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Anaconda3

Manufactured by Anaconda
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Anaconda3 is a free and open-source distribution of the Python programming language for scientific computing, data science, and machine learning. It includes a curated collection of over 1,500 data science packages and the Conda package and environment management system.

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10 protocols using Anaconda3

We extracted ALFF and GMV from 116 brain regions as clustering features for each PD patient, and conducted dimensionless processing through standardization. To reduce the data dimension and improve the clustering performance of the model, we used principal components analysis (PCA) for dimensionality reduction, and took eigenvalue >1 as the selection criterion for principal components. In hierarchical clustering analysis, each patient was first treated as a separate “cluster” and then merged gradually with other patients into a new cluster. We used the Ward linking method to merge clusters at each step while minimizing the sum of error squares from the cluster mean (Uribe et al., 2016 (link), 2018 (link); Inguanzo et al., 2021 (link)). The results of cluster analysis were shown as a dendrogram (Figure 1). We used the Calinski–Harabasz (C–H) Index to assess the optimal number of clusters. This was determined by between-cluster and within-cluster variance, and the larger the value, the better was the cluster solution. We implemented the operations stated above using the “sklearn” package on the Anaconda3 platform (www.anaconda.com).
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The core AutoLC algorithms were written in
Python 3.9 using PyCharm 2021.1.2 (JetBrains, Prague, Czech Republic).
The Python environment was set up using Anaconda 3 (Anaconda Inc.,
Austin, TX, USA). To interface with the LC instrument, an algorithm
was written in C++ using Microsoft Visual Studio 2022 (Microsoft,
Redmond, WA, USA) to interface with the OpenLAB CDS Chemstation Edition
(rev. C.01.10 [287]). For retention modeling, the AutoLC algorithm
and signal processing was done in Python 3.9 using PyCharm 2021.1.2
and MATLAB 2021b (Mathworks, Natick, MA, USA), which was used for
the peak detection, tracking, and optimization algorithms, whereas
peak detection was supported by the findpeaks MATLAB function.31 To monitor progress, the AutoLC algorithm was
programmed to report its status and data continuously in Slack 4.22
(San Francisco, CA, USA) using the Python Slack SDK.
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Unless otherwise stated, all analyses were performed on a Windows machine running Python 3.8 via Anaconda3 using custom scripts. Data were handled in pandas dataframes (V 1.1.3), for numerical computing numpy library (V 1.20.1) was used, and linear regression and multiple testing correction were performed via the sklearn and statsmodels (V 0.23.0 and V 0.12.0, respectively) implementations. For statistical testing, scipy (V 1.5.2) implementations were used. APIs were queried via requests (V 2.22.0), and KEGG via Biopython (V 1.76). BRENDA database was queried via SOAP access (https://www.brenda-enzymes.org/soap.php).
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Image processing, including spherical aberration correction, drift correction, background subtraction, and extraction of fluorescence intensity in chambers, was conducted using ImageJ/Fiji29 (link). ImageJ plugin, Template Matching and Slice Alignment (https://sites.google.com/site/qingzongtseng/template-matching-ij-plugin#credit) were used for the drift correction. All of the processes were automated, using a macro program. A series of the extracted intensity data were analyzed, using a program written in Python, with Anaconda3 (https://www.anaconda.com/).
LOD values were defined as follows: Output values obtained with different concentrations of tgRNA were fitted to a linear curve (the output values correspond to the number of positive chambers and the fluorescence intensities in SATORI and the plate reader-based method, respectively). The means + 3 S.D. for output values obtained without tgRNA were measured, and the crossing point of the linear curve and the mean + 3 S.D. value was determined. The concentration corresponding to the crossing point was defined as the LOD value.
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No statistical methods were used to predetermine sample size. The experiments were not randomized. Data are not pre‐processed unless explicitly declared. The summary of statistics used in this study was shown in Table S12, Supporting Information. Data analysis and visualization were mostly performed in Anaconda3 (conda 4.10.3, https://www.anaconda.com/) using python3 language. Some standard python extension packages were used for these works, which included numpy, pandas, scipy, math, matplotlib, seaborn, re, random, pickle, Levenshtein, and pylab.
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As a machine-learning technique, random forest regression (RF) was employed to identify the factors that influence the variation in SOC content among producer fields. In this study, the importance of independent variables was quantified by RF with Anaconda 3. The correlations between soil physicochemical properties, soil texture, and climatic factors were analyzed using RF at regional and local scales. Factors with high importance to SOC content were selected for the next step. Only factors with relative importance above or around 10% were selected. The Principal Component Analysis (PCA) was conducted in SPSS, while random forests were constructed using Anaconda 3. ANOVA and LSD test were used to determine if soil physicochemical properties were statistically different among the three land covers and the statistical significance was checked using Duncan’s test at p = 0.05 via SPSS.
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The experimental data were organized and analyzed using PyCharm 2020, Anaconda 3, and Tensorflow 2.1. The regression equation were analyzed and plotted using GraphPad 8.4.
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The basic structure of the ANN‐PK model is presented in Figure 1. The clinical data and background of patients were used for the model input, and the output by ANN was the clearance (CL). The estimated CL value from the ANN, the previously reported values of a volume of distribution, and an absorption rate constant were substituted into the one‐compartment with one‐order absorption model, and predictions of drug concentrations were calculated. Differences between the predictions and observed values were calculated, and the weights in the ANN were updated by the back‐propagation method. Mean squared errors (MSEs) were used as the loss function (Equation 1), and Adam was used for the parameter optimization.25 Minibatch learning was performed in each training. Training and evaluating the ANN‐PK model were performed using Python version 3.7.9 with Anaconda 3 version 4.8.5 (Anaconda, Inc.). PyTorch version 1.7.0 (https://pytorch.org/) was used as the framework for deep learning. MSE=1ni=1nyi^yi2
n represents the number of training data items. yi^ and yi represent the predicted and observed drug concentrations of ith data, respectively.
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Demographics of the patient population are presented as frequencies and percentages. All continuous variables are presented with medians and interquartile ranges (IQR). Mann-Whitney U test was used to test whether there were significant differences between patients admitted to ITU vs ward for ROTEM and calprotectin. We included individuals in the ITU cohort if they were admitted to the high dependency unit (HDU) or intensive treatment unit (ITU) at any point during admission (n = 13). The ward cohort represents the remaining individuals in the study (n = 20). Spearman’s rank correlation coefficients were calculated to assess associations between variables. A Bonferroni correction was used to evaluate significance with both tests. A p-value below 0.05 at a 95% confidence interval was considered significant. Analysis was performed in Anaconda 3 with Python 3.8.8.
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According to the sample classification of the normal group and RA group, we used the RFECV function in the sklearn package (version 0.24.2) and adopted three different machine learning algorithms, namely least absolute shrinkage and selection operator (lasso) regression, random forest (RF) classifier, and linear support vector machine (SVM) classification, to perform feature extraction on the gene expression data of RA samples to screen out the key genes based on classification features. The gene sets screened out by these three different machine learning classifiers were intersected, and the characteristic genes common to more than two classifiers were selected as the key RA gene set based on the feature extraction method.
The key RA gene sets based on PPI network analysis and feature extraction were intersected again to obtain the core gene set of RA. To verify the disease classification effect of these core genes, the three different machine learning classifiers mentioned above were used to predict RA classification, which was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) value. All classification prediction analyses were performed on the Anaconda3 (https://www.anaconda.com/).
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