP) that correlate with optimal apheresis and downstream cellular products, identifying biomarkers and signatures for clinical response or toxicity and further explore unique cancer-specific and shared characteristics that make an optimal and effective chimeric antigen receptor (CAR) T cell. As shown here, this first of its kind study will include samples that target dozens of different antigens covering both primary and metastatic hematological and solid tumors. Samples will be characterized using the standardized set of genes included in the nCounter CAR-T Characterization Panel and will measure essential components of CAR-T including: metabolic fitness, phenotype, TCR diversity, toxicity, activation, persistence, exhaustion and cell typing along with individual transgene expression.ResultsPresented here are initial questions that will be asked as part of this study. Meta-analysis will be performed as an aggregated set of data and individual site-specific analysis. Data will further be analyzed across individual cancer types, target types, outcome and manufacturing conditions as examples. We anticipate this information will prove useful across many aspects of the development, manufacturing and clinical applications for cellular therapies and further hypothesize that these findings will promote the understanding of pathways affecting safety and efficacy that may help optimize the therapy.ConclusionsThe project is anticipated to begin Fall of 2021 with work continuing in phases through 2022 with periodic data reports to be shared through scientific conferences. All data and findings will be made publicly available to the scientific community through PICI’s Cancer Data and Evidence Library analysis platform (CANDEL).

Examining the Role of Environmental Memory in the Predictability of Carbon and Water Fluxes Across Australian Ecosystems

The vegetation’s response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of direct, as well as long-term, plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e., droughts and heatwaves) and structural lags in the systems have largely been overlooked in the development of models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 13 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr−1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. Latent heat was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.95. When considered at a continental scale, both fluxes were more predictable when memory effects were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. The importance of environmental memory in predicting fluxes increased as site water availability declined (ρ = −0.72, p < 0.01 for NEE, ρ = −0.62, p < 0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a k-means clustering plus regression model to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems.

Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

Background: People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods: The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17-April 25; T2: April 26-May 23; T3: May 24-June 25). Results: The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06, 0.75) during T2 and 0.14 times lower (95% CI: 0.043, 0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Interpretation: During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks and/or individual site characteristics.

The 3D spatial constraint on 6.1 million amino acid sites in the human proteome

Quantification of the tolerance of protein-coding sites to genetic variation within human populations has become a cornerstone of the prediction of the function of genomic variants. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct 3D interactions with neighboring sites. To quantify the constraint on protein-coding genetic variation in 3D spatial neighborhoods, we introduce a new framework called COntact Set MISsense tolerance (or COSMIS) for estimating constraint. Leveraging recent advances in computational structure prediction, large-scale sequencing data from gnomAD, and a mutation-spectrum-aware statistical model, we comprehensively map the landscape of 3D spatial constraint on 6.1 amino acid sites covering >80% (16,533) of human proteins. We show that the human proteome is broadly under 3D spatial constraint and that the level of spatial constraint is strongly associated with disease relevance both at the individual site level and the protein level. We demonstrate that COSMIS performs significantly better at a range of variant interpretation tasks than other population-based constraint metrics while also providing biophysical insight into the potential functional roles of constrained sites. We make our constraint maps freely available and anticipate that the structural landscape of constrained sites identified by COSMIS will facilitate interpretation of protein-coding variation in human evolution and prioritization of sites for mechanistic or functional investigation.

A New Insight Into Seawater-Basalt Exchange Reactions Based on Combined δ18O—Δ′17O—87Sr/86Sr Values of Hydrothermal Fluids From the Axial Seamount Volcano, Pacific Ocean

The δ18O values of submarine vent fluids are controlled by seawater-basalt exchange reactions, temperature of exchange, and to a lesser extent, by phase separation. These variations are translated into the δ18O values of submarine hydrothermal fluids between ca. 0 and + 4‰, a range defined by pristine seawater and equilibrium with basalt. Triple oxygen isotope systematics of submarine fluids remains underexplored. Knowing how δ17O and δ18O change simultaneously during seawater-basalt reaction has a potential to improve i) our understanding of sub-seafloor processes and ii) the rock-based reconstructions of ancient seawater. In this paper, we introduce the first combined δ17O-δ18O-87Sr/86Sr dataset measured in fluids collected from several high-temperature smoker- and anhydrite-type vent sites at the Axial Seamount volcano in the eastern Pacific Ocean. This dataset is supplemented by measurements of major, trace element concentrations and pH indicating that the fluids have reacted extensively with basalt. The salinities of these fluids range between 30 and 110% of seawater indicating that phase separation is an important process, potentially affecting their δ18O. The 87Sr/86Sr endmember values range between 0.7033 and 0.7039. The zero-Mg endmember δ18O values span from -0.9 to + 0.8‰, accompanied by the Δ′17O0.528 values ranging from around 0 to −0.04‰. However, the trajectory at individual site varies. The endmember values of fluids from focused vents exhibit moderate isotope shifts in δ′18O up to +0.8‰, and the shifts in Δ′17O are small, about −0.01‰. The diffuse anhydrite-type vent sites produce fluids that are significantly more scattered in δ′18O—Δ′17O space and cannot be explained by simple isothermal seawater-basalt reactions. To explain the observed variations and to provide constraints on more evolved fluids, we compute triple O isotope compositions of fluids using equilibrium calculations of seawater-basalt reaction, including a non-isothermal reaction that exemplifies complex alteration of oceanic crust. Using a Monte-Carlo simulation of the dual-porosity model, we show a range of possible simultaneous triple O and Sr isotope shifts experienced by seawater upon reaction with basalt. We show the possible variability of fluid values, and the causal effects that would normally be undetected with conventional δ18O measurements.

Social Network Analysis Reveals the Subtle Impacts of Tourist Provisioning on the Social Behavior of a Generalist Marine Apex Predator

Shark dive ecotourism is a lucrative industry in many regions around the globe. In some cases, sharks are provisioned using bait, prompting increased research on how baited dives influence shark behavior and yielding mixed results. Effects on patterns of habitat use and movement seemly vary across species and locations. It is unknown, however, whether wide-ranging, marine apex predators respond to provisioning by changing their patterns of grouping or social behavior. We applied a tiered analytical approach (aggregation-gregariousness-social preferences) examining the impact of provisioning on the putative social behavior of tiger sharks (Galeocerdo cuvier) at a dive tourism location in The Bahamas. Using network inference on three years of acoustic tracking data from 48 sharks, we tested for non-random social structure between non-provisioned and provisioned monitoring sites resulting in 12 distinct networks. Generally considered a solitary nomadic predator, we found evidence of sociality in tiger sharks, which varied spatiotemporally. We documented periods of both random (n = 7 networks) and non-random aggregation (n = 5 networks). Three of five non-random aggregations were at locations unimpacted by provisioning regardless of season, one occurred at an active provisioning site during the dry season and one at the same receivers during the wet season when provision activity is less prevalent. Aggregations lasted longer and occurred more frequently at provisioning sites, where gregariousness was also more variable. While differences in gregariousness among individuals was generally predictive of non-random network structure, individual site preferences, size and sex were not. Within five social preference networks, constructed using generalized affiliation indices, network density was lower at provisioning sites, indicating lower connectivity at these locations. We found no evidence of size assortment on preferences. Our data suggest that sociality may occur naturally within the Tiger Beach area, perhaps due to the unusually high density of individuals there. This study demonstrates the existence of periodic social behavior, but also considerable variation in association between tiger sharks, which we argue may help to mitigate any long-term impacts of provisioning on this population. Finally, we illustrate the utility of combining telemetry and social network approaches for assessing the impact of human disturbance on wildlife behavior.