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392 protocols using excel 2010

1

Cellulose and Lignin Content Quantification

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Cellulose and lignin contents of cell walls of the WT and the brittle stem mutant were measured according to Jin et al. [28 (link)]. The rice sample was ground into fine powder in liquid nitrogen, and then 35 μL of 0.01% sodium azide (NaN3), 35 μL Amylase solution, 17 μL branched chain amylase solution was used to remove starch. Then fully dried to obtain isolated cell wall samples. The cellulose in the cell wall sample was digested and decomposed into glucose, and its absorbance value at 625 nm was measured. Draw the standard curve according to the absorbance value reading of glucose concentration standard solution, and calculate the content of glucose (i.e., crystalline cellulose) by comparing the absorbance of the sample with the standard curve. Similarly, the lignin in the cell wall sample was digested and decomposed, and the absorbance of the decomposed solution at 280 nm was measured. Draw the standard curve according to the absorbance value reading of the standard solution, and calculate the lignin content by comparing the absorbance of the sample with the standard curve. All the data were analyzed using Excel 2010 and SPSS 26.0. The data were analyzed using Excel 2010 and SPSS 26.0.
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2

Data Analysis of Biological Samples

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Microsoft Excel 2010 was used to summarize data and SPSS16.0 software was used to analyze statistical significance. Data were expressed as means ± standard deviation (SD). One-way ANOVA were used to analyze the normality followed by Duncan’s multiple range test to obtain the statistical significance between groups. Alphabet indicated that data showed significant difference at P < 0.05 level.
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3

Urinalysis Methods for Urinary Schistosomiasis

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Data obtained from the survey was entered into Microsoft Excel 2010 and analysed with SPSS version 20.0. Data summary was done with descriptive statistics. Comparison between the performances of urine filtration methods and urinalysis reagent strip to diagnose urinary schistosomiasis was assessed by determining sensitivity, specificity, positive and negative predictive values. Chi square, bivariate and multivariate logistic regression and Pearson correlation test were used to measure associations between urogenital schistosomiasis and haematuria. Odds ratio was used to measure strengths of association between variables. Agreement between the urine filtration method and urinalysis reagent strip was determined using Kappa statistics. Kappa value were interpreted as poor (<0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80) and perfect (0.81–1.00) [35 (link)]. Statistical significance was considered at 95% confidence level (CL). P-value of less than 0.05 was considered significant during the analysis.
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4

Soil Heavy Metals Characterization

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Descriptive statistics, such as mean, standard deviation, median, maximum, minimum and coefficient of variation were used to characterize the contents of HMs in soil samples. Microsoft Excel 2010 and SPSS 26 and Origin 2019b were used to process the experimental data, perform for correlation analysis of HMs concentrations, and test significance analysis for soils properties and biomarker responses at P < 0.05 and P < 0.01 levels. Adobe Origin 2019b was also taken to draw graphics.
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5

Evaluating GDMT Utilization and Dosing

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Patient data relevant to the study was obtained from the electronic medical record (EMR) during three time points, i.e., first month, sixth month, and twelfth month after the first visit, and the collected data were then evaluated between the study groups to assess the usage of GDMT. Attainment of evidence-based target doses of disease-modifying drugs (ACEI’s, ARB’s, beta-blockers, and MRA’s) and time to reach target dose were also evaluated. The patients were categorized to four groups based on the percentage of target dose achieved as group 1 (0–25%), group 2 (26–50%), group 3 (51–75%), and group 4 (76–100%). Data storage and analysis were performed using Microsoft Excel 2010 and SPSS Version 24. For the comparison of continuous variable, we used independent sample t test and paired t test, and the categorical variables were compared using χ2-likelihood ratio test. All the p values were two-tailed, and a significance level of 5% was used.
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6

Genetic Risk Factors for Sepsis

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Statistical analysis was carried out using Microsoft® Excel 2010 and the “Statistical Package for the Social Sciences (SPSS) for windows” software, version 24. Odds ratios (OR) with a 95% confidence interval (CI) were calculated. Descriptive statistics were expressed as percentages for qualitative variables and mean ± standard deviation (SD) for quantitative variables. Testing differences between septic patients and no septic patients were performed using Student’s t-test, Chi-square (χ2) test, or Fisher’s exact tests. p-value was considered statistically significant below 0.05. The Hardy–Weinberg equilibrium (HWE) was calculated by the Online Encyclopedia for Genetic Epidemiology (OEGE) software (http://www.oege.org/software/hwe-mrcalc.shtml (accessed on 10 March 2019)). The relationship between the risk factors including our polymorphisms and the development of sepsis was further determined using logistic regression after adjustment of factors. Survival analysis was performed as well. Log-rank, Breslow, and Tarone–Ware tests were used to find Kaplan–Meier estimates for survival. Cox regression analysis was applied to the data to determine if any of the variables were independently associated with the duration of survival.
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7

Statistical Analysis of Experimental Data

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Data plotting and statistical analysis were performed in Microsoft Excel 2010 and SPSS11.5. Data are given as mean ± S.E.M. Data groups were compared applying Students unpaired t-test for two groups or one-way ANOVA analysis with Dunnett's or Bonferroni's multiple comparison post-test for more than two groups. Differences were considered statistically significant when P < 0.05.
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8

Evaluating Zinc and Arsenic Levels in Rice

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Statistical analysis was performed using Microsoft Excel 2010 and SPSS-19.0 software. The statistical difference among control and treatments was determined using one-way ANOVA multiple comparison. Data were compared by Duncan’s test. Differences were considered significant at p ≤ 0.05. Pearson correlation analysis was performed by SPSS to quantify the relationships between Zn concentration and As concentration in rice.
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9

Multivariate Analysis of Toxic Metals in Soil

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Multivariate statistical techniques such as principal component analysis (PCA), cluster analysis (CA), and Pearson's correlation matrix (PCM) were used to determine the most likely sources of toxic harmful metals in the soil. To explain the study findings, PCA was performed using the mean value of the individual variable, whereas each principal component's loading value > 0.5 and eigenvalue > 1 were taken from the analysis table. The Ward technique, Euclidean distance, and standardized data sets (Z-cores) were employed for CA. The dendrogram, which is graphically displayed, demonstrates how each examined toxic metal is connected to a cluster. This study analyzed data using Microsoft Excel-2010, SPSS (V.20), and ArcGIS (V.10.5) software.
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10

ANOVA Analysis of Experimental Data

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All data were expressed as mean ± standard deviation (SD). Data were arranged with Microsoft Excel 2010 and analyzed with SPSS 18.0 by one-way ANOVA. A p-value of < 0.05 was considered as statistically significant.
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