Scale-Up of the Carbon Dioxide Removal by Ionic Liquid Sorbent (CDRILS) System

The Carbon Dioxide Removal by Ionic Liquid Sorbent (CDRILS) system is designed for efficient, safe and reliable carbon dioxide (CO2) removal from cabin air on long-duration missions to the Moon, deep space, and Mars. CDRILS integrates an ionic liquid sorbent with hollow fiber membrane contactors for rapid CO2 removal and recovery. The liquid-based system provides continuous CO2 delivery, which avoids complicated valve networks to switch between absorbing and desorbing beds and enables simpler integration to the Sabatier without the need for the CO2 Management System (CMS). Ionic liquids are particularly desirable as liquid absorbents for space applications since they are non-volatile, non-odorous, and have high oxidative stability. The hollow fiber membrane contactors offer both high contact area and rigorous containment between the gas and liquid phases in a microgravity environment. Scale-up of the CDRILS technology has presented a series of fascinating challenges, since the interaction between hollow fiber properties, ionic liquid properties and performance is complex. Properties measured with lab-scale hollow fiber contactors are used to estimate the performance of contactors that are similar in scale to flight-scale demonstrations. To accomplish this, component and system models have been built to relate the key scrubber and stripper design and operating variables with performance, and experiments directed to validate the models have been performed. System size, weight and power are determined by component selection, arrangement, and operating conditions. Reliability will be extremely important for any long-range mission and depends on the stability of the ionic liquids and hollow fiber contactors. We report on our continuing long term stability experiments for the ionic liquid and contactor materials and our investigation of the physical properties of additional ionic liquids

Cycling of lithogenic marine particles in the US GEOTRACES North Atlantic transect

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 116 (2015): 283-302, doi:10.1016/j.dsr2.2014.11.019.In this paper, we present, describe, and model the first size-fractionated (0.8–51 µm; >51 µm) water-column particulate trace metal results from the US GEOTRACES North Atlantic Zonal Transect in situ pumping survey, with a focus on the lithogenic tracer elements Al, Fe and Ti. This examination of basin-wide, full-depth distributions of particulate elements elucidates many inputs and processes—some for bulk lithogenic material, others element-specific—which are presented via concentration distributions, elemental ratios, size-fractionation dynamics, and steady-state inventories. Key lithogenic inputs from African dust, North American boundary interactions, the Mediterranean outflow, hydrothermal systems, and benthic nepheloid layers are described. Using the refractory lithogenic tracer Ti, we develop a 1-D model for lithogenic particle distributions and test the sensitivities of size-fractionated open-ocean particulate Ti profiles to biotically driven aggregation, disaggregation rates, vertical sinking speeds, and dust input rates. We discuss applications of this lithogenic model to particle cycling in general, and to POC cycling specifically.International and US GEOTRACES Offices (OCE-0850963 and OCE-1129603), and fellowship assistance from the Williams College Tyng Fellowship and MIT/WHOI Academic Programs Office to DCO

Integrated Genomic and Social Network Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in the Healthcare Setting

BackgroundInfection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.MethodsWe performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.ResultsAmong 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).ConclusionsIP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission

Safer at school early alert: an observational study of wastewater and surface monitoring to detect COVID-19 in elementary schools

BackgroundSchools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown.MethodsThe objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 to March 2021.FindingsIn 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88%-98%); 67% were associated with a positive wastewater sample (95% CI: 57%-77%), and 40% were associated with a positive surface sample (95% CI: 29%-52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples.InterpretationPassive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy.FundingCounty of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control

Wastewater sequencing uncovers early, cryptic SARS-CoV-2 variant transmission

Summary As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

Summary: The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of SARS-CoV-2 lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of ‘local’ when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission