dataesr/scientific-paragraphs-categorization huggingface dataset:
Tracking particles The particle tracker is available only as a GUI tool at the time of writing. It can be accessed by typing the command trait2d_tracker_gui in a terminal with a compatible Python installation. The application accepts 8 bit TIFF stacks as an input. It is possible to run a simple pre-processing step with background subtraction. The tracker GUI provides options to set parameters, and to preview particle detection results frame by frame. Once the parameters for detection and trajectory linking are deemed satisfactory by the user, the application will perform the trajectory linking over the image stack, and export another tiff stack where the detected trajectories are superimposed as a coloured track over the original data (Figure  2 ). The user can thereafter choose to export the particle tracks as a csv file, with formatting compatible to the analysis tool.
e. the time range over which the MSD analysis is performed, must be chosen with care to avoid misrepresenting the dynamics of the tracked particles, especially in the case of Brownian motion. [16] [17] [18]  Analysing a track through the ADC pipeline In order to be analysed, a track must be contained in a compatible file or data structure (see above). Data analysis can then be performed through either the analysis GUI, accessible by typing trait2d_analysis_gui in a terminal with a compatible installation of Python, or through a user script by importing the trait2d. analysis module. For the use of the analysis GUI, we would reference the documentation https://eggeling-lab-microscope-software. github. io/ TRAIT2D/analysis_gui. html, where it is extensively described. The following code snippets exemplify how to load a single track from one of the example files provided with the code, and analyse it using a Python script.
analysis import ModelDB ModelDB(). add_model(ModelBrownian) For the other models, the procedure is identical, however more or less parameters should be initialized, as required by their expression. Once the ModelDB has been populated with the desired diffusion models, the analysis is performed via the command: DM, results = single_track. adc_analysis(fit_max_time=0. 5e-1) The analysis module saves the analysis results to the trait2d. analysis. Track object, so they can easily be accessed at a later point. More options concerning the whole analysis pipeline are also available in the documentation. Several examples are also provided as downloadable Jupyter Notebooks.
Adding a custom model for ADC analysis The analysis module allows to define custom diffusion models which can be used in the analysis pipeline, and integrate seamlessly into it. This is done by defining a class which inherits from trait2d. analysis. models. ModelBase. This functionality is only available when importing the module in a Python script and not from the GUI. The newly defined model can be added to trait2d. analysis. ModelDB, in addition to or instead of the built-in models and will be used in all subsequent analyses. This functionality is only available when importing the module in a Python script and not from the GUI.
Bulk processing When using the analysis module inside a Python script, bulk processing of multiple tracks is available. For this, the trait2d. analysis. ListOfTracks class is used. In the following example, a CSV file containing multiple tracks is loaded. In addition to MSD and ADC analysis, ListOfTracks also exposes useful methods such as ListOf-Tracks. plot_trajectories or ListOfTracks. adc_summary to quickly visualise all contents and analysis results of the contained tracks. Bulk processing is also available for MSD analysis via ListOfTracks. msd_ analysis. from trait2d. analysis import ListOfTracks tracks = ListOfTracks. from_file("three_tracks. csv", unit_length='micrometres') tracks. plot_trajectories() from trait2d. analysis. models import ModelBrownian, ModelConfined, ModelHop from trait2d.
Simulating a track Track simulation can be done either from the GUI, which can be accessed by typing trait2d_simulator_gui in a terminal with Python available, or by importing a simulator from trait2d. simulators. The following code example shows the simulation of a track using the imported module and the built-in Brownian diffusion simulator. The GUI also allows for the creation of an image sequence from the simulated track or a loaded trajectory file. Note that this is currently not possible from the imported module inside a Python script. Figure  3  shows such a simulated particle diffusion movie.
Conclusion TRAIT2D is a Python-based, open source and easily deployable toolkit for the analysis and simulation of two-dimensional SPT data. The combination of particle tracking, simulation, and trajectory analysis in the same package provides a number of benefits for the user. Another beneficial feature of this software is its user-friendliness for inexperienced users, thanks to the graphical user interfaces provided for each module. Nevertheless, the potential for customisation is retained and the scope of the module can be readily expanded by users with more advanced coding background.
Data availability The tracks used throughout the examples above are available in the source tree at the GitHub repository mentioned above in the examples folder: track1. csv, track2. csv, and track3. csv each contain a single track in the format expected by TRAIT2D. three_tracks. csv contains the same files which are identified by a separate id column. These example files report the trajectories obtained by performing particle tracking on three distinct timelapses of 40 nm diameter, streptavidin-coated gold nanoparticle linked to DSPE-PEG(2000)-biotin lipid analogues diffusing on an artificial membrane (Supported Lipid Bilayer) created on glass support. A track_example. tif file, consisting of a movie of a diffusing 40 nm streptavidin-coated gold nanoparticle linked to a biotinylated lipid analogue (DSPE-PEG (2000)-biotin), is provided as an example for the particle tracker module.
-trait2d_tracker_gui ○ Once again, I had to fix some dependency issues before successfully launching the app. The example file was processed successfully using the default parameters. Summary: The installation problems I ran into are not insurmountable assuming some basic familiarity with the pip command but could result in a lot of overhead for the authors if they target non-Python savvy users. It might be useful to release some "standalone" versions (maybe PC and Mac only?) of the runtime to avoid these problems. ○ I found the color contrast rather poor for the simulator and tracker GUIs. Consider using the same color scheme as for the analysis GUI for instance, although the graph part of it is also lacking contrast. ○ 3. As a last point, I would suggest setting the correct option for ReadTheDocs such that a PDF version of the manual can be downloaded (web navigation is not always the best approach to discover a specific feature of a new software).
Version 1 Reviewer Report 05 October 2021 https://doi. org/10. 5256/f1000research. 58301. r95432 tracking (SPT); 2) single-trajectory analysis (STA) by MSD (isotropic Brownian diffusion and isotropic anomalous diffusion) or ADC (Apparent Diffusion Coefficient, an acronym coined by the authors to designate a single-MSD point estimate of the diffusion coefficient); and 3) SPT movie simulation. The package is provided as a series of Python modules installed using the pip install command and can be run as 3 separate GUIs, as Python command lines, or within Jupyter Notebooks. The package comes with fairly detailed documentation (ReadTheDocs format, with code comments used to describe the different functions and classes in the module, and additional illustrated text description of the installation procedure and basic tutorials).
The manuscript consists of a very basic introduction on the basics of SPT and single-trajectory analysis and a brief overview of a few examples of how to use the 3 different modules (SPT, Analysis and Simulation), and provides the necessary links to the Github repository and documentation. It is fairly well written and is sufficient to assess what the package will allow an inexperienced user to do. Basic attempts by this reviewer to test the different GUIs, followed by some failures or lengthy lists of warnings, seem to indicate that minor additional details and guidance could be useful for potential inexperienced users. The samples of code checked by the reviewer appear well-documented, but an overview of the relationship between different functions and classes might be useful for developers interested in using this project as a basis for their own tools (contributions do not appear to be discussed in the Github repository).
Introduction While this is not a review article, it seems that the introduction ignores a large body of literature and code on SPT and STA. For instance, the Particle Tracking Challenge paper (2014)  1  could be cited as it was an interesting effort to compare the multitude of isolated tools existing at the time, and would have provided a convenient set of test files to run the present software package on. There are many (literally dozens of) freely available packages to track and analyze single-particle data (easily found by an internet search with single-particle tracking as the keyword) and not all are using Matlab or "require licensed software to be executed" (Matlab can usually be replaced by the open-source Octave package), some are web-based (Spot-On) and many come with GUIs; therefore, some of the arguments put forward by the authors to justify their effort are somewhat artificial.
From a coding perspective, it could be useful to document how to replace this part of the code with another (or better yet, provide an example of such an optional alternative fitting algorithm -FluoBancroft comes to mind). When mentioning the blurring effect on diffusion analysis, the paper by Goulian    4  went some extra length to show how this affected the uncertainty on estimated parameters when performing optimal analysis (a question addressed further below). The authors might find it useful to point their readers to these papers (or not: it is not my intention to be self-serving, only to point out the often forgotten and rediscovered fact that this is not a completely trivial matter), which are a good starting point to follow the more recent literature on parameter estimation in more complex models.
Test of the provided code I only tested the GUIs after installation in a dedicated Anaconda environment. Unfortunately, I encountered a few issues, which the authors might be able to address in the documentation. trait2d_analysis_gui: Tested on the provided trackn. csv files, the terminal window spits out a large number of warnings. How is the user supposed to deal with those (and should they care)? ○ What is the "Max. fit iterations" parameter for? ○ What is the "Use parameters as initial guesses" checkbox for? ○ What is the "Range" parameter for (it seems to be used to determine the number of data points (MSD or ADC) used for analysis)? I am assuming this is equivalent to the "fit_max_time" parameter discussed in the command line version (P 10) but again, this could be leaving a lot of freedom to a user unaware of the literature.
I used the test image stack provided with TrackMate (a FIJI plugin) and the default parameters (with Light Spot option) but that failed (empty track). The file I generated by simulation using the previous module worked somewhat but it was difficult to figure out optimal parameters, and once tracking is started, it appears impossible to abort it (the progress was clearly indicated by the terminal output). Enabling partial initial processing of large stacks could prevent this kind of problem. Likewise, providing a short example stack -for instance, that discussed in the text -and initializing the GUI with appropriate parameters could help a user play around with parameters and understand the influence of each of the 7 important ones. Saving/loading analysis parameters for future reuse with similar datasets could also be useful.
We, therefore, invite the reviewer to contribute an issue to our GitHub once the review process is complete so that the topic can be further debated and the tool developed in this direction. The inclusion of Ref [30] before equation (  5 ) is an oversight by the authors and it has been amended, we apologize for the confusion. The inclusions of Eq. (  5 ) and (  8 ) are included as the MSD is still widely used as a measure to at least qualitatively evaluate anomalous diffusion [2], and thus its inclusion was deemed worthwhile. It should be noted, however, that while in the GUI, in the present release, both Eq. (  7 ) and (  8 ) are always fitted to the experimental data, this is not the case for a customized script including our library, which allows the user to freely exclude either, or define their own models altogether.
Tracking The reference to the Laplacian-of-Gaussian and a short discussion of its role is included in the manuscript, as well as the influence of the LoG kernel and threshold parameters into the spot detection. Suggestions on the parameter settings are also provided in the software manual. We have added a link to the manual in the software interface to make it convenient for the user to find the information. We agree with the reviewer that localization uncertainty and integrated intensity can be a useful addition to the tracker output, and as a GitHub-based open-source project, we intend to extend the current version with a contribution from the community and our team. Regarding the current tool, the Radial Symmetry center approach in the implementation made available as a reference in the text only provides the standard deviation of the fitted 2D gaussian as an estimation, and not a calculated parameter, and does not include an integrated intensity.
Simulations The displacement distribution was modified to use N(0,sqrt(2D dt)). numpy. random. randn returns a sample from the standard normal distribution N(0,1). To convert this to any normal distribution N(mu, sigma^2) => sigma * N(0,1) + mu. For mu=0 and var=sigma^2=2D dt, we indeed need to change the multiplicative factor in the free (Brownian) equation from sqrt(D dt) to sqrt(2 D dt). This modification was done in both the software and in the paper. Also, in the context of the movie simulator, we used the SNR parameter as a way to adjust the contrast between the simulated particle intensity and the background intensity (contrast = particle intensity -background intensity). To better represent this, we have renamed this parameter to "contrast" in the GUI, the code, the documentation, and the paper.
○ The Range parameter is, indeed, equivalent to the fix_max_time argument present in the code at various points. The issue of proper range determination is already presented in the paper. A link to the paper was added to the README file in the repository, so that it can serve as further documentation for the user, together with the present reviewer report. Once again, we extend an invitation to the reviewer to write an issue on the GitHub repository on this topic, following the finalization of the review process, to direct further developments of the tool.
Software availability • Software available from https://github. com/Eggeling-Lab-Microscope-Software/TRAIT2D/releases/ and https:// pypi. org/project/trait2d. • Source code available from https://github. com/Eggeling-Lab-Microscope-Software/TRAIT2D. • Archived source code at time of publication: https://doi. org/10. 5281/zenodo. 4725268. • License: GPL-3. 0 License.
Patients were included if they met the same criteria as the discovery cohort. Cases and controls were also similarly defined. For cases, serum samples were collected on the same day as RHC (except for four samples collected within 6 weeks before). In a third step, in order to determine if our candidate biomarkers were influenced by other SSc manifestations apart from PAH, three other patient groups were recruited from Boston University Arthritis Centre. The first group included patients with dcSSc, no extensive ILD and no evidence of PAH. A second group included patients with lcSSc, extensive ILD and no evidence of PAH. A third group included healthy controls. All patients were recruited consecutively by screening the databases of participating centres in 2015. Serum samples had been collected between 2004 and 2014.
Statistical analyses For the description of study populations, quantitative variables were expressed as means±SD or medians (IQR) for nonnormal distributions, and categorical variables were expressed as numbers (percentage). Normality of distributions was assessed using histograms and tested using the Shapiro-Wilk test. In a first step, data from the discovery cohort were log2 transformed and a differential analysis was performed between cases and controls with the Bioconductor R (V. 3. 6. 1) package limma (V. 3. 38. 3). 19 Limma uses an empirical Bayesian approach to estimate variances in moderated t-tests, which has proven to improve results on standard t-tests, especially when the number of replicates is small. Raw p values were adjusted with the Benjamini Hochberg method  20  and proteins with adjusted p values <0.
05 were considered differentially expressed. For cases, the Spearman correlations between PVR and expressions of these differentially expressed proteins were tested and were considered significant if raw p values were <0. 05. In a second step, differential expression of candidate biomarkers identified in the discovery cohort was assessed between cases and controls from the validation cohort using Mann-Whitney test; and correlations with PVR in cases were analysed using Spearman test. In a third step, serum levels of the candidate biomarkers were compared between the five patient groups using the Kruskal-Wallis test. Pairwise comparison was conducted by post-hoc Dunn test followed by Bonferroni correction. As protein production could always be quantified in all datasets, no imputation for missing data was performed.
Single-cell RNA sequencing in explanted human lungs Data generation Lung samples from four SSc-PAH patients and four non-SSc controls were transported in Perfadex and processed within 30 min of explantation as previously described. 21 Of note, three SSc patients had extensive ILD. Following mechanical and enzymatic digestion by Liberase DL and DNase I, resulting singlecell suspensions were loaded into 10 x Genomics Chromium instrument (Pleasanton, California, USA), with scRNA-seq library preparation per manufacturer's protocol. Sequencing was performed on an Illumina NovaSeq 6000 instrument through the UPMC Genome Centre.
04) in cases (figure  2 ). Serum SET levels were undetectable in both cases and controls when measured by ELISA, consistent with its intracellular nature. In a third step, in order to determine if other SSc manifestations influenced biomarker concentrations, chemerin serum levels from our discovery cases and controls were compared with other patient groups included in the SOMAscan dataset: patients with dcSSc, no extensive ILD and no PAH (n=11); patients with lcSSc, extensive ILD and no PAH (n=10); and healthy controls (n=11). Circulating concentrations of chemerin were only increased in the PAH group, and similar to healthy controls in all others (figure  3 ). Overall, these results could suggest that chemerin seems a potential surrogate biomarker for PVR in SSc-PAH. Since results with SET were not reproduced, and since literature data suggested chemerin to be a more promising pathophysiological lead, we chose to focus our investigations on this latter protein.
Body Composition, Cardio-Respiratory Fitness, and One-Repetition Maximum Body weights and heights were assessed using a calibrated scale (Seca, Germany) and stadiometer (Seca, Germany), respectively. These data were used to calculate body mass index (BMI) (kg/m 2 ). Levels of fat-free mass (FFM), and fat mass (FM) were measured by a bio-impedance analyzer (Medigate Company Inc. , Dan-dong Gunpo, South Korea). Evaluation of VO 2peak was conducted using a modified Bruce protocol (in a temperature-controlled room, 21-23 • C) as reported in previous studies of overweight and obese populations  (Hunter et al. , 2008; Ghroubi et al. , 2009) , using an electrically motorized treadmill (H/P/Cosmos, Pulsar med 3p-Sports & Medical, Nussdorf-Traunstein Germany). The physiological criteria used to determine VO 2peak [according to American College of Sports Medicine (ACSM) guidelines] included: if subjects reported they were physically exhausted and reached their maximal effort (according to Borg scale), or if the supervisor recognized the subjects had severe dyspnea, dizziness and other limiting symptoms based on guidelines for CPET test of ACSM and American Heart Association (AHA;  Thompson et al.
Statistical Analysis All data were evaluated with SPSS software (version 22), and the normality of the data was assessed by the Shapiro-Wilk test. Oneway ANOVA and Tukey's post-hoc tests were used for evaluation baseline data of four groups. Interactions between groups were determined by a two-way ANOVA repeated measures test (Groups * time). When a significant difference was detected by ANOVA, mean differences were determined by pairwise comparisons. Correlation between Nrg4 levels and other data was measured with Pearson correlation tests. The sample size was calculated to detect a statistical difference between study variables with a 95% confidence interval (CI) and 80% or greater power value. Additionally, effect sizes (ES) were determined from ANOVA output by partial eta-squared. Moreover, within-group ES were computed using the following equation: ES = (mean post-mean pre)/SD  (Field, 2013).
For the invasion assay, cells were incubated overnight at 4 • C with recombinant anti-vimentin antibody conjugated with Alexa Fluor® 568 (ab202504, Abcam®) at a 1/1000 dilution. After washing the actin cytoskeleton was stained with Alexa Fluor® 488 Phalloidin in PBS (containing 1% DMSO from the original stock solution, Abcam®) for 40 min at room temperature in a dark chamber. Cell nuclei were then stained with DAPI (4,6-diamidino-2-phenylindole dihydrochloride, Molecular Probe®) for 15 min. Immunofluorescent labelling was observed with a confocal microscope (LSM710, Zeiss) equipped with 405, 488, and 543 nm lasers and with LSM ZEN 2009 software and using 1 μm z-stack intervals and sequential scanning. Images were processed with ImageJ.
Since canine meningioma subtypes have not been definitely correlated to prognosis, the tumour subtype was not considered for the histologic grade. For each slide, the tumour area of the largest sample fragment was calculated by each pathologist, using NDP view. 2 free software. The consensus diagnosis for the histologic grade and the mitotic grade was obtained by reviewing the cases with histologic disagreement by all the participants at roundtables. Consensus diagnosis was defined as the diagnosis given by at least three out of four pathologists (majority consensus diagnosis).
In order to evaluate the possible impact of the histologically available tumour size on agreement about the histologic and mitotic grade, estimates of CCC TOT , CCC INTER and CCC INTRA were calculated separately for samples measuring arbitrarily less than or equal to 10 mm 2 (small samples) and for samples of more than 10 mm 2 (regular samples). Analyses were performed using R software version 4. 0. 4  18 with the Agreement  19  and irrCAC  20  packages added, and the Knime Analytics Platform release 4. 2. 3. 21, 22 For κ and CCC-based agreement evaluation, the classification given by Landis and Koch  23  was used: κ from 0. 00 to 0. 20 for slight agreement; from 0. 21 to 0. 40 for fair agreement; from 0. 41 to 0. 60 for moderate agreement; from 0. 61 to 0. 80 for substantial agreement; and over 0.