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There are many potential approaches to marine carbon dioxide removal (mCDR), of which ocean iron fertilization (OIF) has the longest history of study. However, OIF studies to date were not primarily designed to quantify the durability of carbon (C) storage, nor how wise OIF might be as an mCDR approach. To quantify C sequestration, we introduce a metric called the “centennial tonne,” defined as 1,000 kg of C isolated from atmospheric contact for on average at least 100 years. We present the activities needed to assess OIF from a scientific and technological perspective, and additionally, how it might be responsibly studied and potentially deployed. The five activities include: field studies in the Northeast Pacific; improved modeling for field studies, data assimilation and predictions at larger scales; improvements in monitoring, reporting and verification (MRV) for C, and also MRV for tracking ecological and environmental impacts; and developing new iron sources and their delivery, to increase efficiencies and reduce costs. The fifth activity is to understand whether public and community support exists for OIF, and what governance structures might support further research and possible deployment of OIF. This article is written by a multidisciplinary experts group called Exploring Ocean Iron Solutions (ExOIS) that is organized around a responsible code of conduct. Of the mCDR approaches, OIF has the potential to be low cost, scalable, and rapidly deployable. Reducing CO2 emissions must lead the way, but there is also an urgency to decide under what conditions and whether OIF might be deployed or not.

Abstract Background While rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and adolescents, past epidemiology studies of this malignancy used data that covered

Among 664,956 hospitalized COVID-19 patients during March 2020–July 2021 in the United States, select mental health conditions (i.e., anxiety, depression, bipolar, schizophrenia) were associated with increased risk for same-hospital readmission and longer length of stay. Anxiety was also associated with increased risk for intensive care unit admission, invasive mechanical ventilation, and death.

Objectives: The aim of this study was to evaluate the association between intellectual and developmental disabilities (IDDs) and severe COVID-19 outcomes, 30-day readmission, and/or increased length of stay (LOS) using a large electronic administrative database. Methods: Patients hospitalized with COVID-19 were identified between March 2020 and June 2021 from more than 900 hospitals in the United States. IDDs included intellectual disability, cerebral palsy, Down syndrome, autism spectrum disorder (ASD), and other intellectual disabilities. Outcomes included intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), 30-day readmission, mortality, and LOS. Results: Among 643,765 patients with COVID-19, multivariate models showed that patients with any IDD were at a significantly greater risk of at least 1 severe outcome, 30-day readmission, or longer LOS than patients without any IDD. Compared with those without any IDD, patients with Down syndrome had the greatest odds of ICU admission (odds ratio [OR] and 95% confidence interval [CI]: 1.96 [1.73-2.21]), IMV (OR: 2.37 [2.07-2.70]), and mortality (OR: 2.33 [2.00-2.73]). Patients with ASD and those with Down syndrome both had over a 40% longer mean LOS. Patients with intellectual disabilities had a 23% (12-35%) increased odds of 30-day readmission. Conclusions: Results suggest that patients hospitalized with COVID-19 with IDD have a significantly increased risk of severe outcomes, 30-day readmission, and longer LOS.

Abstract Background Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Results Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets. Conclusions We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

Abstract Background Given the public health relevance of PSA‐based screening, various professional organizations have issued recommendations on the use of the PSA test to screen for prostate cancer in different age groups. Aim Using a large commercial claims database, we aimed to determine the most recent rates of PSA testing for privately insured men age 30 to 64 in the context of screening recommendations. Methods and Results Data from employer plans were from MarketScan commercial claims database. Annual PSA testing rate was the proportion of men with ≥1 paid test(s) per 12 months of continuous enrollment. Men with diagnosis of any prostate‐related condition were excluded. Annual percent change (APC) in PSA test use was estimated using joinpoint regression analysis. In 2011 to 2017, annual testing rate encompassing 5.02 to 5.53 million men was approximately 1.4%, age 30 to 34; 3.4% to 4.1%, age 35 to 39; 11% to 13%, age 40 to 44; 18% to 21%, age 45 to 49; 31% to 33%, age 50 to 54; 35% to 37%, age 55 to 59; and 38% to 41%, age 60 to 64. APC for 2011 to 2017 was −0.5% (P = .11), age 30 to 34; −3.0% (P = .001), age 35‐39; −3.1% (P

Abstract Background Various professional organizations have issued recommendations on use of the PSA test to screen for prostate cancer in different age groups. Aims Using Medicare claims databases, we aimed to determine rates of PSA testing in the context of screening recommendations during 1999–2015 for US men age ≥65, stratified by age group and census regions, after excluding claims relating to all prostate‐related conditions. Methods and Results Medicare claims databases encompassed 9.71–11.12 million men for the years under study. PSA testing rate was the proportion of men with ≥1 test(s) per 12 months of continuous enrollment. Men diagnosed with any prostate‐related condition were excluded. Annual percent change (APC) in PSA test use was estimated using joinpoint regression analysis. In 1999–2015, annual testing rate was 10.1%–23.1%, age ≥85; 16.6%–31.0%, age 80–84; 23.8%–35.8%, age 75–79; 28.3%–36.9%, age 70–74; and 26.4%–33.6%, age 65–69. From 1999 to 2015, PSA testing rate decreased 40.7%, 29.9%, 13.9%, and 2.9%, respectively, for men age ≥85, 80–84, 75–79, and 70–74. For men age 65–69, test use increased by 0.3%. Significant APC trends were: APC1999–2002 = +8.1%, P = .029 and APC2008–2015 = −9.0%, P

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

Satellite ocean color remote sensing is the primary method to retrieve synoptic measurements of the optical properties of the ocean on large spatial and regular time scales. Through bio-optical modeling, changes in ocean color spectra can be linked to changes in marine ecosystem and biogeochemical properties. Bio-optical algorithms rely on assumptions about the covariance of marine constituents as well as the relationships among their inherent and apparent optical properties. Validation with in situ measurements of in-water constituents and their optical properties is required to extrapolate local knowledge about ocean color variations to global scales. Here, we evaluate seasonal and spatial relationships between optical constituents and their inherent and apparent optical properties throughout the annual cycle of the North Atlantic plankton bloom using bio-optical data from four cruises conducted as part of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). Our results show ocean color variability, quantified using field observations of the remote sensing reflectance spectrum at each NAAMES station, is driven by colored dissolved organic matter (CDOM) absorption in the ultraviolet wavelengths, phytoplankton absorption in the blue wavelengths, and total particulate backscattering in the green wavelengths. Results from a recently storm-mixed station at the height of the spring bloom demonstrate that significant changes in bio-optical properties can occur on daily scales. By testing the effects of variations in lighting conditions and solar geometries, we also demonstrate that, for this data set, remote sensing reflectance should be considered a quasi-inherent optical property. We find that the temporal and spatial chlorophyll concentrations and the magnitudes of inherent optical properties can be accurately assessed using previously published ocean color algorithms. However, changes in the spectral slopes of the inherent optical properties are often poorly retrieved, indicating the need for improvements in the retrieval of optical constituent composition. The characterization of such a dynamic environment provides beneficial insights for future bio-optical algorithms.

The net primary productivity (NPP) of marine phytoplankton is ∼50 Pg C year–1, and roughly 10–20% of this NPP is exported out of the surface ocean as sinking particulate organic carbon (POC). Numerous mechanisms are hypothesized to control POC export out of the surface ocean but the relative importance of the various mechanisms remains poorly quantified on global scales. Here, we use a previously published satellite-based mechanistic model of POC export to examine the effects on global POC export of size-specific physical aggregation, size-specific and temperature-dependent zooplankton fecal pellet production, and size-specific and temperature-dependent non-grazing phytoplankton mortality. We test these mechanisms in different model configurations to determine if these processes improve the ability of the model to match POC export observations, and to assess the role of each process in controlling global POC export. We find that all model configurations predict that over 60% of the global POC export is from small zooplankton fecal pellets. All model configurations predict similar total POC export, and we find only small differences in the magnitude, timing, and geographical variations of total POC export. However, the fraction of total POC export due to sinking phytoplankton aggregates, and that due to the fecal pellets of large zooplankton, vary by more than a factor of two across the different model configurations. The POC export in all models is most sensitive to parameters controlling zooplankton fecal fluxes and non-grazing phytoplankton mortality. We compared zooplankton grazing rates predicted by the models to results of experimental data, and found that some models match the experimental grazing rates better than others, although data uncertainties remain large. More field measurements of bulk ecosystem rates (i.e., phytoplankton aggregation and zooplankton grazing), as well as explicit determinations of of the proportion of fecal matter to phytoplankton aggregation, will help to better constrain mechanistic models of global POC export.

Analysis of phytoplankton chemotaxonomic markers from high performance liquid chromatography (HPLC) pigment determination is a common approach for evaluating phytoplankton community structure from ocean samples. Here, HPLC phytoplankton pigment concentrations from samples collected underway and from CTD bottle sampling on the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) are used to assess phytoplankton community composition over a range of seasons and environmental conditions. Several data-driven statistical techniques, including hierarchical clustering, Empirical Orthogonal Function, and network-based community detection analyses, are applied to examine the associations between groups of pigments and infer phytoplankton communities found in the surface ocean during the four NAAMES campaigns. From these analyses, five distinguishable phytoplankton community types emerge based on the associations of phytoplankton pigments: diatom, dinoflagellate, haptophyte, green algae, and cyanobacteria. We use this dataset, along with phytoplankton community structure metrics from flow cytometric analyses, to characterize the distributions of phytoplankton biomarker pigments over the four cruises. The physical and chemical drivers influencing the distribution and co-variability of these five dominant groups of phytoplankton are considered. Finally, the composition of the phytoplankton community across the onset, accumulation, and decline of the annual phytoplankton bloom in a changing North Atlantic Ocean is compared to historical paradigms surrounding seasonal succession.

Offshore aquaculture of giant kelp (Macrocystis pyrifera) has been proposed by the US Department of Energy for large scale biofuel production along the west coast of California. The Southern Californian Bight provides an ideal area for offshore kelp aquaculture as the upwelling and advection of cool, nutrient-rich waters supports the growth of vast native giant kelp populations. However, concentrations of nutrients vary greatly across space, can be limiting for kelp growth over seasonal to interannual time scales, and inputs of nutrients to surface waters may be subject to local circulation processes. Therefore, it is important to understand both the spatiotemporal variability of seawater nitrate concentrations and the appropriate scale of observation in order for offshore kelp aquaculture to be successful. Here, we use a combination of satellite sea surface temperature imagery, in situ measurements, and modeling to determine seawater nitrate fields across multiple spatial and temporal scales. We then combine this information with known giant kelp physiological traits to develop a kelp stress index (KSI) for the optimal siting of offshore kelp aquaculture over seasonal to decadal scales. Temperature to nitrate relationships were determined from in situ measurements using generalized additive models and validated with buoy data. Summer and winter relationships were significantly different, and satellite-derived products compared well to buoy validations. Surface nitrate patterns, as derived from satellite temperature products, reveal the spatial variability in nitrate concentrations, and indicate areas that that may cause nutrient stress seasonally and during the negative phase of the North Pacific Gyre Oscillation. As the spatial scale of the surface nitrate product decreased, the negative bias increased and fine scale spatial variability was lost. Similarly, the averaging of daily nitrate concentration determinations over longer time scales increased the negative bias. We found that daily, 1 km spatial resolution nitrate products were most sufficient for identifying localized upwelling and areas of consistently high surface nitrate concentrations, and that areas in the northern and western-most portions of the Southern California Bight are the most suitable for sustained offshore kelp aquaculture.

The North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) is an interdisciplinary investigation to improve understanding of Earth's ocean ecosystem-aerosol-cloud system. Specific overarching science objectives for NAAMES are to (1) characterize plankton ecosystem properties during primary phases of the annual cycle and their dependence on environmental forcings, (2) determine how these phases interact to recreate each year the conditions for an annual plankton bloom, and (3) resolve how remote marine aerosols and boundary layer clouds are influenced by plankton ecosystems. Four NAAMES field campaigns were conducted in the western subarctic Atlantic between November 2015 and April 2018, with each campaign targeting specific seasonal events in the annual plankton cycle. A broad diversity of measurements were collected during each campaign, including ship, aircraft, autonomous float and drifter, and satellite observations. Here, we present an overview of NAAMES science motives, experimental design, and measurements. We then briefly describe conditions and accomplishments during each of the four field campaigns and provide information on how to access NAAMES data. The intent of this manuscript is to familiarize the broad scientific community with NAAMES and to provide a common reference overview of the project for upcoming publications.

Ocean color remote sensing has profoundly influenced how oceanographers think about marine ecosystems and their variability in space and time. Satellite ocean color radiometry (OCR) provides a unique perspective for studying the processes regulating marine ecosystems and biogeochemistry at scales difficult to study with ships and moorings. Satellite OCR is especially useful when supported by other in situ and space observations. In this review, we highlight three important and unique contributions of satellite observations related to marine ecosystems and biogeochemical processes: understanding the responses of ocean ecosystems to physical processes operating at meso- to global scales, coupled physical-ecosystem-biogeochemical modeling, and marine living resources management.