Developing a mathematical modeling method for determining the potential rates of microbial ammonia oxidation and nitrite oxidation in environmental samples

2018 ◽  
Vol 133 ◽  
pp. 116-123 ◽  
Author(s):  
Yiguo Hong ◽  
Yan Wang ◽  
Jiapeng Wu ◽  
Lijing Jiao ◽  
Xiang He ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Environmental Samples ◽  
Ammonia Oxidation ◽  
Modeling Method ◽  
Nitrite Oxidation

scholarly journals Quantification of multiple simultaneously occurring nitrogen flows in the euphotic ocean

2016 ◽  
Author(s):  
Min Nina Xu ◽  
Yanhua Wu ◽  
Li Wei Zheng ◽  
Zhenzhen Zheng ◽  
Huade Zhao ◽  
...  
Keyword(s):  
Matrix Equation ◽  
Ammonia Oxidation ◽  
Mass Conservation ◽  
Quantitative Information ◽  
Nitrite Oxidation ◽  
Dissolved Nitrogen ◽  
Total Dissolved Nitrogen ◽  
Transformation Rates ◽  
The Matrix ◽  
Multiple Transformation

Abstract. The general features of the N cycle in the sunlit ocean are known, but quantitative information about multiple transformation rates among nitrogen pools, i.e., ammonium (NH4+), nitrite (NO2−), nitrate (NO3−) and particulate/dissolved organic nitrogen (PN/DON), are limited due to methodological difficulties. By adding a single 15N-labelled NH4+ tracer into incubators, we monitor ed the changes in concentration and isotopic composition of the total dissolved nitrogen (TDN), PN, NH4+, NO2−, and NO3− pools to trace the 15N and 14N flows. Based on mass conservation and isotope mass balance, we formulate d a matrix equation that allow edus to simultaneously derive the rates of multiple transformation processes in the nitrogen reaction web . We abandoned inhibitors and minimized the alteration of the system by adding a limited amount of tracer. In one single incubation, solution of the matrix equation provided the rates of NH4+, NO2−, and NO3− uptake; ammonia oxidation; nitrite oxidation; nitrite excretion; DON release; and potentially, the remineralization rate. To our knowledge, this is the first and most convenient method designed to quantitatively and simultaneously resolve complicated nitrogen transformation rates, albeit with some uncertainties. Field examples are given, and c omparisons with conventional labeling methods are discussed.

Temporal variation in maximum cell-specific nitrification rate

2010 ◽  
Vol 61 (8) ◽  
pp. 2069-2073 ◽  
Author(s):  
M. Fujita ◽  
K. Tsuji ◽  
A. Akashi
Keyword(s):  
Ammonia Oxidation ◽  
Thermal Power ◽  
Nitrification Rate ◽  
Nitrite Oxidation ◽  
Cell Numbers ◽  
Oxidation Rates ◽  
Batch Tests ◽  
Nitrogen Loads ◽  
Clone Analysis ◽  
Maximum Cell

The cell numbers of ammonia-oxidising bacteria (AOBs), Nitrospira and Nitrobacter in activated sludge used to treat wastewater from a thermal power plant in Japan were examined for nine months using a real-time PCR quantification technique. AOB cell numbers ranged 2.8 × 1010–2.3 × 1011 cell/L. The amoA clone analysis showed that the only Nitrosomonas halophila was responsible for ammonia oxidation over the period. Nitrospira were in the range of 2.6 × 109–2.4 × 1010 cell/L and Nitrobacter were less than 1% as common as Nitrospira. Meanwhile, maximum nitrification rates, maximum ammonia- and nitrite-oxidation rates obtained from aerobic batch tests, ranged 0.5–1.3 mmol-N/L h and 1.0–2.5 mmol-N/L h, respectively. No clear correlations were observed between the cell numbers of AOBs or Nitrospira and their maximum rates, because the maximum cell-specific ammonia- and nitrite-oxidation rates varied remarkably over the ranges of 1.1–11.9 and 2.4–21.6 fmol-N/cell h, respectively. To explore the factors controlling maximum cell-specific nitrification rates, the relationship to influent nitrogen loads per AOB or Nitrospira cell numbers was investigated. Fairly good correlations were obtained. Considering the effluent ammonia and nitrite concentrations were zero and only Nitrosomonas halophila had a role in ammonia oxidation over the period, we conclude that the amount of nitrogen oxidised per AOB or Nitrospira cell numbers likely controls maximum cell-specific ammonia- or nitrite-oxidation rates, respectively.

Mathematical Modeling Method for Generation of Gaussian-Type Removal Function in Fluid Jet Polishing

2018 ◽  
Vol 38 (10) ◽  
pp. 1022002
Author(s):  
王中昱 Wang Zhongyu ◽  
张连新 Zhang Lianxin ◽  
孙鹏飞 Sun Pengfei ◽  
李建 Li Jian ◽  
尹承真 Yin Chengzhen
Keyword(s):  
Mathematical Modeling ◽  
Modeling Method ◽  
Gaussian Type

Mathematical modeling method of cell tension and compression based on multi-modal mechanical signals

2020 ◽  
pp. 1-10
Author(s):  
Dongyang Pan ◽  
Jingrui Liu
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Elastic Modulus ◽  
Human Health ◽  
Mechanical Stimulation ◽  
Cell Morphology ◽  
Cell Mechanics ◽  
Modeling Method ◽  
Mechanical Signals ◽  
Stimulation Of

Mechanical biology is the study of the influence of the mechanical environment on human health, disease, or injury. To study the mechanism of the organism’s perception and response to mechanical signals can promote the development of biomedical basic and clinical research, and promote human health. The purpose of this paper is to study the mathematical modeling method of the effect of multimodal mechanical signals on cell stretching and compression. This article first established a cell mechanics model based on the generalization of membrane theory, introduced the micro-manipulation techniques used to characterize cell mechanics and the method of cell mechanics loading, and then explained why mathematical modeling was established. Finally, according to the multi-modality During the mechanical preparation process, the effects of multi-modal mechanical signals on the stretching and compression of annulus fibrosus stem cells were studied. The experimental results in this paper show that after planting fibrous stem cells with different elastic modulus, the cell proliferation is obvious after the tensile mechanical stimulation of different conditions, and the different elastic modulus scaffolds are stimulated by the tensile mechanical stimulation of 2% tensile amplitude. The cell morphology is different. The low elastic modulus is round-like, and the high elastic modulus is fusiform-like. After 5% and 12% stretch amplitude, the cells are oriented at different elastic modulus. Arranged, there is no obvious difference in cell morphology.

ANALYSIS OF THE GROWING PROCESS OF SOY BEAN

2020 ◽  
Author(s):  
Ekaterina Statsenko
Keyword(s):  
Mathematical Modeling ◽  
Regression Analysis ◽  
Food Industry ◽  
Modeling Method ◽  
Grain Weight ◽  
Technological Processes ◽  
Established Relationship ◽  
Method Of Correlation

Prediction of technological processes in various sectors of the food industry using mathematical modeling is becoming increasingly important. The paper describes the effect of various factors on the grain weight after germination by the method of correlation-regression analysis. Using this modeling method, based on the established relationship, it is possible to predict the weight of the grain after germination in various conditions.

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