scholarly journals Diel investments in metabolite production and consumption in a model microbial system

2021 ◽  
Author(s):  
Mario Uchimiya ◽  
William Schroer ◽  
Malin Olofsson ◽  
Arthur S. Edison ◽  
Mary Ann Moran
Keyword(s):  
Net Primary Production ◽  
Model Performance ◽  
Surface Ocean ◽  
Diel Cycles ◽  
Extracellular Release ◽  
Carbon Transfer ◽  
Bacterial Uptake ◽  
Active Release ◽  
Bacterial Genes ◽  
Improved Model

AbstractOrganic carbon transfer between surface ocean photosynthetic and heterotrophic microbes is a central but poorly understood process in the global carbon cycle. In a model community in which diatom extracellular release of organic molecules sustained growth of a co-cultured bacterium, we determined quantitative changes in the diatom endometabolome and the bacterial uptake transcriptome over two diel cycles. Of the nuclear magnetic resonance (NMR) peaks in the diatom endometabolites, 38% had diel patterns with noon or mid-afternoon maxima; the remaining either increased (36%) or decreased (26%) through time. Of the genes in the bacterial uptake transcriptome, 94% had a diel pattern with a noon maximum; the remaining decreased over time (6%). Eight diatom endometabolites identified with high confidence were matched to the bacterial genes mediating their utilization. Modeling of these coupled inventories with only diffusion-based phytoplankton extracellular release could not reproduce all the patterns. Addition of active release mechanisms for physiological balance and bacterial recognition significantly improved model performance. Estimates of phytoplankton extracellular release range from only a few percent to nearly half of annual net primary production. Improved understanding of the factors that influence metabolite release and consumption by surface ocean microbes will better constrain this globally significant carbon flux.

scholarly journals Variability of North Atlantic CO<sub>2</sub> fluxes for the 2000–2017 period estimated from atmospheric inverse analyses

2021 ◽  
Vol 18 (15) ◽  
pp. 4549-4570
Author(s):  
Zhaohui Chen ◽  
Parvadha Suntharalingam ◽  
Andrew J. Watson ◽  
Ute Schuster ◽  
Jiang Zhu ◽  
...  
Keyword(s):  
North Atlantic ◽  
Model Performance ◽  
Co2 Flux ◽  
Global Ocean ◽  
Surface Ocean ◽  
Basin Scale ◽  
Ensemble Transform Kalman Filter ◽  
Site Network ◽  
Improved Model

Abstract. We present new estimates of the regional North Atlantic (15–80∘ N) CO2 flux for the 2000–2017 period using atmospheric CO2 measurements from the NOAA long-term surface site network in combination with an atmospheric carbon cycle data assimilation system (GEOS-Chem–LETKF, Local Ensemble Transform Kalman Filter). We assess the sensitivity of flux estimates to alternative ocean CO2 prior flux distributions and to the specification of uncertainties associated with ocean fluxes. We present a new scheme to characterize uncertainty in ocean prior fluxes, derived from a set of eight surface pCO2-based ocean flux products, and which reflects uncertainties associated with measurement density and pCO2-interpolation methods. This scheme provides improved model performance in comparison to fixed prior uncertainty schemes, based on metrics of model–observation differences at the network of surface sites. Long-term average posterior flux estimates for the 2000–2017 period from our GEOS-Chem–LETKF analyses are −0.255 ± 0.037 PgC yr−1 for the subtropical basin (15–50∘ N) and −0.203 ± 0.037 PgC yr−1 for the subpolar region (50–80∘ N, eastern boundary at 20∘ E). Our basin-scale estimates of interannual variability (IAV) are 0.036 ± 0.006 and 0.034 ± 0.009 PgC yr−1 for subtropical and subpolar regions, respectively. We find statistically significant trends in carbon uptake for the subtropical and subpolar North Atlantic of −0.064 ± 0.007 and −0.063 ± 0.008 PgC yr−1 decade−1; these trends are of comparable magnitude to estimates from surface ocean pCO2-based flux products, but they are larger, by a factor of 3–4, than trends estimated from global ocean biogeochemistry models.

scholarly journals Diel Investments in Phytoplankton Metabolite Production Influenced by Associated Heterotrophic Bacteria

2020 ◽  
Author(s):  
Mario Uchimiya ◽  
William Schroer ◽  
Malin Olofsson ◽  
Arthur S. Edison ◽  
Mary Ann Moran
Keyword(s):  
Carbon Sequestration ◽  
Carbon Cycle ◽  
Organic Molecules ◽  
Heterotrophic Bacteria ◽  
Global Carbon Cycle ◽  
Phytoplankton Production ◽  
Surface Ocean ◽  
Extracellular Release ◽  
Carbon Transfer ◽  
Global Carbon

AbstractOrganic carbon transfer between photoautotrophic and heterotrophic microbes in the surface ocean mediated through metabolites dissolved in seawater is a central but poorly understood process in the global carbon cycle. In a synthetic microbial community in which diatom extracellular release of organic molecules sustained growth of a co-cultured bacterium, metabolite transfer was assessed over two diel cycles based on per cell quantification of phytoplankton endometabolites and bacterial transcripts. Of 31 phytoplankton endometabolites identified and classified into temporal abundance patterns, eight could be matched to patterns of bacterial transcripts mediating their uptake and catabolism. A model simulating the coupled endometabolite-transcription relationships hypothesized that one category of outcomes required an increase in phytoplankton metabolite synthesis in response to the presence of the bacterium. An experimental test of this hypothesis confirmed higher endometabolome accumulation in the presence of bacteria for all five compounds assigned to this category – leucine, glycerol-3-phosphate, glucose, and the organic sulfur compounds dihydroxypropanesulfonate and dimethylsulfoniopropionate. Partitioning of photosynthate into rapidly-cycling dissolved organic molecules at the expense of phytoplankton biomass production has implications for carbon sequestration in the deep ocean. That heterotrophic bacteria can impact this partitioning suggests a previously unrecognized influence on the ocean’s carbon reservoirs.Significance StatementMicrobes living in the surface ocean are critical players in the global carbon cycle, carrying out a particularly key role in the flux of carbon between the ocean and atmosphere. The release of metabolites by marine phytoplankton and their uptake by heterotrophic bacteria is one of the major routes of microbial carbon turnover. Yet the identity of these metabolites, their concentration in seawater, and the factors that affect their synthesis and release are poorly known. Here we provide experimental evidence that marine heterotrophic bacteria can affect phytoplankton production and extracellular release of metabolites. This microbial interaction has relevance for the partitioning of photosynthate between dissolved and particulate carbon reservoirs in the ocean, an important factor in oceanic carbon sequestration.

scholarly journals Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 238
Author(s):  
Pablo Contreras ◽  
Johanna Orellana-Alvear ◽  
Paul Muñoz ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Random Forest ◽  
Model Performance ◽  
Early Warning Systems ◽  
Point Of View ◽  
Lead Times ◽  
Physical Parameters ◽  
Runoff Forecasting ◽  
Spatio Temporal ◽  
Improved Model ◽  
Search Approach

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

scholarly journals Incremental value of QT interval for the prediction of obstructive coronary artery disease in patients with chest pain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dong-Hyuk Cho ◽  
Jimi Choi ◽  
Mi-Na Kim ◽  
Hee-Dong Kim ◽  
Soon Jun Hong ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Chest Pain ◽  
Coronary Artery ◽  
Qt Interval ◽  
Obstructive Coronary Artery Disease ◽  
Model Performance ◽  
Clinical Score ◽  
Qtc Prolongation ◽  
Artery Disease ◽  
Improved Model

AbstractIdentification of obstructive coronary artery disease (OCAD) in patients with chest pain is a clinical challenge. The value of corrected QT interval (QTc) for the prediction of OCAD has yet to be established. We consecutively enrolled 1741 patients with suspected angina. The presence of obstructive OCAD was defined as ≥ 50% diameter stenosis by coronary angiography. The pre-test probability was evaluated by combining QTc prolongation with the CAD Consortium clinical score (CAD2) and the updated Diamond-Forrester (UDF) score. OCAD was detected in 661 patients (38.0%). QTc was longer in patients with OCAD compared with those without OCAD (444 ± 34 vs. 429 ± 28 ms, p < 0.001). QTc was increased by the severity of OCAD (P < 0.001). QTc prolongation was associated with OCAD (odds ratio (OR), 2.27; 95% confidence interval (CI), 1.81–2.85). With QTc, the C-statistics increased significantly from 0.68 (95% CI 0.66–0.71) to 0.76 (95% CI 0.74–0.78) in the CAD2 and from 0.64 (95% CI 0.62–0.67) to 0.74 (95% CI 0.72–0.77) in the UDF score, respectively. QT prolongation predicted the presence of OCAD and the QTc improved model performance to predict OCAD compared with CAD2 or UDF scores in patients with suspected angina.

scholarly journals Incremental model breakdown to assess the multi-hypotheses problem

2018 ◽  
Vol 22 (8) ◽  
pp. 4565-4581 ◽  
Author(s):  
Florian U. Jehn ◽  
Lutz Breuer ◽  
Tobias Houska ◽  
Konrad Bestian ◽  
Philipp Kraft
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Complex Model ◽  
Model Parameters ◽  
Objective Functions ◽  
Final Model ◽  
Incremental Model ◽  
Model Efficiency ◽  
Improved Model ◽  
Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

scholarly journals A Thermodynamic Model of the Formation, Growth, and Decay of First-Year Sea Ice

1987 ◽  
Vol 33 (113) ◽  
pp. 105-119 ◽  
Author(s):  
R. Gabison
Keyword(s):  
Sea Ice ◽  
Thermodynamic Model ◽  
Model Performance ◽  
Melting Process ◽  
The Arctic ◽  
First Year ◽  
Climatological Data ◽  
Oceanic Tides ◽  
Waves And Tides ◽  
Improved Model

AbstractThe formulation and application of a onedimensional sea-ice thermodynamic model is presented in this paper. The model’s sensitivity to changes in oceanic and atmospheric parameters is analyzed and compared with previous studies. The model is next applied to three locations in the Arctic: Cambridge Bay, Frobisher Bay, and Alert Inlet to study the model’s ability to simulate the annual cycle of first-year ice. The model’s results are compared with available climatological data and discussed in terms of the main thermodynamic processes, the combined effects of oceanic tides, and of sea-ice deterioration by melting on the break-up of sea ice.It is shown that the model is effective in simulating the climatology of the first-year ice thickness at the three Arctic locations. The study also suggests that improved model performance can be expected from additional research and application of flexural forcing of the ice by waves and tides, and of deterioration of ice strength during the melting process.

scholarly journals Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps

2020 ◽  
Vol 24 (5) ◽  
pp. 2711-2729 ◽  
Author(s):  
Joseph L. Gutenson ◽  
Ahmad A. Tavakoly ◽  
Mark D. Wahl ◽  
Michael L. Follum
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Army Corps ◽  
Time Step ◽  
Large Spatial Scale ◽  
Reservoir Routing ◽  
Routing Methods ◽  
Improved Model

Abstract. Large-scale hydrologic forecasts should account for attenuation through lakes and reservoirs when flow regulation is present. Globally generalized methods for approximating outflow are required but must contend with operational complexity and a dearth of information on dam characteristics at global spatial scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting, particularly at diurnal time steps. This research compares two parsimonious reservoir routing methods at daily steps: Döll et al. (2003) and Hanasaki et al. (2006). These reservoir routing methods have been previously implemented in large-scale hydrologic modeling applications and have been typically evaluated seasonally. These routing methods are compared across 60 reservoirs operated by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The method proposed by Döll et al. (2003) outperformed that presented by Hanasaki et al. (2006) at a daily time step and improved model skill over most run-of-the-river conditions. The temporal resolution of the model influences model performances. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the method proposed by Döll et al. (2003) could enhance large-scale hydrologic forecasting, but can be subject to instability under certain conditions.

scholarly journals Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data

2019 ◽  
Vol 23 (6) ◽  
pp. 2647-2663 ◽  
Author(s):  
Yingchun Huang ◽  
András Bárdossy ◽  
Ke Zhang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Model Performance ◽  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Dense Network ◽  
Spatially Distributed ◽  
Important Input ◽  
Temporal And Spatial ◽  
Improved Model

Abstract. Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

Predicting Flowering of Rhizome Johnsongrass (Sorghum halepense) Populations Using a Temperature-dependent Model

Weed Science ◽  
1996 ◽  
Vol 44 (2) ◽  
pp. 266-272 ◽  
Author(s):  
David L. Holshouser ◽  
James M. Chandler
Keyword(s):  
Rate Equation ◽  
Model Performance ◽  
Population Level ◽  
Weibull Function ◽  
Data Sets ◽  
Temperature Dependent ◽  
Independent Field ◽  
Dependent Model ◽  
Improved Model ◽  
Temperature Dependent Model

Research was conducted to formulate a temperature-dependent population-level model for rhizome johnsongrass flowering. A nonlinear poikilotherm rate equation was used to describe development as a function of temperature and a temperature-independent Weibull function was used to distribute development times for the population. Johnsongrass flowering data were collected under constant temperature conditions to parameterize the poikilotherm rate equation and Weibull function. Coupling the poikilotherm rate equation with the Weibull function resulted in a population level temperature-dependent model. The model was validated against independent field data sets. The model accurately predicted rhizome johnsongrass flowering from plants emerging in the spring. The model performed poorly for plants emerging in summer. Adjustments to the high-temperature inhibition parameter of the poikilotherm rate equation improved model performance in the summer without affecting spring predictions.

scholarly journals Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000

2009 ◽  
Vol 6 (11) ◽  
pp. 2333-2353 ◽  
Author(s):  
M. Vichi ◽  
S. Masina
Keyword(s):  
Organic Carbon ◽  
Mixed Layer ◽  
General Circulation ◽  
Net Primary Production ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Data Set ◽  
Surface Ocean ◽  
Ocean Biogeochemistry

Abstract. Global Ocean Biogeochemistry General Circulation Models are useful tools to study biogeochemical processes at global and large scales under current climate and future scenario conditions. The credibility of future estimates is however dependent on the model skill in capturing the observed multi-annual variability of firstly the mean bulk biogeochemical properties, and secondly the rates at which organic matter is processed within the food web. For this double purpose, the results of a multi-annual simulation of the global ocean biogeochemical model PELAGOS have been objectively compared with multi-variate observations from the last 20 years of the 20th century, both considering bulk variables and carbon production/consumption rates. Simulated net primary production (NPP) is comparable with satellite-derived estimates at the global scale and when compared with an independent data-set of in situ observations in the equatorial Pacific. The usage of objective skill indicators allowed us to demonstrate the importance of comparing like with like when considering carbon transformation processes. NPP scores improve substantially when in situ data are compared with modeled NPP which takes into account the excretion of freshly-produced dissolved organic carbon (DOC). It is thus recommended that DOC measurements be performed during in situ NPP measurements to quantify the actual production of organic carbon in the surface ocean. The chlorophyll bias in the Southern Ocean that affects this model as well as several others is linked to the inadequate representation of the mixed layer seasonal cycle in the region. A sensitivity experiment confirms that the artificial increase of mixed layer depths towards the observed values substantially reduces the bias. Our assessment results qualify the model for studies of carbon transformation in the surface ocean and metabolic balances. Within the limits of the model assumption and known biases, PELAGOS indicates a net heterotrophic balance especially in the more oligotrophic regions of the Atlantic during the boreal winter period. However, at the annual time scale and over the global ocean, the model suggests that the surface ocean is close to a weakly positive autotrophic balance in accordance with recent experimental findings and geochemical considerations.

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