Assessment of Skill and Portability in Regional Marine Biogeochemical Models: Role of Multiple Planktonic Groups

Application of biogeochemical models to the study of marine ecosystems is pervasive, yet objective quantification of these models\u27 performance is rare. Here, 12 lower trophic level models of varying complexity are objectively assessed in two distinct regions (equatorial Pacific and Arabian Sea). Each model was run within an identical one-dimensional physical framework. A consistent variational adjoint implementation assimilating chlorophyll-a, nitrate, export, and primary productivity was applied and the same metrics were used to assess model skill. Experiments were performed in which data were assimilated from each site individually and from both sites simultaneously. A cross-validation experiment was also conducted whereby data were assimilated from one site and the resulting optimal parameters were used to generate a simulation for the second site. When a single pelagic regime is considered, the simplest models fit the data as well as those with multiple phytoplankton functional groups. However, those with multiple phytoplankton functional groups produced lower misfits when the models are required to simulate both regimes using identical parameter values. The cross-validation experiments revealed that as long as only a few key biogeochemical parameters were optimized, the models with greater phytoplankton complexity were generally more portable. Furthermore, models with multiple zooplankton compartments did not necessarily outperform models with single zooplankton compartments, even when zooplankton biomass data are assimilated. Finally, even when different models produced similar least squares model-data misfits, they often did so via very different element flow pathways, highlighting the need for more comprehensive data sets that uniquely constrain these pathways

Assessment of skill and portability in regional marine biogeochemical models: Role of multiple planktonic groups

Application of biogeochemical models to the study of marine ecosystems is pervasive, yet objective quantification of these models' performance is rare. Here, 12 lower trophic level models of varying complexity are objectively assessed in two distinct regions (equatorial Pacific and Arabian Sea). Each model was run within an identical one-dimensional physical framework. A consistent variational adjoint implementation assimilating chlorophyll-a, nitrate, export, and primary productivity was applied and the same metrics were used to assess model skill. Experiments were performed in which data were assimilated from each site individually and from both sites simultaneously. A cross-validation experiment was also conducted whereby data were assimilated from one site and the resulting optimal parameters were used to generate a simulation for the second site. When a single pelagic regime is considered, the simplest models fit the data as well as those with multiple phytoplankton functional groups. However, those with multiple phytoplankton functional groups produced lower misfits when the models are required to simulate both regimes using identical parameter values. The cross-validation experiments revealed that as long as only a few key biogeochemical parameters were optimized, the models with greater phytoplankton complexity were generally more portable. Furthermore, models with multiple zooplankton compartments did not necessarily outperform models with single zooplankton compartments, even when zooplankton biomass data are assimilated. Finally, even when different models produced similar least squares model-data misfits, they often did so via very different element flow pathways, highlighting the need for more comprehensive data sets that uniquely constrain these pathways

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Efficient CO2 fixation by surface Prochlorococcus in the Atlantic Ocean

Nearly half of the Earth’s surface is covered by the ocean populated by the most abundant photosynthetic organisms on the planet—Prochlorococcus cyanobacteria. However, in the oligotrophic open ocean, the majority of their cells in the top half of the photic layer have levels of photosynthetic pigmentation barely detectable by flow cytometry, suggesting low efficiency of CO2 fixation compared with other phytoplankton living in the same waters. To test the latter assumption, CO2 fixation rates of flow cytometrically sorted 14C-labelled phytoplankton cells were directly compared in surface waters of the open Atlantic Ocean (30°S to 30°N). CO2 fixation rates of Prochlorococcus are at least 1.5–2.0 times higher than CO2 fixation rates of the smallest plastidic protists and Synechococcus cyanobacteria when normalised to photosynthetic pigmentation assessed using cellular red autofluorescence. Therefore, our data indicate that in oligotrophic oceanic surface waters, pigment minimisation allows Prochlorococcus cells to harvest plentiful sunlight more effectively than other phytoplankton

Improved Methods for Reprogramming Human Dermal Fibroblasts Using Fluorescence Activated Cell Sorting

<div><p>Current methods to derive induced pluripotent stem cell (iPSC) lines from human dermal fibroblasts by viral infection rely on expensive and lengthy protocols. One major factor contributing to the time required to derive lines is the ability of researchers to identify fully reprogrammed unique candidate clones from a mixed cell population containing transformed or partially reprogrammed cells and fibroblasts at an early time point post infection. Failure to select high quality colonies early in the derivation process results in cell lines that require increased maintenance and unreliable experimental outcomes. Here, we describe an improved method for the derivation of iPSC lines using fluorescence activated cell sorting (FACS) to isolate single cells expressing the cell surface marker signature CD13^NEGSSEA4^POSTra-1-60^POS on day 7–10 after infection. This technique prospectively isolates fully reprogrammed iPSCs, and depletes both parental and “contaminating” partially reprogrammed fibroblasts, thereby substantially reducing the time and reagents required to generate iPSC lines without the use of defined small molecule cocktails. FACS derived iPSC lines express common markers of pluripotency, and possess spontaneous differentiation potential <i>in vitro</i> and <i>in vivo</i>. To demonstrate the suitability of FACS for high-throughput iPSC generation, we derived 228 individual iPSC lines using either integrating (retroviral) or non- integrating (Sendai virus) reprogramming vectors and performed extensive characterization on a subset of those lines. The iPSC lines used in this study were derived from 76 unique samples from a variety of tissue sources, including fresh or frozen fibroblasts generated from biopsies harvested from healthy or disease patients.</p> </div

Information-Based Relative Consumption Effects

Preferences exhibit relative consumption effects if a person's satisfaction with their own consumption appears to depend upon how much others are consuming. This paper examines a model of an evolutionary environment in which Nature optimally builds relative consumption effects into preferences in order to compensate for incomplete environmental information. Copyright Econometric Society 2004.