scholarly journals Phytoplankton growth rate modelling: can spectroscopic cell chemotyping be superior to physiological predictors?

2017 ◽  
Vol 284 (1848) ◽  
pp. 20161956 ◽  
Author(s):  
Andrea Fanesi ◽  
Heiko Wagner ◽  
Christian Wilhelm
Keyword(s):  
Growth Rate ◽  
Predictive Power ◽  
Partial Least Square ◽  
Phytoplankton Growth ◽  
Photosynthetic Parameters ◽  
Strong Impact ◽  
High Predictive Power ◽  
Phytoplankton Communities ◽  
Quality Control Tool ◽  
Phytoplankton Cells

Climate change has a strong impact on phytoplankton communities and water quality. However, the development of robust techniques to assess phytoplankton growth is still in progress. In this study, the growth rate of phytoplankton cells grown at different temperatures was modelled based on conventional physiological traits (e.g. chlorophyll, carbon and photosynthetic parameters) using the partial least square regression (PLSR) algorithm and compared with a new approach combining Fourier transform infrared-spectroscopy and PLSR. In this second model, it is assumed that the macromolecular composition of phytoplankton cells represents an intracellular marker for growth. The models have comparable high predictive power ( R 2 > 0.8) and low error in predicting new observations. Interestingly, not all of the predictors present the same weight in the modelling of growth rate. A set of specific parameters, such as non-photochemical fluorescence quenching (NPQ) and the quantum yield of carbon production in the first model, and lipid, protein and carbohydrate contents for the second one, strongly covary with cell growth rate regardless of the taxonomic position of the phytoplankton species investigated. This reflects a set of specific physiological adjustments covarying with growth rate, conserved among taxonomically distant algal species that might be used as guidelines for the improvement of modern primary production models. The high predictive power of both sets of cellular traits for growth rate is of great importance for applied phycological studies. Our approach may find application as a quality control tool for the monitoring of phytoplankton populations in natural communities or in photobioreactors.

scholarly journals Prediction of Phytoplankton Biomass in Small Rivers of Central Spain by Data Mining Method of Partial Least-Squares Regression

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 16 ◽  
Author(s):  
Juan Carlos García-Prieto ◽  
Francisco Javier Burguillo Muñoz ◽  
Manuel G. Roig ◽  
José Bernardo Proal-Najera
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ecological Status ◽  
Phytoplankton Growth ◽  
Small Rivers ◽  
Strong Impact ◽  
Least Squares Regression ◽  
Phytoplankton Communities ◽  
The Impact

The Water Framework Directive (WFD, EC, 2000) states that the “good” ecological status of natural water bodies must be based on their chemical, hydromorphological and biological features, especially under drastic conditions of floods or droughts. Phytoplankton is considered a good environmental bioindicator (WFD) and climate change has a strong impact on phytoplankton communities and water quality. The development of robust techniques to predict and control phytoplankton growth is still in progress. The aim of this study is to analyze the impact of the different stressors associated with the change in phytoplanktonic communities in small rivers in the center of the Iberian Peninsula (Southwestern Europe). A statistical study on the identification of the essential limiting variables in the phytoplankton growth and its seasonal variation by climate change was carried out. In this study, a new method based on the partial least-squares (PLS) regression technique has been used to predict the concentration of phytoplankton and cyanophytes from 22 variables usually monitored in rivers. The predictive models have shown a good agreement between training and test data sets in rivers and seasons (dry and wet). The phytoplankton in dry periods showed greatest similarities, these dry periods being the most important factor in the phytoplankton proliferation

The Effect of Phosphorus Limitation on Algal Growth Rates: Evidence from Alkaline Phosphatase

1981 ◽  
Vol 38 (11) ◽  
pp. 1421-1427 ◽  
Author(s):  
Ralph E. H. Smith ◽  
Jaap Kalff
Keyword(s):  
Growth Rate ◽  
Alkaline Phosphatase ◽  
Total Phosphorus ◽  
Growth Rates ◽  
Algal Growth ◽  
Phosphorus Limitation ◽  
Phytoplankton Growth ◽  
Limited Growth ◽  
Phytoplankton Communities

The inducible enzyme alkaline phosphatase (APA) was used to quantify phosphorus limitation of freshwater phytoplankton growth in situ. Natural phytoplankton communities from oligotrophic–eutrophic Lake Memphremagog were grown at phosphorus-limited rates of 0.06–0.93 per day in continuous, lake water cultures. APA varied inversely with phosphorus-limited growth rate and was little influenced by the community composition changes that occurred in culture. Compared to cultures, average in situ APA in Lake Memphremagog during the ice-free season was low, and suggested phosphorus-limited growth rates greater than 0.6 per day in even the most oligotrophic parts of the lake. Although algal abundance varies with total phosphorus among most north-temperate zone lakes, including Lake Memphremagog, our data show that average community growth rates are normally high and change relatively little over a fourfold range of total phosphorus concentration.Key words: alkaline phosphatase, phytoplankton, growth rate, phosphorus

A modeling approach to simulate impact of climate change in lake water quality: phytoplankton growth rate assessment

1998 ◽  
Vol 37 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Hany Hassan ◽  
Keisuke Hanaki ◽  
Tomonori Matsuo
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Growth Rate ◽  
Dissolved Oxygen ◽  
Lake Water ◽  
Phytoplankton Growth ◽  
Quality Data ◽  
Model Parameters ◽  
Lake Water Quality ◽  
Phytoplankton Growth Rate

Global climate change induced by increased concentrations of greenhouse gases (especially CO2) is expected to include changes in precipitation, wind speed, incoming solar radiation, and air temperature. These major climate variables directly influence water quality in lakes by altering changes in flow and water temperature balance. High concentration of nutrient enrichment and expected variability of climate can lead to periodic phytoplankton blooms and an alteration of the neutral trophic balance. As a result, dissolved oxygen levels, with low concentrations, can fluctuate widely and algal productivity may reach critical levels. In this work, we will present: 1) recent results of GCMs climate scenarios downscaling project that was held at the University of Derby, UK.; 2) current/future comparative results of a new mathematical lake eutrophication model (LEM) in which output of phytoplankton growth rate and dissolved oxygen will be presented for Suwa lake in Japan as a case study. The model parameters were calibrated for the period of 1973–1983 and validated for the period of 1983–1993. Meterologic, hydrologic, and lake water quality data of 1990 were selected for the assessment analysis. Statistical relationships between seven daily meteorological time series and three airflow indices were used as a means for downscaling daily outputs of Hadley Centre Climate Model (HadCM2SUL) to the station sub-grid scale.

The Thiamine Content of Phytoplankton Cells Is Affected by Abiotic Stress and Growth Rate

2012 ◽  
Vol 65 (3) ◽  
pp. 566-577 ◽  
Author(s):  
Peter Sylvander ◽  
Norbert Häubner ◽  
Pauline Snoeijs
Keyword(s):  
Growth Rate ◽  
Abiotic Stress ◽  
Thiamine Content ◽  
Phytoplankton Cells

Method for monitoring plasma progesterone concentrations in pregnancy.

1976 ◽  
Vol 22 (4) ◽  
pp. 422-428 ◽  
Author(s):  
P Winkel ◽  
P Gaede ◽  
J Lyngbye
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Reference Material ◽  
Gestational Age ◽  
Predictive Power ◽  
Mean Value ◽  
Single Subject ◽  
Progesterone Concentration ◽  
Plasma Progesterone ◽  
In Pregnancy

Abstract We present a time-series model for monitoring concentrations in plasma of hormones produced in the placenta, progesterone being chosen as an example. The model, which is based on the assumption that variations in plasma progesterone concentration in pregnant subjects mainly reflect variations in the growth rate of the placenta, was applied to eight series of progesterone values measured during pregnancy in eight subjects. In the model, which was found to fit the data, it is assumed that progesterone concentration is proportional to the size of the placenta and that the growth rate of the placenta varies at random, with a mean value alpha. The variation of alpha was of the same magnitude among and within the subjects. If the average of many subjects alpha values is used, a single subject may be used as her own reference, based on only one previous observation. When two observations are available, an individual's own alpha value may be estimated and used for the prediction. The predictive power of the new method was found to be far superior to the conventional method in which a single sample reference material is used. Furthermore, one need not know the gestational age in order to use the method.

scholarly journals QSAR studies on imidazoles and sulfonamides as antidiabetic agents

2019 ◽  
Vol 30 (1) ◽  
pp. 5-13
Author(s):  
Veerasamy Ravichandran ◽  
Rajak Harish
Keyword(s):  
Predictive Power ◽  
Partial Least Square ◽  
Least Square ◽  
Antidiabetic Activity ◽  
Statistical Parameters ◽  
Qsar Studies ◽  
2D Qsar ◽  
Partial Least Square Analysis ◽  
Qsar Models ◽  
The Relationship

Abstract The main objective of the present study was to establish significant and validated QSAR models for imidazoles and sulfonamides to explore the relationship between their physicochemical properties and antidiabetic activity. Two dimensional QSAR models had been developed by multiple linear regression and partial least square analysis methods, and then validated for internal and external predictions. The established 2D QSAR models were statistically significant and highly predictive. The validation methods provided significant statistical parameters with q2 > 0.5 and pred_r2 > 0.6, which proved the predictive power of the models. The developed 2D QSAR models revealed the significance of SlogP and T_N_O_5, and Mol.Wt and SsBrE-index properties of imidazoles and sulfonamides on their antidiabetic activity, respectively. These results should prove to be an essential guide for the further design and development of new imidazoles and sulfonamides having better antidiabetic activity.

scholarly journals ββ DECAY AND NUCLEAR STRUCTURE

2007 ◽  
Vol 16 (02) ◽  
pp. 552-560 ◽  
Author(s):  
E. CAURIER ◽  
F. NOWACKI ◽  
A. POVES
Keyword(s):  
Shell Model ◽  
Nuclear Matrix ◽  
Large Scale ◽  
Predictive Power ◽  
Strong Impact ◽  
Matrix Elements ◽  
Model Calculations ◽  
Mode Stability ◽  
Recent Developments

The determination of accurate nuclear matrix elements for ββ decay processes is a challenge for nuclear theory and can have a strong impact in neutrino physics. Large Scale Shell Model (LSSM) calculations are among the best tools for such determination and recent developments have allowed to extend its application domains. In particular, systematic studies of nuclear matrix elements calculations have been now undertaken in this framework for most of the ββ emitters. These calculations are crucial in the determination of the most favorable emitters in the forthcoming generation of ββ experiments. The present paper focuses on the recent advances and remaining difficulties of shell model calculations for the neutrinoless mode. Stability and predictive power of the results will be discussed.

scholarly journals Anthracene phytotoxicity in the freshwater flagellate alga Euglena agilis Carter

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sreejith Kottuparambil ◽  
Jihae Park
Keyword(s):  
Growth Rate ◽  
Environmental Protection Agency ◽  
Photosynthetic Pigment ◽  
Photosynthetic Electron Transport ◽  
Photosynthetic Parameters ◽  
Energy Regulation ◽  
Total Carotenoids ◽  
Ecological Processes ◽  
Polycyclic Aromatic ◽  
Median Effective Concentration

Abstract The freshwater flagellate alga Euglena agilis Carter was exposed to the polycyclic aromatic hydrocarbon (PAH) anthracene for 96 h under optimal photosynthetically active radiation (PAR), and responses of growth, photosynthetic pigment production, and photosynthetic efficiency were assessed. Anthracene reduced the growth rate (μ) and levels of chlorophyll a (Chl a), chlorophyll b (Chl b), and total carotenoids. The growth rate was more sensitive than photosynthetic parameters, with a median effective concentration (EC50) of 4.28 mg L−1. Between 5 and 15 mg L−1, anthracene inhibited the maximum quantum yield (Fv/Fm) of photosystem II (PSII) and the maximum photosynthetic electron transport rate through PSII (rETRmax) with EC50 values of 14.88 and 11.8 mg L−1, respectively. At all anthracene concentrations, intracellular reactive oxygen species (ROS) were elevated, indicating increased oxidative stress. Anthracene presumably reduced the PSII efficiency of photochemical energy regulation and altered the photochemistry through intracellular ROS formation. Acute exposure to PAHs may induce severe physiological changes in phytoplankton cells, which may influence vital ecological processes within the aquatic environments. Additionally, growth and Chl a content may serve as sensitive risk assessment parameters of anthracene toxicity in water management since EC50 values for both overlap with anthracene levels (8.3 mg L−1) permitted by the US Environmental Protection Agency (USEPA).

scholarly journals Predicting Liver Disease Risk Using a Combination of Common Clinical Markers: A Screening Model from Routine Health Check-Up

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yi Wang ◽  
Yanni Li ◽  
Xiaoyi Wang ◽  
Ranko Gacesa ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Liver Disease ◽  
Early Detection ◽  
Predictive Power ◽  
Disease Screening ◽  
Health Check ◽  
Initial Presentation ◽  
Screening Tools ◽  
Clinical Markers ◽  
Liver Disorders ◽  
High Predictive Power

Background. Early detection is crucial for the prognosis of patients with autoimmune liver disease (AILD). Due to the relatively low incidence, developing screening tools for AILD remain a challenge. Aims. To analyze clinical characteristics of AILD patients at initial presentation and identify clinical markers, which could be useful for disease screening and early detection. Methods. We performed observational retrospective study and analyzed 581 AILD patients who were hospitalized in the gastroenterology department and 1000 healthy controls who were collected from health management center. Baseline characteristics at initial presentation were used to build regression models. The model was validated on an independent cohort of 56 patients with AILD and 100 patients with other liver disorders. Results. Asymptomatic AILD individuals identified by the health check-up are increased yearly (from 31.6% to 68.0%, p<0.001). The cirrhotic rates at an initial presentation are decreased in the past 18 years (from 52.6% to 20.0%, p<0.001). Eight indicators, which are common in the health check-up, are independent risk factors of AILD. Among them, abdominal lymph node enlargement (LN) positive is the most significant different (OR 8.85, 95% CI 2.73-28.69, p<0.001). The combination of these indicators shows high predictive power (AUC=0.98, sensitivity 89.0% and specificity 96.4%) for disease screening. Except two liver or cholangetic injury makers, the combination of AGE, GENDER, GLB, LN, concomitant extrahepatic autoimmune diseases, and familial history also shows a high predictive power for AILD in other liver disorders (AUC=0.91). Conclusion. Screening for AILD with described parameters can detect AILD in routine health check-up early, effectively and economically. Eight variables in routine health check-up are associated with AILD and the combination of them shows good ability of identifying high-risk individuals.

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