Modeling the irradiance dependency of the quantum efficiency of potosynthesis

Measures of the quantum efficiency of photosynthesis (phi(PSII)) across an irradiance (E) gradient are an increasingly common physiological assay and alternative to traditional photosynthetic-irradiance (PE) assays. Routinely, the analysis and interpretation of these data are analogous to PE measurements. Relative electron transport rates (rETR = E x phi(PSII)) are computed and fit to a PE curve to retrieve physiologically meaningful PE parameters. This widespread approach is statistically flawed as the response variable (rETR) is explicitly dependent on the predictor variable (E). Alternatively the E-dependency of phi(PSII) can be modeled directly while retaining the desired PE parameters by normalizing a given PE model to E. This manuscript presents a robust analysis in support of this alternative procedure. First, we demonstrate that scaling phi(PSII) to rETR unnecessarily amplifies the measurement error of phi(PSII) and using a Monte-Carlo analysis on synthetic data induces significantly higher uncertainty in computed PE parameters relative to modeling the E-dependency of phi(PSII) directly. Next a large dataset is simultaneously fitted to four PE models implemented in their original and E-normalized forms. Four statistical criteria used to evaluate the efficacy of nonlinear models demonstrate improved model fits and more precise PE parameters when data are modeled as E-dependent changes in phi(PSII). The analysis presented in this manuscript clearly demonstrates that modeling the E-dependency of phi(PSII) directly should be the norm for interpreting active fluorescence measures

Estimating primary production from oxygen time series: A novel approach in the frequency domain

Based on an analysis in the frequency domain of the governing equation of oxygen dynamics in aquatic systems, we derive a new method for estimating gross primary production (GPP) from oxygen time series. The central result of this article is a relation between time averaged GPP and the amplitude of the diel harmonic in an oxygen time series. We call this relation the Fourier method for estimating GPP. To assess the performance and accuracy of the method, we generate synthetic oxygen time series with a series of gradually more complex models, and compare the result with simulated GPP. We demonstrate that the method is applicable in systems with a range of rates of mixing, air–water exchange and primary production. We also apply the new method to oxygen time series from the Scheldt estuary (Belgium) and compare it with 14C-based GPP measurements. We demonstrate the Fourier method is particularly suited for estimating GPP in estuarine and coastal systems where tidal advection has a large imprint in observed oxygen concentrations. As such it enlarges the number of systems where GPP can be estimated from in situ oxygen concentrations. By shifting the focus to the frequency domain, we also gain some useful insights on the effect of observational error and of stochastic drivers of oxygen dynamics on metabolic estimates derived from oxygen time series

Imaging-in-flow: digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms

Traditional taxonomic identification of planktonic organisms is based on light microscopy, which is both time-consuming and tedious. In response, novel ways of automated (machine) identification, such as flow cytometry, have been investigated over the last two decades. To improve the taxonomic resolution of particle analysis, recent developments have focused on "imaging-in-flow," i.e., the ability to acquire microscopic images of planktonic cells in a flow-through mode. Imaging-in-flow systems are traditionally based on classical brightfield microscopy and are faced with a number of issues that decrease the classification performance and accuracy (e. g., projection variance of cells, migration of cells out of the focus plane). Here, we demonstrate that a combination of digital holographic microscopy (DHM) with imaging-in-flow can improve the detection and classification of planktonic organisms. In addition to light intensity information, DHM provides quantitative phase information, which generates an additional and independent set of features that can be used in classification algorithms. Moreover, the capability of digitally refocusing greatly increases the depth of field, enables a more accurate focusing of cells, and reduces the effects of position variance. Nanoplanktonic organisms similar in shape were successfully classified from images captured with an off-axis DHM with partial coherence. Textural features based on DHM phase information proved more efficient in separating the three tested phytoplankton species compared with shape-based features or textural features based on light intensity. An overall classification score of 92.4% demonstrates the potential of holographic-based imaging-in-flow for similar looking organisms in the nanoplankton range

Carbon and nitrogen cycling in the Scheldt estuary: the major players, long-term changes and an integrated view

The Scheldt estuary is a highly heterotrophic, nutrient-rich, turbid, tidal estuary in a densely populated area (Belgium/The Netherlands). Here we present the results (1) on the long-term changes in nutrient loadings and transformations within the estuary and (2) on nitrogen cycling rate measurements obtained with isotopic tracers. Moreover, we have developed and applied novel techniques that allow direct linking of process rates to the identity and biomass of the organisms involved. Monitoring data and process studies have been used in numerical models to integrate the various biogeochemical cycles and to advance our understanding of the evolving estuarine filter function of the Scheldt estuary

Technical Note: Calibration and validation of geophysical observation models

We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines: soil moisture and water quality. The results demonstrate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces, and the resulting uncertainty can be used as input to data assimilation schemes

Strengthening Europe’s capability in biological ocean observations

This publication is primarily aimed at stakeholders involved in ocean observing, spanning diverse roles from commissioning, managing, funding and coordinating, to developing, implementing, or advising on, ocean observation programmes. Such programmes will have strategic and policy drivers but their main purpose may vary from predominantly researchdriven scientific purposes to environmental monitoring for providing data and reporting to legally-binding regulations or directives. The main focus is on European capabilities but set in a global context with the various actors spanning a variety of geographical scales from national to regional and European. Key stakeholder organizations include environmental or other agencies; marine research institutions, their researchers and operators; international and regional ocean observing initiatives and programmes; national, regional and European policy makers and their advisors; national stations for observations; etc.). It will also be of interest to the wider marine and maritime research and policy community. The main aim of the publication is to increase the relevance of current (and future) European biological ocean observation capacity to strengthen global efforts towards our understanding of the ocean and enhance marine biodiversity conservation, for maintaining a healthy ocean for healthy societies. This document explains why biological ocean observations are needed to assess progress against national and international conservation targets, the Sustainable Development Goals (SDGs), the Blue Growth agenda and to contribute to key EU directives including the Marine Strategy Framework Directive (MSFD). To achieve this, the publication highlights the need of biological ocean observations to reflect clearly defined hypotheses about potential causes of change, including the combined impacts of local and global drivers, and to support the management of our impacts on the ocean. Additionally, it calls for flexible biological ocean observing programmes to capture the relevant drivers operating at multiple spatial scales, by networking and integration of ongoing monitoring programmes, methodological standardization and appropriate policies of data integration and dissemination. It then presents key variables, elements and information products to inform on the status and trends of marine biodiversity. The Future Science Brief finishes by recommending priorities for enhancing relevant and integrated current biological ocean observing capacity in Europe

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study

Objectives: To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs). Design: Prospective cross-sectional study. Setting: Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals. Participants: 494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious. Measurements: DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets. Methods: Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus. Results: DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity. Conclusions: DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.</p