Micro-phytoplankton photosynthesis, primary production and potential export production in the Atlantic Ocean

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordMicro-phytoplankton is the >20 μm component of the phytoplankton community and plays a major role in the global ocean carbon pump, through the sequestering of anthropogenic CO2 and export of organic carbon to the deep ocean. To evaluate the global impact of the marine carbon cycle, quantification of micro-phytoplankton primary production is paramount. In this paper we use both in situ data and a satellite model to estimate the contribution of micro-phytoplankton to total primary production (PP) in the Atlantic Ocean. From 1995 to 2013, 940 measurements of primary production were made at 258 sites on 23 Atlantic Meridional Transect Cruises from the United Kingdom to the South African or Patagonian Shelf. Micro-phytoplankton primary production was highest in the South Subtropical Convergence (SSTC ∼ 409 ± 720 mg C m−2 d−1), where it contributed between 38 % of the total PP, and was lowest in the North Atlantic Gyre province (NATL ∼ 37 ± 27 mg C m−2 d−1), where it represented 18 % of the total PP. Size-fractionated photosynthesis-irradiance (PE) parameters measured on AMT22 and 23 showed that micro-phytoplankton had the highest maximum photosynthetic rate (PmB) (∼5 mg C (mg Chl a)−1 h−1) followed by nano- (∼4 mg C (mg Chl a)−1 h−1) and pico- (∼2 mg C (mg Chl a)−1 h−1). The highest PmB was recorded in the NATL and lowest in the North Atlantic Drift Region (NADR) and South Atlantic Gyre (SATL). The PE parameters were used to parameterise a remote sensing model of size-fractionated PP, which explained 84 % of the micro-phytoplankton in situ PP variability with a regression slope close to 1. The model was applied to the SeaWiFS time series from 1998–2010, which illustrated that micro-phytoplankton PP remained constant in the NADR, NATL, Canary Current Coastal upwelling (CNRY), Eastern Tropical Atlantic (ETRA), Western Tropical Atlantic (WTRA) and SATL, but showed a gradual increase in the Benguela Upwelling zone (BENG) and South Subtropical Convergence (SSTC). The mean annual carbon fixation of micro-phytoplankton was highest in the CNRY (∼140 g C m−2 yr−1), and lowest in the SATL (27 g C m−2 yr−1). A Thorium-234 based export production (ThExP) algorithm was applied to estimates of total PP in each province. There was a strong coupling between micro-phytoplankton PP and ThExP in the NADR and SSTC where between 23 and 39 % of micro-phytoplankton PP contributed to ThExP. The lowest contribution by micro-phytoplankton to ThExP was in the ETRA and WTRA which were 15 and 21 % respectively. The results suggest that micro-phytoplankton PP in the SSTC is the most efficient export system and the ETRA is the least efficient in the Atlantic Ocean.UK Natural Environment Research Council National CapabilityPOGOEU FP7 project GreenSeasNCE

Scratching beneath the surface: a model to predict the vertical distribution of Prochlorococcus using remote sensing.

The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical gyres, which are considered to be the largest biomes on Earth. In this study, the spatial and temporal variability in the global distribution of Prochlorococcus was estimated in the Atlantic Ocean using an empirical model based on data from 13 Atlantic Meridional Transect cruises. Our model uses satellite-derived sea surface temperature (SST), remote-sensing reflectance at 443 and 488 nm, and the water temperature at a depth of 200 m from Argo data. The model divides the population of Prochlorococcus into two groups: ProI, which dominates under high-light conditions associated with the surface, and ProII, which favors low-light found near the deep chlorophyll maximum. ProI and ProII are then summed to provide vertical profiles of the concentration of Prochlorococcus cells. This model predicts that Prochlorococcus cells contribute 32 Mt of carbon biomass (7.4×1026 cells) to the Atlantic Ocean, concentrated mainly within the subtropical gyres (35%) and areas near the Equatorial Convergence Zone (30%). When projected globally, 3.4×1027 Prochlorococcus cells represent 171 Mt of carbon biomass, with 43% of this global biomass allocated to the upper ocean (0-45 m depth). Annual cell standing stocks were relatively stable between the years 2003 and 2014, and the contribution of the gyres varies seasonally as gyres expand and contract, tracking changes in light and temperature, with lowest cell abundances during the boreal and austral winter (1.4×1013 cells m-2), when surface cell concentrations were highest (9.8×104 cells ml-1), whereas the opposite scenario was observed in spring-summer (2×1013 cells m-2). This model provides a three-dimensional view of the abundance of Prochlorococcus cells, revealing that Prochlorococcus contributes significantly to total phytoplankton biomass in the Atlantic Ocean, and can be applied using either in-situ measurements at the sea surface (r2=0.83) or remote-sensing observables (r2=0.58)

Modelling size-fractionated primary production in the Atlantic Ocean from remote sensing

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordMarine primary production influences the transfer of carbon dioxide between the ocean and atmosphere, and the availability of energy for the pelagic food web. Both the rate and the fate of organic carbon from primary production are dependent on phytoplankton size. A key aim of the Atlantic Meridional Transect (AMT) programme has been to quantify biological carbon cycling in the Atlantic Ocean and measurements of total primary production have been routinely made on AMT cruises, as well as additional measurements of size-fractionated primary production on some cruises. Measurements of total primary production collected on the AMT have been used to evaluate remote-sensing techniques capable of producing basin-scale estimates of primary production. Though models exist to estimate size-fractionated primary production from satellite data, these have not been well validated in the Atlantic Ocean, and have been parameterised using measurements of phytoplankton pigments rather than direct measurements of phytoplankton size structure. Here, we re-tune a remote-sensing primary production model to estimate production in three size fractions of phytoplankton (10 μm) in the Atlantic Ocean, using measurements of size-fractionated chlorophyll and size-fractionated photosynthesis-irradiance experiments conducted on AMT 22 and 23 using sequential filtration-based methods. The performance of the remote-sensing technique was evaluated using: (i) independent estimates of size-fractionated primary production collected on a number of AMT cruises using 14C on-deck incubation experiments and (ii) Monte Carlo simulations. Considering uncertainty in the satellite inputs and model parameters, we estimate an average model error of between 0.27 and 0.63 for log10-transformed size-fractionated production, with lower errors for the small size class (10 μm), and errors generally higher in oligotrophic waters. Application to satellite data in 2007 suggests the contribution of cells 2 μm to total primary production is approximately equal in the Atlantic Ocean.UK National Centre for Earth ObservationNatural Environment Research Council (NERC)Plymouth Marine Laborator

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R2> 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission’s Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblage

Respiration, phytoplankton size and the metabolic balance in the Atlantic gyres

The balance between plankton photosynthesis (GPP) and community respiration (CR) in the euphotic zone (net community production, NCP) is an essential driver of the biological carbon pump. Deficient datasets and a lack of knowledge of the mechanisms regulating CR cause poor empirical models and oversimplified parameterisations that maintain NCP as one of the most important unknowns for projections of the carbon pump. One important unresolved issue is the unexpected lack of empirical relationships between CR and the biomass or size-structure of the phytoplankton, which undermines the use of remotely sensed observations to predict net community metabolism. Here we analyse the spatial variation of plankton metabolism, chlorophyll a concentration (Chla), pico- and nanophytoplankton abundance and size-fractionated primary production (14CPP) along a latitudinal (49°N–46°S) transect of 73 stations across the Atlantic Ocean (AMT-22 cruise). The use of depth-weighted rates (rates integrated to the depth of 0.1% PAR, divided by the regionally varying depth of integration) markedly improved the depiction of latitudinal patterns and the significance of relationships, over volumetric or integrated rates. Depth-weighted CR showed clear and consistent latitudinal patterns with relevance for the distribution of NCP. Depth-weighted Chla and CR exhibited a significant relationship (CRZ=1.42ChlaZ-0.21, r2 = 0.69, N=37, p<0.001) with potential for the difficult prediction of CR. A general ratio of 1.42 mmolO2 mgChla-1 d-1 and a threshold Chla for net heterotrophy of ca. 0.25 mgChla m-3 can be tentatively proposed for the Atlantic, although further analyses of spatial and seasonal variation are necessary. We observed unusually positive NCP rates in the central part of the N gyre, due to a marked decrease of CR in a patch of high Synechococcus spp. abundance and high 14CPP by large phytoplankton. However, no relationship was observed between size-fractionated 14CPP and CR or the GPP : CR ratio during the cruise, contradicting the hypothesis that food web functioning is determined by the phytoplankton size structure. Such independence, together with the persistence of distinct GPP : CR and 14CPP : NCP relationships in distinct biogeographic provinces suggest a resilience of trophic dynamics and the existence of alternative ecosystem states, whose implications for projections of the metabolic state of the ocean are discussed

On the conservation of the slow conformational dynamics within the amino acid kinase family: NAGK the paradigm

N-Acetyl-L-Glutamate Kinase (NAGK) is the structural paradigm for examining the catalytic mechanisms and dynamics of amino acid kinase family members. Given that the slow conformational dynamics of the NAGK (at the microseconds time scale or slower) may be rate-limiting, it is of importance to assess the mechanisms of the most cooperative modes of motion intrinsically accessible to this enzyme. Here, we present the results from normal mode analysis using an elastic network model representation, which shows that the conformational mechanisms for substrate binding by NAGK strongly correlate with the intrinsic dynamics of the enzyme in the unbound form. We further analyzed the potential mechanisms of allosteric signalling within NAGK using a Markov model for network communication. Comparative analysis of the dynamics of family members strongly suggests that the low-frequency modes of motion and the associated intramolecular couplings that establish signal transduction are highly conserved among family members, in support of the paradigm sequence→structure→dynamics→function © 2010 Marcos et al

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

A conceptual approach to partitioning a vertical profile of phytoplankton biomass into contributions from two communities

This is the final version. Available from American Geophysical Union / Wiley via the DOI in this record. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System. All data and code used in the paper are provided openly on a GitHub page (https://github.com/rjbrewin/Two-community-phyto-model). This includes an example Jupyter Notebook Python Script, processing this BGC-Argo float and tuning the models. Details of how to run it without having to install software are provided as Supplementary Material to this manuscript.We describe an approach to partition a vertical profile of chlorophyll-a concentration into contributions from two communities of phytoplankton: one (community 1) that resides principally in the turbulent mixed-layer of the upper ocean and is observable through satellite visible radiometry; the other (community 2) residing below the mixed-layer, in a stably stratified environment, hidden from the eyes of the satellite. The approach is tuned to a time-series of profiles from a Biogeochemical-Argo float in the northern Red Sea, selected as its location transitions from a deep mixed layer in winter (characteristic of vertically well-mixed systems) to a shallow mixed layer in the summer with a deep chlorophyll-a maximum (characteristic of vertically stratified systems). The approach is extended to reproduce profiles of particle backscattering, by deriving the chlorophyll-specific backscattering coefficients of the two communities and a background coefficient assumed to be dominated by non-algal particles in the region. Analysis of the float data reveals contrasting phenology of the two communities, with community 1 blooming in winter and 2 in summer, community 1 negatively correlated with epipelagic stratification, and 2 positively correlated. We observe a dynamic chlorophyll-specific backscattering coefficient for community 1 (stable for community 2), positively correlated with light in the mixed-layer, suggesting seasonal changes in photoacclimation and/or taxonomic composition within community 1. The approach has the potential for monitoring vertical changes in epipelagic biogeography and for combining satellite and ocean robotic data to yield a three-dimensional view of phytoplankton distribution.Medical Research Council (MRC)Simons Foundation (SF)King Abdullah University of Science and Technology (KAUST)European Space Agency (ESA)European Space Agency (ESA

Discovering Conformational Sub-States Relevant to Protein Function

Background: Internal motions enable proteins to explore a range of conformations, even in the vicinity of native state. The role of conformational fluctuations in the designated function of a protein is widely debated. Emerging evidence suggests that sub-groups within the range of conformations (or sub-states) contain properties that may be functionally relevant. However, low populations in these sub-states and the transient nature of conformational transitions between these substates present significant challenges for their identification and characterization. Methods and Findings: To overcome these challenges we have developed a new computational technique, quasianharmonic analysis (QAA). QAA utilizes higher-order statistics of protein motions to identify sub-states in the conformational landscape. Further, the focus on anharmonicity allows identification of conformational fluctuations that enable transitions between sub-states. QAA applied to equilibrium simulations of human ubiquitin and T4 lysozyme reveals functionally relevant sub-states and protein motions involved in molecular recognition. In combination with a reaction pathway sampling method, QAA characterizes conformational sub-states associated with cis/trans peptidyl-prolyl isomerization catalyzed by the enzyme cyclophilin A. In these three proteins, QAA allows identification of conformational sub-states, with critical structural and dynamical features relevant to protein function. Conclusions: Overall, QAA provides a novel framework to intuitively understand the biophysical basis of conformational diversity and its relevance to protein function. © 2011 Ramanathan et al

The Darlington and Northallerton Long Term Asthma Study: pulmonary function

BACKGROUND: The Darlington and Northallerton Asthma Study is an observational cohort study started in 1983. At that time little was published about long term outcome in asthma and the contribution of change in reversible disease or airway remodelling to any excess deterioration in function. The study design included regular review of overall and fixed function lung. We report the trends over fifteen years. METHODS: All asthmatics attending secondary care in 1983, 1988 and 1993 were recruited. Pulmonary function was recorded at attendance and potential best function estimated according to protocol. Rate of decline was calculated over each 5-year period and by linear regression analysis in those seen every time. The influence of potential explanatory variables on this decline was explored. RESULTS: 1724 satisfactory 5-year measurements were obtained in 912 subjects and in 200 subjects on all occasions. Overall rate of decline (ml/year (95%CI)) calculated from 5-year periods was FEV1 ♂41.0 (34.7–47.3), ♀28.9 (23.2–34.6) and best FVC ♂63.1 (55.1–71.2)ml/year, ♀45.8 (40.0–51.6).The principal association was with age. A dominant cubic factor suggested fluctuations in the rate of change in middle life with less rapid decline in youth and more rapid decline in the elderly. Rapid decline was possibly associated with short duration. Treatment step did not predict rate of deterioration. CONCLUSIONS: Function declined non-linearly and more rapidly than predicted from normal subjects. It reports for the first time a cubic relationship between age and pulmonary function. This should be taken into account when interpreting other articles reporting change in function over time