scholarly journals Pairwise efficiency: a new mathematical approach to qPCR data analysis increases the precision of the calibration curve assay

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yulia Panina ◽  
Arno Germond ◽  
Brit G. David ◽  
Tomonobu M. Watanabe
Keyword(s):  
Data Analysis ◽  
Calibration Curve ◽  
Mathematical Approach ◽  
Qpcr Data

scholarly journals Pairwise Efficiency: A new mathematical approach to qPCR data analysis increases the precision of the classical calibration curve assay

2018 ◽  
Author(s):  
Yulia Panina ◽  
Arno Germond ◽  
Brit G. David ◽  
Tomonobu M. Watanabe ◽  
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Calibration Curve ◽  
Quantitative Polymerase Chain Reaction ◽  
Expression Ratio ◽  
Qpcr Data ◽  
Novel Approach ◽  
Data Points ◽  
Polymerase Chain ◽  
New Formula

ABSTRACTThe real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters that needs to be determined, reported, and incorporated into data analysis in any qPCR experiment. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines recognize the calibration curve as the method of choice for estimation of qPCR efficiency. The precision of this method has been reported to be between SD=0.007 (3 replicates) and SD=0.022 (no replicates). In this manuscript we present a novel approach to analysing qPCR data obtained by running a dilution series. Unlike previously developed methods, our method relies on a new formula that describes pairwise relationships between data points on separate amplification curves and thus operates extensive statistics (hundreds of estimations). The comparison of our method with classical calibration curve by Monte Carlo simulation shows that our approach can almost double the precision of efficiency and gene expression ratio estimations on the same dataset.

scholarly journals qPCR data analysis: Better results through iconoclasm

2019 ◽  
Vol 17 ◽  
pp. 100084 ◽  
Author(s):  
Joel Tellinghuisen ◽  
Andrej-Nikolai Spiess
Keyword(s):  
Data Analysis ◽  
Qpcr Data

Mathematical approach to data analysis in environmental science: The lecture of Seveso

Chemosphere ◽  
1989 ◽  
Vol 18 (1-6) ◽  
pp. 855-860 ◽  
Author(s):  
S.P. Ratti ◽  
G. Belli ◽  
S. Cerlesi
Keyword(s):  
Data Analysis ◽  
Environmental Science ◽  
Mathematical Approach

scholarly journals PENINGKATAN KOMPETENSI REPRESENTASI MATEMATIKA SISWA MELAUI PENERAPAN PEMBELAJARAN MATEMATIKA REALISTIK

2018 ◽  
Author(s):  
UNIMED SEMNAS ◽  
Hernita Permata Sari ◽  
budi halomoan
Keyword(s):  
Data Analysis ◽  
Action Research ◽  
Simple Form ◽  
Mathematical Representation ◽  
Mathematical Approach ◽  
Meaningful Information ◽  
Analysis Technique ◽  
Success Indicators ◽  
Three Stages ◽  
Academic Year

ABSTRAKPenelitian ini dilakukan pada siswa kelas IX SMP Sultan Iskandar Muda T.A 2017/2018 yang berjumlah 39 orang. Penelitian ini bertujuan untuk meningkatkan kemampuan representasi matematika siswa dengan penerapan pembelajaran matematika realistik pada pokok bahasan kekongruenan dan kesebangunan. Jenis penelitian ini adalah penelitian tindakan kelas (PTK), yang terdiri dari dua siklus. Data diperoleh melalui interview, observasi, dan tes tertulis. Teknik analisis data dilakukan melalui tiga tahap, yaitu reduksi data, paparan data, dan penarikan kesimpulan. Data yang diperoleh direduksi dengan mengelompokkan kemudian mengorganisasikannya sehingga diperoleh informasi yang bermakna. Setelah direduksi, kemudian data dipaparkan secara sederhana dalam bentuk paparan naratif, grafik, dan tabel, yang bertujuan untuk menggambarkan secara jelas mengenai proses dan hasil tindakan. Paparan informasi yang didapat kemudian dibandingkan dengan indikator-indikator keberhasilan yang digunakan dan selanjutnya dilakukan penarikan kesimpulan. Hasil penelitian ini menunjukkan terjadinya peningkatan kemampuan representasi matematika siswa setelah menerapkan pendekatan pembelajaran matematika realistik pada materi kekongruenan dan kesebangunan.Kata kunci:pendekatan matematika realistik, PTK, kekongruenan dan kesebangunan.ABSTRACTThis research was conducted on 39 students of class IX SMP Sultan Iskandar Muda in academic year 2017/2018. This research aims to improve the ability of mathematical representation by applying realistic mathematical approach to the material of congruence and kesebangunan. This type of research is a classroom action research (PTK), which consists of two cycles. Data obtained through interviews, observations, and written tests. Data analysis technique is done through three stages, namely: reducing data, exposing data, and drawing conclusions. The data obtained is reduced by grouping and then organizing it to produce meaningful information. Furthermore, the data is presented in a simple form of narrative, graphics, and tables.The information obtained is then compared with the success indicators used and then the conclusion is drawn. The results of this study indicate an increase in the ability of students' mathematical representation after applying realistic mathematical approach to the material of congruence and kesebangunan.

scholarly journals Characterization of Red Onion (Alium Ascalonicum.L) Drying using Greenhouse (ERK) Dryer

2017 ◽  
Vol 5 (1) ◽  
pp. 330-338
Author(s):  
Amalia Islami ◽  
Murad ◽  
Asih Priyati
Keyword(s):  
Data Analysis ◽  
Moisture Content ◽  
Greenhouse Effect ◽  
Average Moisture Content ◽  
Drying Rate ◽  
Mathematical Approach ◽  
Weight Changes ◽  
Microsoft Excel ◽  
Sample Weight

Aim of this research was to determine drying rate and moisture content changes on Greenhouse (ERK) Dryer. Result was showed using graphic by analyzing drying rate characteristics of Greenhouse Dryer. Data analysis was performed using mathematical approach that solved using Microsoft Excel. Method used in this research was experimental method. Based on the results, onion drying using ERK showed decreasing rate of weight changes. Sample shrinkage was 25.7% with average moisture content 60.06% for sample weight 0.187 kg. Average humidity (RH) was lower than ambient humidity on the range of 63.4% to 83.0%. Characteristics of onion drying was the decreasing rate of moisture content of 0.17% with equation MR: y = -0,017 + 1,061, R2 is 0,985. Value of Ln MR at first day was y = -0,019x and decreasing rate of moisture content 0,19%. Whereas decreasing rate at second, third and fourth day were 0,008%, 0,11% and 0,002% respectively for 1 hour interval period. Keywords: red onion, Greenhouse effect, drying rate, dryer   ABSTRAK   Tujuan penelitian ini untuk mengetahui laju pengeringan, penurunan kadar air pada alat pengering ERK. Data hasil penelitian ditampilkan dalam bentuk grafik dengan menganalisis karakteristik laju pengeringan alat pengering ERK. Analisis data dilakukan dengan pendekatan matematis untuk menyelesaikan model perhitungan matematik yang diolah dengan program komputer Microsoft Excel. Metode yang digunakan pada penelitian ini adalah eksperimental dengan melakukan penelitian langsung di lapangan. Hasil penelitian menunjukkan bahwa pengeringan bawang merah menggunakan alat ERK memiliki laju pengeringan menurun dengan dilihat dari perubahan berat yang semakin berkurang. Penyusutan bahan terjadi 25,7% dengan kadar air rata-rata 60,06% dan berat bahan 0,187 kg. Kelembaban ruang pengering rata-rata 63,4%, lebih kecil dari  kelembaban  lingkungan sebesar 83,0%. Bawang merah memiliki karakteristik pengeringan dengan penurunan kadar air 0,17% dan MR: y = -0,017 + 1,061 dengan nilai determinasi R2 = 0,985. Ln MR pada hari pertama terlihat y = -0,019x dengan penurunan kadar air sebesar 0,19%. Sedangkan penurunan kadar air pada hari kedua mencapai 0,008%, hari  ketiga 0,11%, dan hari keempat 0,002% pada interval waktu 1 jam. Kata kunci: bawang merah, ERK, laju pengeringan, pengeringan

Quantitative PCR (qPCR) Data Analysis of SOD and its Activity and Expression in Elettaria cardamomum under Biotic Stress

2014 ◽  
Vol 11 (2) ◽  
pp. 435-440
Author(s):  
Susan George ◽  
S. Bhasker ◽  
R. Mamkulathil Devasia ◽  
H. Madhav Warrier ◽  
Mohankumar Chinnamma
Keyword(s):  
Data Analysis ◽  
Quantitative Pcr ◽  
Biotic Stress ◽  
Qpcr Data ◽  
Elettaria Cardamomum

scholarly journals Effect of water intake and storage time on protein concentration and enzyme AChE activity in erythrocyte and plasma blood samples of healthy individuals

2020 ◽  
Author(s):  
Snežana Jovičić
Keyword(s):  
Data Analysis ◽  
Water Intake ◽  
Calibration Curve ◽  
Ache Activity ◽  
Protein Concentration ◽  
Storage Time ◽  
Statistical Tests ◽  
Healthy Individuals ◽  
Efficient Inhibitor ◽  
And Storage

AbstractBackgroundStorage time influence concentration levels of blood biomarker. This study aimed to assess the effect of water intake prior sampling and storage time on protein concentration, enzyme AChE activity, inhibitor efficacy and to build an efficient inhibitor calibration curve in healthy individuals.MethodsData analysis was performed on 11 participants. Study utilizes substrate acetylcholine chloride and inhibitors BW284c51 (0.01mM) and GUK-987 (0.1mM). Calibration curve ranging from 10-1 to 10-38 mM was build for inhibitor GUK-987 and GDK-510.Data analysis is carried out with Microsoft Excel 2007. Data analysis was performed via IBM SPSS Statistical Software v23.0. Descriptive statistics and parametric statistical tests were assessed for 0, 90, 91 and 92 days of storage in Plasma and Erythrocyte samples. Workflow of building calibration curve and the most efficient inhibition concentration is assessed.ResultsWater intake and storage time have effect on in vitro protein concentration, activation/inhibition of enzyme AChE activity in Plasma and Erythrocyte samples. However, 100% inhibitor efficacy is maintained for inhibitor GUK-987 in Plasma samples and inhibitor BW284c51 in Erythrocyte samples. The most efficient inhibitor concentration is determined.ConclutionSignificant changes and variable association have been estimated between protein concentration, activation/inhibition of enzyme AChE activity, as a cause of water intake and storage time. Taking all these factors into account for further research is important for disease prevention and human wellbeing.

scholarly journals Use and Misuse of Cq in qPCR Data Analysis and Reporting

Life ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 496
Author(s):  
Adrián Ruiz-Villalba ◽  
Jan M. Ruijter ◽  
Maurice J. B. van den Hoff
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Concentration Ratio ◽  
Amplification Curve ◽  
Qpcr Data ◽  
Pcr Efficiency ◽  
Qpcr Analysis ◽  
Sampling Variation ◽  
The Difference ◽  
Threshold Setting

In the analysis of quantitative PCR (qPCR) data, the quantification cycle (Cq) indicates the position of the amplification curve with respect to the cycle axis. Because Cq is directly related to the starting concentration of the target, and the difference in Cq values is related to the starting concentration ratio, the only results of qPCR analysis reported are often Cq, ΔCq or ΔΔCq values. However, reporting of Cq values ignores the fact that Cq values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, Cq values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported Cq values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in Cq values and discusses the limits to the interpretation of observed Cq values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.

scholarly journals Groundbreaking predictions about COVID-19 pandemic duration, number of infected and dead: A novel mathematical approach never used in epidemiology

2020 ◽  
Author(s):  
Javier Garcia Garcia de Alcaniz ◽  
Victoria Lopez-Rodas ◽  
Eduardo Costas
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Mortality Rate ◽  
Big Data Analysis ◽  
Mathematical Approach ◽  
Great Uncertainty ◽  
R Value ◽  
Complex Phenomena ◽  
New Procedures ◽  
Lack Of Knowledge

Hundreds of predictions about the duration of the pandemic and the number of infected and dead have been carried out using traditional epidemiological tools (i.e. SIR, SIRD models, etc.) or new procedures of big-data analysis. However, the extraordinary complexity of the disease and the lack of knowledge about the pandemic (i.e. R value, mortality rate, etc.) create uncertainty about the accuracy of these estimates. However, several elegant mathematical approaches, based on physics and probability principles, like the Delta-t argument, Lindy's Law or the Doomsday principle-Carter's catastrophe, which have been successfully applied by scientists to unravel complex phenomena characterized by their great uncertainty (i.e. Human race's longevity; How many more humans will be born before extinction) allow predicting parameters of the Covid-19 pandemic. These models predict that the COVID-19 pandemic will hit us until at least September-October 2021, but will likely last until January-September 2022, causing a minimum of 36,000,000 infected and most likely 60,000,000, as well as 1,400,000 dead at best and most likely 2,333,000.

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