dataesr/scientific-paragraphs-categorization huggingface dataset:
PeccaResult Various software tools allow the estimation of population parameters, whether they are proprietary (NONMEM, Monolix, ADAPT) or open-source (Saemix, nlmixr), producing similar outputs (parameter estimates, objective function, simulated values,. ), but often stored with different architecture. The goal of PeccaResult is thus to provide a tool to easily compare various runs together independently of the parameter estimation software by first standardizing the output of each software in a uniformized structure before manipulating those structures with new model diagnostic functions. However, metaprogramming such uniformization of software is very challenging and would probably result in a final code far too complex compared to the direct use of other specific software (e. g. , Monolix Suite or Xpose for NONMEM runs 23 ).
Generality The use of multiple software is essential for pharmacometricians to improve efficiency, as no single program can perform all desired analyses. However, this creates significant efficiency issues. In this context, Peccary is an R project created with the sole objective of improving the efficiency of pharmacometricians. So far, the most complex model on which Peccary has been used in real conditions (using both PeccAnalysis, PeccaReverse and PeccaResult functions) was a Monolix PK/PD model containing complex if-else structures, multiple covariates, 51 parameters, 18 ODEs, and five observation types fitted simultaneously.
Pandoc legacy From many aspects, Peccary tried to reproduce the markdown pandoc efficiency but applied in PMx. First, Peccary can accelerate the use of various R packages (pknca, RxODE, PopED, table1,. ) by simplifying their syntax, and hence reducing 1) the required knowledge and 2) the time needed before launching these analyses. Second, Peccary can create links between the different packages. For instance, the same input tables in PeccaReverse are used both for simulations, design evaluation and syntax translations, improving the efficiency at the multi-software (or interoperability) level. The Shiny application is fast enough to be used during a live meeting, dynamically producing plots or analyses. Similarly, the Shiny app has educational potential, especially with its ability to easily reload any dataset or model simulations.
Fixing bugs Due to the bazaar development approach of Peccary, there is a large heterogeneity in terms of Peccary function qualities. Some of them have been tested, used, improved and fixed several times until complete stability. Some others (the most recent or the less used ones) are more likely to contain errors or to be incomplete. Nevertheless, Peccary was designed to be useful even if imperfect and unvalidated. Despite the integration of several unit tests (e. g. , for the peccary_nca() function, more information on GitHub), it is always the user's responsibility to verify the results, as the purpose of Peccary is only to speed up the creation of R codes and have quick access to various results. Users can verify these outputs in two ways, 1) via the metaprogramming system and 2) by comparing the Peccary result to any other software making the same result.
However, the actual potentialities of Peccary to improve work of pharmacometricians have to be better demonstrated in the paper. For example, the description of PeccaReverse is mainly focused on the implementation and simulation of a model through deSolve and/or RxODE. However, a pharmacometric model is typically developed in NONMEM or Monolix and it wouldn't be efficient to re-code the model, the parameters and the study design to make simulations through PeccaReverse functions. I think that the possibility of importing NONMEM and Monolix projects could be of great value and could increase the usability of PeccaReverse package. In the paper, only a short paragraph was included on this topic and any example was reported. I strongly suggest to extend this point, also providing examples. In addition, the authors reported that only simplest study designs can be imported and translated for now.
Please provide a more complete list of supported features. As also reported by the authors in the paper, the use of multiple software is a common practice during a pharmacometrics project. For example, a very common situation is to develop and identify a model using a software (NONMEM) and, subsequently, to perform simulations through another tool (SimulX). The possibility of switch from one tool to another without re-coding the model and the entire set-up would allow to strongly improve efficiency. I would like to see an example in which a model (and the entire set-up) is developed and identified in a tool, it is imported in Peccary and then translated into another syntax to perform simulations through a different software. Is it currently possible in Peccary? Minor: Most Peccary functions have been metaprogrammed.
Peccary source code: Software available using the following R code (automatically downloading all the subpackages): devtools::install_ github("Peccary-PMX/Peccary") Source code available from: https://github. com/Peccary-PMX/Peccary Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6841801  41  License: GNU General Public License v3. 0.
PeccAnalysis source code: Software available using the following R code (already downloaded with Peccary main package): devtools::install_ github("Peccary-PMX/PeccAnalysis") Source code available from: https://github. com/Peccary-PMX/PeccAnalysis Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6841823  42  License: GNU General Public License v3. 0.
PeccaReverse source code: Software available using the following R code (already downloaded with Peccary main package): devtools::install_ github("Peccary-PMX/PeccaReverse") Source code available from: https://github. com/Peccary-PMX/PeccaReverse Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6841829  43  License: GNU General Public License v3. 0.
PeccaResult source code: Software available using the following R code (already downloaded with Peccary main package): devtools::install_ github("Peccary-PMX/PeccaResult") Source code available from: https://github. com/Peccary-PMX/PeccaResult Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 6841833  44  License: GNU General Public License v3. 0. Author contributions TD initiated the project, designed the software architecture, implemented Peccary and wrote the original manuscript. SF supervised the project. SF and XD reviewed and edited the manuscript.
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Mathematical modelling in pharmacometrics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.
return(list(y = y, poped. db = poped. db)) ## } ## poped. db <-create. poped. database(ff_fun = "model2", fError_fun = "feps_ODE_compiled", ## fg_fun = "fg", groupsize = c(30, 15), m = 2L, sigma = c(conc_prop = 0. 2, ## Gut_prop = 0. 1), notfixed_sigma = c(conc_prop = 1, ## Gut_prop = 1), bpop = c(ka = 0. 3, ke = 0. 1, k12 = 0. 3, ## k21 = 0. 1, vd = 5), notfixed_bpop = c(1, 1, 1, 1, ## 1), d = c(ka = 0. 3, k21 = 0. 3, k12 = 0. 3, ke = 0. 3, ## vd = 0. 3), notfixed_d = c(ka = 1, k21 = 1, k12 = 1, ## ke = 1, vd = 1), xt = list(c(c(0. 2, 1, 5, 10), c(0. 2, ## 1, 5, 10)), seq(0, 48, 12)), model_switch = list(c(1, #.
and in vivo experimental protocols to obtain reproducible Ca 2+ imaging data from oligodendroglia in demyelinated lesions. Importantly, we provide an analytical pipeline containing two free, open source and cross-platform software programs, Occam and post-prOccam, that enable the fully automated analysis of one-and two-photon Ca 2+ imaging datasets from oligodendroglia obtained by either ex vivo or in vivo Ca 2+ imaging techniques. This versatile and accessible experimental and analytical framework, which revealed significant but uncorrelated spontaneous Ca 2+ activity in oligodendroglia inside demyelinated lesions, should facilitate the elucidation of Ca 2+ -mediated mechanisms underlying remyelination and therefore help to accelerate the development of therapeutic strategies for the many myelin-related disorders, such as multiple sclerosis.
Older ROI-based astrocyte Ca 2+    imaging analysis packages such as GECIquant and CaSCaDe rely on manual input which may introduce bias, making them not fully desirable  (Agarwal et al. , 2017; Venugopal et al. , 2019). Here, we provide a comprehensive experimental and analytical workflow to investigate oligodendroglial Ca 2+ imaging data obtained either ex vivo or in vivo in two different preclinical mouse models of myelin repair, the lysolecithin (LPC) and cuprizone (CPZ) models. We established reproducible experimental protocols aiming to obtain Ca 2+ imaging data of oligodendroglia both in brain slices and in freely moving mice expressing genetically encoded fluorescent Ca 2+ indicators. Next, we developed two free, open source and cross-platform software programs, Occam and post-prOccam (Occam: 5 Oligodendroglial cells calcium activity monitoring), for the fully automated analysis of one-and twophoton Ca 2+ imaging data from oligodendroglia in demyelinated lesions (GNU GPLv3+ license; code repository at: https://gitlab.
Occam and post-prOccam: Ca 2+ -signal analysis softwares for investigation of oligodendroglia in demyelinated lesions To perform an automated and unbiased detection of active regions in different Ca 2+ imaging datasets from demyelinated preparations, we developed a configurable Fiji/ImageJ2 plugin named Occam (Oligodendroglial cells calcium activity monitoring; Fig. 2a ). We first validated our analytical workflow on wide-field one-photon Ca 2+ imaging data recorded in acute slices of corpus callosum which revealed high levels of spontaneous Ca 2+ activity in oligodendroglia of LPC-induced demyelinated lesions (Fig. 1b , 1d, 2a, 3a; Supplementary Video 1). Occam first performs noise and bleaching corrections of image stacks (Fig. 2a ) and then uses the trainable machine learning-based WEKA Fiji/ImageJ2 segmentation plugin  (Arganda-Carreras et al.
2a, 2b ; Supplementary Manual). These two files are then read by the post-prOccam software to perform a systematic ROI analysis along with quantitative calculations ultimately aimed at easing the detailed characterization of oligodendroglial Ca 2+ activity (Fig. 2b ). The post-prOccam software was implemented as a configurable program where users can set various analysis parameters in a configuration file so as to adapt its operation to any specific dataset (Supplementary Manual). Supplementary Manual). Based on repeated intensity values subtraction in a sliding window over the whole ROI vector of mean intensity values (Supplementary Fig. 2a-c ), our method allowed us to single out fluorescence intensity changes in an image stack that were greater than noise fluctuations (Fig. 2b , 3a-d). Since Ca 2+ signals of oligodendroglia in lesions were characterized by long-lasting kinetics (Fig.
Finally, the post-prOccam program performs a series of calculations to help characterize the Ca 2+ activity of oligodendroglia 12 in lesions (Fig. 2b ). Figure  3f -h illustrates part of the calculations performed for each ROI in a single image stack, i. e. the mean integral, the percentage of active area and the mean integral multiplied by the percentage of the active area. Since the complex features of oligodendroglial Ca 2+ events preclude their proper individual isolation (Supplementary Fig. 3 ), we found it more appropriate to calculate the mean intensity integral of each ROI trace rather than to use a procedure for single event detection (see discussion). As expected, high intensity ROIs exhibited significantly larger mean integrals than medium intensity ROIs (Fig. 3f ) and, despite similar mean percentage of active area between high and medium intensity ROIs, the mean integral multiplied by the percentage of the active area remained larger for high intensity ROIs (Fig.
3i-l ). 14 For informational and debugging purposes, a log file is produced by post-prOccam with a highly detailed account of all the data processing steps and their outcome. The accepted ROIs and the corresponding processed data of a stack are saved by post-prOccam in two separate files for further statistical analysis. Of note is the pp-supervisor program that allows the scientist to run post-prOccam automatically over a set of stacks located in a single directory using the configuration file that was elaborated during the exploratory steps described above.
Interval-based analysis and evaluation of synchronized Ca 2+ signals of oligodendroglia in lesions One important feature of post-prOccam is its ability to compare ROIs only at specific time point intervals of the recorded datasets. For instance, in the case of applications of pharmacological agents during Ca 2+ imaging recordings, a user might wish to compare Ca 2+ signals in the absence or in the presence of a drug in a single image stack. The default post-prOccam software behavior is to perform all the calculations described in the previous sections on the whole ROI vector. However, in specific cases mentioned above, it might be useful for the calculations to be performed only over selected ranges of the ROI vector (that is, intervals of that ROI's acquisition time points). The configuration file provides a section in which the user might list any number of ROI vector intervals over which to perform the previously described calculations.
4e-g ), with a mean Pearson correlation value of 0. 82±0. 04. These data showed that the majority of ROIs did simultaneously respond to the agonist. The post-prOccam software thus successfully determined the degree of Ca 2+ activity correlation between ROIs and was able to detect simultaneous increases of that activity as induced by pharmacological agents. In order to adapt to any specifics of biological applications, the Pearson correlation R value threshold might be configured. The percentage of correlated ROIs as well as the mean Pearson correlation coefficient are reported for each image stack in the corresponding output file generated by the program. Taken together, our results show that the Occam and the post-prOccam software programs make for a configurable and automatable solution for the analysis of oligodendroglial Ca 2+ signals.
Two-photon imaging of Ca 2+ signals in single oligodendroglia in demyelinated lesions Although one-photon imaging allows one to determine the global Ca 2+ signaling properties of oligodendroglia in a large area of the LPC-induced lesion, the spatial resolution of this technique is not sufficient to reveal local Ca 2+ changes occurring specifically either in the soma or in the cell processes of any individual cell. To test whether our workflow is suitable for the analysis of images obtained by twophoton microscopy at the single cell resolution, we performed two-photon Ca 2+ imaging of putative OPCs and OLs in callosal LPC-induced lesions in ex vivo brain slices and analyzed the data using Occam and post-prOccam (Fig. 5 ; Supplementary Video 2 and Supplementary File 2). We used morphological criteria to distinguish OPCs and OLs, and only recorded cells for which we could connect the processes to a particular soma.
6a-b ; Supplementary Video 3). Four animals were implanted according to the procedure described in Figure  1. Three of them were recorded during the fifth postnatal week of CPZ feeding, when demyelination is advanced  (Remaud et al. , 2017) , and sacrificed afterwards in order to confirm the proper position of the GRIN lens above the corpus callosum. The fourth mouse was maintained alive and imaged at four different timepoints to show that multiple recordings of the same animal can be made over several weeks. The image stacks we acquired contained 4200 frames collected over 7 minutes. To enhance the precision of ROI detection by Occam in such large data files, the program first splits the image stack into a configurable number of sub-stacks. Then, for each sub-stack, it performs the noise correction, uses the maximum and standard deviation projections to build an input for the ROI classifier and proceeds to the automated ROI designation using the WEKA Fiji/ImageJ2 segmentation plugin combined with a local maxima segmentation tool (Fig.
Discussion While it is suspected that oligodendroglial Ca 2+ signaling plays a key role in myelin repair, the characteristics of oligodendroglial Ca 2+ signals in demyelinated lesions remain unknown. This is due to a limited number of ex vivo or in vivo Ca 2+ imaging studies as well as a lack of automated analytical tools adapted to the monitoring of the specific characteristics of oligodendroglial Ca 2+ activity. To fill this need, we devised experimental conditions for ex vivo and in vivo Ca 2+ imaging in mouse demyelinated lesions and implemented an analytical workflow comprising two programs, Occam and post-prOccam, which provide a reliable solution for the automated in-depth analysis of one-and two-photon oligodendroglial Ca 2+ imaging data. Tested in Windows 10 and Debian GNU/Linux, these cross-platform programs were designed to be easily configurable to ensure an unbiased selection of active ROIs and to allow an efficient analysis of large datasets.
, 2017)  and in recordings at single cell resolution  (Baraban et al. , 2018; Krasnow et al. , 2018; Battefeld et al. , 2019; Marisca et al. , 2020) , indicating that complex Ca 2+ dynamics are a hallmark of oligodendroglia and it is therefore essential to evaluate them in an appropriate manner. Thresholding techniques or event template detection methods commonly used on neuron and astrocyte Ca 2+ imaging datasets are not easily applicable to the unique and complex Ca 2+ events observed in oligodendroglia. To overcome this constraint, we evaluate activity levels by measuring the number and size of active ROIs as well as the integral of the traces. Our measurements thus account for the activity throughout the whole ROI trace or during configurable intervals without isolating Ca 2+ activity events.
Ex vivo wide-field calcium imaging Demyelinated lesions were recognized under the microscope at 4x as a brighter area in corpus callosum as described previously  (Sahel et al. , 2015). After identifying the demyelinated lesion at 4x, cells expressing GCaMP6f in the lesion were visualized with a 40x water immersion objective in a wide-field microscope (Olympus BX51) using a LED system (CoolLED PE-2; Scientifica, UK) and a CCD camera (ImageQ, Optimos; Scientifica, UK). Excitation and emission wavelengths were 470 nm and 525 nm, respectively. The CCD camera and the LED system were controlled using a Digidata 1440A interface and Pclamp10. 5 software (Molecular Devices, USA). The image stacks were acquired at a frame rate of 1. 75 Hz with 50 ms light exposure for a total duration of 240 s using Micro-manager-1.
Occam and post-prOccam: an automated analytical workflow for oligodendroglial Ca 2+ imaging data The analytical processing consists of two parts performed by two distinct software pieces thoroughly described in the software user manual provided as supplementary information (Supplementary Manual). Briefly, the analysis is composed of two main steps: first, the image stack is processed by Occam, a Fiji/ImageJ2-based plugin written in Java (Fiji/Imagej2 version 1. 53k or later); second, the data output by Occam is processed by post-prOccam, a software written in Python, to both refine it and perform a series of calculations on the refined data. Importantly, ex vivo one-photon, ex vivo two-photon and in vivo microendoscopic Ca 2+ imaging stacks are processed in slightly different ways to optimize the analysis of Ca 2+ signals acquired in these different preparations (Fig.
2a ; Supplementary Manual). The softwares described in this report are cross-platform Free and Open Source Software (FOSS) and licensed under the GNU GPLv3+ license (available at: https://gitlab. com/d5674/occam). Preprocessing: noise correction and ROI definition. The input for Occam is an oligodendroglial Ca 2+ imaging stack as recorded by the microscopy acquisition software. The Occam software initially performs noise correction steps according to the imaging condition, and then proceeds to an automatic ROI designation (Supplementary Manual). ROI designation is carried out using the machine learning-based WEKA Fiji/ImageJ2 plugin that performs a trainable segmentation of frames  (Arganda-Carreras et al. , 2017)  combined with a local maxima segmentation tool. A ROI as designated by Occam is defined as a vector containing the mean fluorescence intensity value of the corresponding region in the different frames of the stack (that is, over the acquisition time points).
These files are then fed to the post-prOccam software. Post-processing: automated ROI refinement and quantifications. The post-prOccam software is a configurable software that processes the files produced by Occam. Before performing a baseline subtraction, false positive ROI traces of a stack are rejected by using a sliding window-based subtraction of intensities in each ROI trace and applying a detection threshold defined by the mean absolute deviation (MAD) value on each subtracted trace (Supplementary Manual). Only ROIs that show significant Ca 2+fluorescence fluctuations are accepted for further analyses. A number of parameters configuring the ROI processing and the filtering stringency can be set in a configuration file (Supplementary Files 1-3). The accepted ROIs of a stack are output to a file and calculations that are then computed on these ROIs are output to another file (such as the surface area of each ROI in pixels, the ROI integral, the sum integral of all ROIs, the total ROI surface area in pixels, the sum integral corrected by the surface area of all ROIs, the Pearson correlation between each ROI and every other ROI; Supplementary Manual).
Statistical analysis Data are expressed as mean ± SEM. GraphPad Prism (version 9. 3. 0; GraphPad Software Inc. , USA) was used for statistical analysis. Each group of data was first subjected to Shapiro-Wilk normality test. According to the data structure, two-group comparisons were performed using the two-tailed unpaired Student's t-test or the non-parametric two-tailed unpaired Mann-Whitney U test for independent.
(b) Output of Occam can be fed to post-prOccam which is a configurable Python-based software that first rejects any ROIs that do not show significant Ca 2+ fluctuations, then performs baseline corrections, subsequently performs a series of calculations for each ROI in the image stack and finally computes a ROIs correlation matrix of the Pearson correlation value of each ROI as compared to every other ROI. Occam and post-prOccam are available as free, multiplatform and open source softwares at: https://gitlab. com/d5674/occam and procedures are described in more detail in the Supplementary Manual. Automated ROI detection algorithms cannot be error-free and almost always require an a.