dataesr/scientific-paragraphs-categorization huggingface dataset:
Maximize: ( P, BW) = f (α, β, R load ) Constraints 1 ≤ α ≤ 1. 1 0 < β ≤ 3% R c < R load Where R c stands for the critical load resistance used to tune the nonlinearity level. It has been obtained from experiments performed in  [41]  and it is equal to 15 Ω. The load resistances used in the following optimization procedures should be higher than R c to guarantee a nonlinear behavior of the device. The simulations performed generate the frequency responses of all the candidate solutions using the ode45 function of MATLAB. While assuming that the bifurcation point of the nonlinear response curves coincides with the maximum value of the frequency response, the bandwidth and the average harvested power are calculated. After running the multiobjective algorithm convenient to this problem, the following Pareto front, illustrated in Figure  4 , is generated.
This choice is made in order to have distributed perturbations over the network so that the energy localization is more efficient. During the conducted simulations, this system of equations is solved by the ode45 MATLAB solver. The optimization returns the position of the mass mistuning and the maximum harvested energy of the best configuration. Results indicate that the 5-DOFs system with mistuning of the 2nd and 4th DOFs, illustrated by the equivalent model in Figure  6 , give the maximum harvested energy. Thus, the harvested power will be calculated through this expression in the following study: P = P 2 + P 4 , where P 2 and P 4 are the harvested powers from the DOFs 2 and 4, respectively.
Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https:// auto-q-pcr. com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample.
Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs. Abbreviations CT Cycle threshold qPCR Quantitative polymerase chain reaction iPSC Induced pluripotent stem cells CNVs Copy number variants SNVs Single nucleotide variants DA Dopaminergic NPC Neural precursor cells DANs DA neurons Polymerase chain reaction (PCR) identifies a nucleic acid fragment of interest by increasing its proportion relative to others 1. Initially the technique was primarily used to visualize DNA fragments for cloning 2,3 or genotyping  [4] [5] [6]  , but can now be used to investigate genetic polymorphisms and mutations  7, 8  , copy number variants (CNVs) 9 , single nucleotide variants (SNVs), point mutations, and genetic deletion/duplication events 10.
Also, not all qPCR software provides a statistical output. Analysis of qPCR data is still highly time consuming and error prone, especially when processing large numbers of data points. The user must intervene to include or exclude replicates, which, without guidelines or standardized procedures, can potentially introduce "user-dependent" variation and errors. To both simplify and accelerate this data analysis step for qPCR datasets, we have created a Python-based, open source, user-friendly web application "Auto-qPCR" to process exported qPCR data and to provide summary tables, visual representations of the data, and statistical analysis. The program can be found at the website https:// auto-q-pcr. com/. Furthermore, the program can be installed locally, and then run offline. The program can work with the two commonly used molecular biology approaches: (i) absolute quantification, where all RNA estimations rely on orthogonal projection of the samples of interest onto a calibration curve  19  , and (ii) relative quantification that relies on difference of cycle threshold (CT) values between the gene of interest and endogenous controls  20.
Here we use Auto-qPCR to analyze qPCR datasets and illustrate four distinct computational methods. Overall, Auto-qPCR provides an all-in-one solution for the user, going from datasets to graphs, within one web-based software package. Unlike other software, the intermediate and final results are output by the program, allowing a full review of the data and accurate statistical treatment based on the experimental design. Auto-qPCR was conceived to build logical links between the experimental design and required statistics for differential analyses of each mode, which is rarely found in other qPCR programs. While other open-source qPCR analysis software programs and web apps  [21] [22] [23]  are available, they are only able to normalize, compare and display qPCR data generated with one of the two quantification modes  19, 20.
In contrast, Auto-qPCR provides a comprehensive data analysis package for a wide variety of qPCR experiments. Using the web app does not require prior programming knowledge, account creation or desktop installation. Additionally, the program has been designed to assist the user at each step of the analysis once the exported data files have been collected from the qPCR system. Auto-qPCR can be used to analyse qPCR data in a reproducible manner, simplifying data analysis, avoiding potential human error, and saving time. In this manuscript, we describe some of the uses of the software and outline the steps required, from entering an individual dataset to complete statistical analysis and graphical presentation of the data.
Reverse transcription reactions were performed on 400 ng of total RNA extract to obtain cDNA in a 40 μl total volume containing, 0. 5 μg random primers, 0. 5 mM dNTPs, 0. 01 M DTT and 400 U/µl-MMLV RT (Carlsbad, CA, USA). The reactions were conducted in single plex, in a 10 µl total volume containing 2 × TaqMan Fast Advanced Master Mix, 20 × TaqMan primers/ probe set (Thermo Fisher Scientific), 1 µl of diluted cDNA and RNAse-free H 2 O. Real-time PCR (RT-PCR) were performed on a QuantStudio 3 or QuantStudio 5 machines (Thermo Fisher Scientific). Primers/probe sets from Applied Biosystems were selected from the Thermo Fisher Scientific web site. Two endogenous controls (betaactin and GAPDH) were used for normalization (Table  S2 ). Data generated from the QuantStudio machine were extracted using QuantStudio design and analysis software, either (i) as Excel files (*.
xls or *. xlsx extensions) and the results tab was saved as a 'comma delimited' csv file or (ii) extracted as a txt file that only contained the result tab. Excel files should be carefully used since gene names (notably those whose numbers can be recognized as potential dates) could be modified by automatic changes in cell formatting  29. We suggest exporting data in txt or csv file format. Collection of external data set. An external qPCR data set was provided from an earlier published study  30  , which quantified levels of Nrxns and Nlgn transcripts in the subcortical areas of the brains from mice submitted to conditioned place preference (CPP) with cocaine. Briefly, subcortical areas (subthalamic nucleus, globus pallidum and substantia nigra) of sectioned mouse brains were isolated by laser capture microdissection.
RNA was extracted with the Arcturus PicoPure kit and reverse transcription performed as above. The qPCR experiments were performed according to an absolute quantification design on the Opticon 2 PCR machine (Bio-Rad). Β2Microglobulin (B2M) was used as endogenous control. Data were re-extracted from the Opticon Monitor 2 files as csv files and analyzed by Auto-qPCR. Program development and structure. The program was written in Python 3 using Pandas and NumPy. A main script calls the selected model script (absolute. py, relative. py and stability. py), which processes the data and then calls the statistical functions script (if selected) and the plotting function script. The graphical user interface (GUI) was created using Flask, a package for integrating HTML and Python code. The GUI is written in JavaScript, CSS, HTML and Bootstrap4, a framework for building responsive websites.
Our GitHub repository (https:// github. com/ neuro eddu/ Auto-qPCR) includes all Python processing scripts and scripts to build the GUI that can be installed locally to run on a computer. A complete list of package dependencies and versions are in the GitHub repository (requirements. txt) and File S1. The program was developed using git version control. The web app is hosted by the Brain Imaging Centre at the Montreal Neurological Institute-Hospital (The Neuro) and was installed in a virtual machine directly from the public GitHub repository. When updates are available the changes will be applied to the web app using GitHub. The organization and function of the script files for the program are in Table  S3. The web app can be found at https:// auto-q-pcr. com (Figure  S1 ). The app can also be used locally, installation instructions for command line/Linux as well as executable files for Windows and Apple are on the Auto-qPCR website.
For the relative models, values of reference genes are calculated separately for each input file. The data from one input file will not be applied to another file. For the absolute model, qPCR output for each gene is found in a separate file and the selected endogenous controls will be applied to all the data input in one analysis. For all models, two spreadsheets are created that can be opened in Excel. (1) "clean_data. csv" contains the ΔCT calculated for each technical replicate, including outliers, indicated by "TRUE" in the column "Outlier". (2) and "summary_data. csv" contains the mean, standard deviation (SD) and standard error (SE) for each sample calculated from the included technical replicates; this output can easily be analyzed in another statistical program (R, SASS, Prism). All the input and output data are cleared after processing and no user data is stored in the web app.
org/), the output has been reformatted to be more easily read and interpreted by users and for consistency across statistical outputs. Program function-visualization. The plotting scripts were written using the Matplotlib bar chart function. The labels and axis settings were all adjusted directly within the script (plot. py). The user can dictate the gene/target order and the sample order (cell lines, treatments, time points) in the web app by entering the orders into the appropriate input box. The order variables will be grouped for the summary plots. All the plots are automatically generated and saved as png files. If statistics are applied, two summary bar charts of the mean values are generated, grouped by the selected variable. For two-way ANOVA analysis, the summary bar chart will group the first variable on the x-axis and the second variable will be visualized in different colours and indicated in the legend.
Results The Auto-qPCR program functions with the workflow of a qPCR experiment. A qPCR experiment includes multiple steps that can be divided into two categories: (1) sample preparation to conduct the qPCR reaction, and (2) data analysis, visually represented in the schematic in Fig. 1. Nucleic acids are extracted from biological samples (RNA which is converted to cDNA for quantifying gene expression levels; or genomic DNA). Prior to performing qPCR in vitro, the user must generate the in-silico experimental layout using software that monitors the biochemical reaction. The user defines the experimental design (absolute or relative quantification), the method for detecting DNA synthesis (Taqman or SybrGreen) and the location of each sample within the plate. Finally, at the end of the qPCR process/cycle/program, the recorded data is exported and then would normally be analyzed manually.
In our workflow, the data is exported from the PCR machine and saved as spreadsheet in the form of a txt or csv file (Supplementary Figure  S2 ). The file is then uploaded into the Auto-qPCR web app and the user enters their experimental settings. Auto-qPCR will remove technical replicates by the selected criteria, normalize to an endogenous control, create a clean data table, and summary data table and graphs of all the results. If the user selects the statistical analysis, differential expression analyses will be performed on the designated groups. The program was designed for the most common uses of qPCR: detecting DNA fragment duplications or deletions, and quantifying gene expression levels according to the absolute or relative quantification models.
Genomic instability. A relatively new application for qPCR detects small changes within the genome, from a deletion to a duplication of a DNA segment. DNA regions known to be highly susceptible to such events can be quantified using a genomic instability qPCR test. In induced pluripotent stem cell (iPSC) research, genomic instability tests are critical for quality control to screen for duplication/deletion events that can arise during reprogramming and prolonged cell passaging  31, 32. We performed a qPCR test for genomic stability, where for each cell line, the signal from each DNA region of interest was compared to the endogenous control region. We uploaded the data into the Auto-qPCR web app and selected the genomic instability model (Fig. 2B ). The endogenous control used to normalize the data, was an amplicon of a region on chromosome 4 (CHR4), a location of the genome known not to contain any instabilities.
Nucleic acid preparations are accurately diluted. For the absolute model, a standard curve must be created. The experimental design of the PCR plate, including the chemistry (fluorophore, primer mix), the status of the samples, and the transcripts or DNA region that are going to be amplified, must be generated in silico. After having defined the parameters of the qPCR reactions (number of PCR cycles and length of the different steps (denaturation, hybridization and elongation), and the temperatures), the PCR is run. The exported data from the thermocycler, converted to csv, is entered into the Auto-qPCR software and the model matching the experimental design and parameters for analysis are selected. The software will reformat the data, quantify each sample normalized to controls, and create spreadsheets and graphs to visualize the data analyses, all of which will be included in a zip file for the user to save.
0. 7 indicate a deletion and values above 1. 3 indicate an insertion. For ease of analysis, we have included a column in the output file from the Auto-qPCR program that indicates normal, insertion or deletion (Supplementary Table  S6 ). We found that all seven chromosomal regions in the four cell lines tested were between 0. 7 and 1. 3 and we concluded that no duplications or deletions were present (Fig. 2D  and Supplementary Fig. S3B ). Overall, we demonstrated how Auto-qPCR can be used to analyse the data from a genomic instability qPCR assay, and that the app effectively processed the data, creating a summary table and graph of the data.
We used Auto-qPCR to compare the expression of three gene transcripts across six different cell lines at four different stages in the differentiation of neurons from iPSCs (Fig. 3B  and Supplementary Fig. S4 ). The calibration curve was made from a mix of the cDNAs generated from the reverse-transcribed RNA reactions from the four timepoints in the differentiation process and made of eight four-time serial dilutions to cover a linear relationship in a dynamic range from 1 to 16,384-fold dilution (Fig. 3A ). Raw data was normalized with two endogenous controls (ACTB and GAPDH) (Fig. 3D-H  and Supplementary Fig. S4A ). Auto-qPCR app provides several graphical representations of the normalized expression values. The means of technical replicates are provided for each gene (Fig. 3D ). Bar charts were generated for all gene and sample observations plotted together (grouped by gene Fig.
Relative quantification can be expressed either as RQ = 2 -∆CT , where samples are normalized to internal control(s), or RQ = 2 -∆∆CT , where a given sample is considered as a calibrator for the unknown samples (Fig. 4B, C ). To illustrate the functions of the program, we compared the expression levels of two different control cell lines at two developmental stages, indicated as D0 (neural precursor cells) and D7 (7 days of differentiation into cortical neurons). We measured the expression levels of the progenitor marker PAX6, and two markers of neuronal differentiation (GRIN1and CAMK2A) and normalized to the housekeeping genes ACTB and GAPDH. We used the Auto-qPCR app to process the same data twice, for a direct comparison of the two distinct relative quantification options (Supplementary Fig.
S6A  and S7 ). Interestingly, we found that the CAMK2A RQ ∆CT was twice the level of GRIN1 at D7 RQ ∆CT (Fig. 4F ). We next analysed this dataset with the RQ ∆∆CT model (indicated as ΔΔCT) in the web app (Supplementary Fig. S6B ) where transcript levels are compared to both control gene expression (in this case ACTB and GAPDH) and a calibration sample; in this case we set one sample, AIW002-02-D0 arbitrarily as the reference sample (Fig. 4G ). Here we can easily compare expression in a test condition relative to a control condition by displaying the results as fold change in expression. All decreases are displayed as between 0 and 1 and all the increased expression levels are above 1 (Fig. 4C ). With the double normalization (RQ ∆∆CT ), all values were expressed as a variation compared to the calibrator (AIW002-2-D0) as seen in Fig.
Using only the RQ ∆∆CT analysis, one might mistakenly believe the increase in GRIN1 expression is greater than that of CAMK2a. With Auto-qPCR we provide a quick easy option to process the exported qPCR data with two different relative models. We show the same gene expression ratios between the two time points, but different expression gene levels using the different relative quantitation models. Auto-qPCR produces the same results as manual processing of a previously published dataset. One of our objectives was to provide a tool for analyzing data from qPCR experiments generated with different qPCR machines. We reanalyzed a published dataset generated by the Gorwood lab  30  , on a different machine (Opticon 2, Bio-Rad). The original study measured gene expression in three sub cortical areas (subthalamic nucleus (STN), substantia nigra (SN) and globus pallidus (GP) of mice submitted to a place preference paradigm to cocaine  30.
To allow for statistical analysis of this data, we added a grouping column into the raw data files (Supplementary Table  S13 ) and using the Auto-qPCR statistics module, we reanalysed the effect of drug treatment and brain regions on expression of Nrxn3 across several parameters. We first compared the overall effect of cocaine on expression after pooling the three brain regions and found that although the expression of Nrxn3 was increased across brain regions with cocaine treatment, there was no overall significant effect of drug treatment (Fig. 5C , Supplementary Fig. S9A  and Supplementary Table  S14 ). Comparing the three brain regions while pooling together control and cocaine treatment showed a significant difference in expression across brain regions. Post-hoc analysis revealed Nrxn3 expression in the STN was significantly lower than in the GP and SN (Fig.
Discussion This paper presents Auto-qPCR, a new web app for qPCR analysis and provides examples of the functionalities of the software applied to qPCR experimental datasets generated from DNA (genomic instability assay), cDNA amplification, and RNA transcripts (absolute and relative quantification data). We have also summarized the computational bases of relative and absolute quantifications performed by Auto-qPCR, which is important for users to understand during experimental design. The Auto-qPCR web app also provides a statistical module that will be applicable to the majority of qPCR analysis experiments, and provides a correction across multiple tests, when more than two samples are compared, to mitigate against false positives. As not all experimental designs require differential analyses, the user can use Auto-qPCR without statistical analysis, calculating normalized RNA concentrations, and a summary table and graphs will be generated.
Furthermore, the web app can be used with no installation or login requirements. We have created an easy-to-use program that is completely free and Figure  3. Auto-qPCR can process quantitative qPCR data using a standard curve to perform statistical analysis. Output of Auto-qPCR processing using the absolute model. (A) Illustration of a calibration curve displaying 8 serial dilution points of a four-fold dilution which covers cDNA quantities from 0. 003053 to 50 ng and establishes the linear relationship between CT values (y-axis) and the log2[RNA]. (B) Schematic of PCR workflow showing the pipeline for the absolute quantification using a standard curve in red. (C) Formula used to process a real-time PCR experiment using an absolute quantification design. Top, general formula where the linear relation between the logarithm of RNA concentration and the CT value is provided by the calibration curve.
The normalized quantification is expressed as a ratio between concentrations for the gene of interest and the endogenous control(s) estimated from their respective calibration curves. Bottom, the variables specific to this dataset are shown in the general formula. (D) Bar chart showing the output from Auto-qPCR program using the absolute model for the normalized expression of the gene KCNJ6 for six cell lines at four different developmental stages (iPSC, induced pluripotent stem cells; NPC, Neural progenitor cells; DA4W, dopaminergic neurons at 4 weeks, DA6W: Dopaminergic neurons at 6 weeks). (E,G) Bar charts showing the average expression levels obtained from the three technical replicates for each cell line and time point for the three genes (SYP, KCNJ6 and GRIA1), normalized with two housekeeping genes (ACTB: beta-actin, GAPDH).
It also provides relative quantification results, but requires manual preformatting of an Excel sheet to upload or entering values directly  35. The main specifications of these programs relative to ours are presented in Supplementary Table  S18  for side-by-side comparison. Reviewing different software published to serve similar purposes highlights the unique characteristics of Auto qPCR, as no other web app combines all the features we have included in our software. First as a web app, Auto-qPCR does not require installation or a user login and can be accessed from any device connected to internet. Furthermore, for the users who want to work on their analysis off-line, we also provide the option to install the program onto their computer, which entirely reproduces the environment of the web-app.