scholarly journals Possibilities of R programming language in simulating microbiological synthesis processes

2021 ◽  
Vol 2090 (1) ◽  
pp. 012002
Author(s):  
Marina A. Nikitina ◽  
Irina M. Chernukha
Keyword(s):  
Differential Equations ◽  
Programming Languages ◽  
Programming Language ◽  
Information Technologies ◽  
Growth Patterns ◽  
Substrate Affinity ◽  
Stoichiometric Ratio ◽  
Microbiological Synthesis ◽  
R Programming Language ◽  
R Programming

Abstract Information technologies of biotechnological processes are based on the use of mathematical models to describe microbiological synthesis. Application of digital technologies in analysis of microbial growth patterns is mainly determined by the ability of modern programming languages to numerically integrate systems of differential equations describing the development of the microbial process in time. In Jupyter Notebook environment in the R programming language, the solution of the kinetic growth model of the E.coli microbial population was shown. Two solution methods were used - the one-step Runge-Kutta method of the fourth order of accuracy and the universal solver ODE (General Solver for Ordinary Differential Equations). Initial data of the problem in question: K s S 0 = 2 (Ks is substrate affinity S 0 constant for the biomass (microorganism), S0 is initial concentration of substrate); replicating cells m a0 = 0.01; total number of cells m 0 = 0.05; stoichiometric ratio Ys = 0.5; various ratios 1) 1 ) λ μ m = 0.0357 ; 2 ) λ μ m = 0.0714 ; 3 ) λ μ m = 0.1071 ; 4 ) λ μ m = 0.1428 ; 5 ) λ μ m = 0.2142 (λ is specific growth rate of dividing cells, μm is inactivation rate constant). As a result, the simulation and verification of microbial biomass growth process - its visual representation in the form of tabular and graphical data were carried out. In the process of simulation of E.coli growth the following peculiarity was revealed. In addition to cell division, a fairly intensive loss of their ability to divide occurs. This process is supposedly determinant in population development and limits the growth and ultimate density of the culture. Thus, information technology will help the researcher not only in studying the process, establishing patterns and predicting results, but also in making reasoned decisions.

SciKit-GStat: A scipy flavored geostatistical analysis toolbox written in Python

2020 ◽  
Author(s):  
Mirko Mälicke
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Building Blocks ◽  
Geostatistical Analysis ◽  
Geostatistical Methods ◽  
R Programming Language ◽  
The Past ◽  
Spatio Temporal ◽  
R Programming ◽  
Python Programming

<p><span>Geostatistical and spatio-temporal methods and applications have made major advances during the past decades. New data sources became available and more powerful and available computer systems fostered the development of more sophisticated analysis frameworks. However, the building blocks for these developments, geostatistical packages available in a multitude of programming languages, have not experienced the same attention. Although there are some examples, like the gstat package available for the R programming language, that are used as a de-facto standard for geostatistical analysis, many languages are still missing such implementations. During the past decade, the Python programming language has gained a lot of visibility and became an integral part of many geoscientist’s tool belts. Unfortunately, Python is missing a standard library for geostatistics. This leads to a new technical implementation of geostatistical methods with almost any new publication that uses Python. Thus, reproducing results and reusing codes is often cumbersome and can be error-prone.</span></p><p><span>During the past three years I developed scikit-gstat, a scipy flavored geostatistical toolbox written in Python to tackle these challenges. Scipy flavored means, that it uses classes, interfaces and implementation rules from the very popular scipy package for scientific Python, to make scikit-gstat fit into existing analysis workflows as seamlessly as possible. Scikit-gstat is open source and hosted on Github. It is well documented and well covered by unit tests. The tutorials made available along with the code are styled as lecture notes and </span><span>are</span><span> open </span><span>to everyone</span><span>. The package is extensible, to make it as easy as possible for other researchers to build new models on top, even without experience in Python. Additionally, scikit-gstat has an interface to the scikit-learn package, which makes it usable in existing data analysis workflows that involve machine learning. During the development of scikit-gstat a few other geostatistical packages evolved, namely pykrige for Kriging and gstools mainly for geostatistical simulations and random field generations. Due to overlap and to reduce development efforts, the author has made effort to implement interfaces to these libraries. This way, scikit-gstat understands other developments not as competing solutions, but as parts of an evolving geostatistical framework in Python that should be more streamlined in the future.</span></p>

scholarly journals Developing codes for validation of PM10, PM2.5, and O3 datasets using R programming language

2019 ◽  
Author(s):  
Ramin Nabizadeh ◽  
Mostafa Hadei
Keyword(s):  
Air Pollution ◽  
Programming Language ◽  
Assessment Method ◽  
Daily Maximum ◽  
Data Handling ◽  
R Programming Language ◽  
Wide Range ◽  
Us Epa ◽  
R Programming ◽  
Pm 10

Introduction: The wide range of studies on air pollution requires accurate and reliable datasets. However, due to many reasons, the measured concentra-tions may be incomplete or biased. The development of an easy-to-use and reproducible exposure assessment method is required for researchers. There-fore, in this article, we describe and present a series of codes written in R Programming Language for data handling, validating and averaging of PM10, PM2.5, and O3 datasets.   Findings: These codes can be used in any types of air pollution studies that seek for PM and ozone concentrations that are indicator of real concentra-tions. We used and combined criteria from several guidelines proposed by US EPA and APHEKOM project to obtain an acceptable methodology. Separate   .csv files for PM 10, PM 2.5 and O3 should be prepared as input file. After the file was imported to the R Programming software, first, negative and zero values of concentrations within all the dataset will be removed. Then, only monitors will be selected that have at least 75% of hourly concentrations. Then, 24-h averages and daily maximum of 8-h moving averages will be calculated for PM and ozone, respectively. For output, the codes create two different sets of data. One contains the hourly concentrations of the interest pollutant (PM10, PM2.5, or O3) in valid stations and their average at city level. Another is the   final 24-h averages of city for PM10 and PM2.5 or the final daily maximum 8-h averages of city for O3. Conclusion: These validated codes use a reliable and valid methodology, and eliminate the possibility of wrong or mistaken data handling and averaging. The use of these codes are free and without any limitation, only after the cita-tion to this article.

scholarly journals Spatial variation of physicochemical parameters in a constructed wetland for wastewater treatment: An example of the use of the R programming language

2021 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Junior Pastor Pérez-Molina ◽  
Carola Scholz ◽  
Roy Pérez-Salazar ◽  
Carolina Alfaro-Chinchilla ◽  
Ana Abarca Méndez ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Spatial Variation ◽  
Water Flow ◽  
Programming Language ◽  
Constructed Wetland ◽  
Physicochemical Parameters ◽  
Preferential Flow ◽  
Oxygen Demand ◽  
R Programming Language ◽  
R Programming

Introduction: The implementation of wastewater treatment systems such as constructed wetlands has a growing interest in the last decade due to its low cost and high effectiveness in treating industrial and residential wastewater. Objective: To evaluate the spatial variation of physicochemical parameters in a constructed wetland system of sub-superficial flow of Pennisetum alopecuroides (Pennisetum) and a Control (unplanted). The purpose is to provide an analysis of spatial dynamic of physicochemical parameters using R programming language. Methods: Each of the cells (Pennisetum and Control) had 12 piezometers, organized in three columns and four rows with a separation distance of 3,25m and 4,35m, respectively. The turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), ammoniacal nitrogen (N-NH4), organic nitrogen (N-org.) and phosphorous (P-PO4-3) were measured in water under in-flow and out-flow of both conditions Control and Pennisetum (n= 8). Additionally, the oxidation-reduction potential (ORP), dissolved oxygen (DO), conductivity, pH and water temperature, were measured (n= 167) in the piezometers. Results: No statistically significant differences between cells for TKN, N-NH4, conductivity, turbidity, BOD, and COD were found; but both Control and Pennisetum cells showed a significant reduction in these parameters (P<0,05). Overall, TKN and N-NH4 removal were from 65,8 to 84,1% and 67,5 to 90,8%, respectively; and decrease in turbidity, conductivity, BOD, and COD, were between 95,1-95,4%; 15-22,4%; 65,2-77,9% and 57,4-60,3% respectively. Both cells showed ORP increasing gradient along the water-flow direction, contrary to conductivity (p<0,05). However, OD, pH and temperature were inconsistent in the direction of the water flow in both cells. Conclusions: Pennisetum demonstrated pollutant removal efficiency, but presented results similar to the control cells, therefore, remains unclear if it is a superior option or not. Spatial variation analysis did not reflect any obstruction of flow along the CWs; but some preferential flow paths can be distinguished. An open-source repository of R was provided. 

scholarly journals On the use of R programming language in the analyses of spatial data

2013 ◽  
Vol 102 ◽  
pp. 55-62
Author(s):  
Milan Kobal ◽  
Andrej Ceglar ◽  
Klemen Eler ◽  
Barbara Medved-Cvikl ◽  
Luka Honzak ◽  
...  
Keyword(s):  
Programming Language ◽  
Spatial Data ◽  
R Programming Language ◽  
R Programming

scholarly journals Bioinformatic approaches for analysis of coral-associated bacteria using R programming language

2021 ◽  
Vol 18 (4) ◽  
pp. 733-743
Author(s):  
Doan Thi Nhung ◽  
Bui Van Ngoc
Keyword(s):  
Programming Language ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Metagenomic Data ◽  
Rrna Gene ◽  
Marine Microorganisms ◽  
Taxonomic Assignment ◽  
Associated Bacteria ◽  
R Programming Language ◽  
R Programming

Recent advances in metagenomics and bioinformatics allow the robust analysis of the composition and abundance of microbial communities, functional genes, and their metabolic pathways. So far, there has been a variety of computational/statistical tools or software for analyzing microbiome, the common problems that occurred in its implementation are, however, the lack of synchronization and compatibility of output/input data formats between such software. To overcome these challenges, in this study context, we aim to apply the DADA2 pipeline (written in R programming language) instead of using a set of different bioinformatics tools to create our own workflow for microbial community analysis in a continuous and synchronous manner. For the first effort, we tried to investigate the composition and abundance of coral-associated bacteria using their 16S rRNA gene amplicon sequences. The workflow or framework includes the following steps: data processing, sequence clustering, taxonomic assignment, and data visualization. Moreover, we also like to catch readers’ attention to the information about bacterial communities living in the ocean as most marine microorganisms are unculturable, especially residing in coral reefs, namely, bacteria are associated with the coral Acropora tenuis in this case. The outcomes obtained in this study suggest that the DADA2 pipeline written in R programming language is one of the potential bioinformatics approaches in the context of microbiome analysis other than using various software. Besides, our modifications for the workflow execution help researchers to illustrate metagenomic data more easily and systematically, elucidate the composition, abundance, diversity, and relationship between microorganism communities as well as to develop other bioinformatic tools more effectively.

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