dataesr/scientific-paragraphs-categorization huggingface dataset:
Aspects of the inversion approach The experience during this campaign highlighted a strong dependence of the solution from the smoothing constraints used to perform the inversion. In Sect. 4 we present a detailed analysis of our methodological approach towards defining the optimum smoothing constraints based on objective criteria, but we recognize that there are few aspects that could be addressed in addition. First, the definition could also be based on a synthetic dataset for which the solution is known. Moreover, there should be a dedicated study on whether it is possible to have a unique setting of constraints for the whole network or to define the settings based on the characteristics of the measurements (e. g. , aerosol type, load, vertical distribution, temporal variability). Regarding the sensitivity of the retrieval on the first guess used for the inversion, it was found to be not that significant, compared to the corresponding sensitivity of the smoothing constraints.
Although herein we have used the first guess provided as the default option for GRASP/GARRLiC retrieval, it may be preferable to define a first guess closer to the solution based on climatological data and/or previous days solutions. We should highlight again that although the GRASP/GARRLiC retrieval was found to be sensitive to both setting parameters (i. e. , smoothing constraints and first guess), the resulting variability was well-described by the retrieval uncertainty of the solution, as provided by the algorithm. Thus, the uncertainty provided for GRASP/GARRLiC product also accounts for the uncertainty due to the sensitivity of the algorithm to different setting parameters. Moreover, the retrieval depends also on the input dataset, and issues may arise from the way the low-quality data are handled.
Herein we removed the low-quality data (i. e. , lidar signals with noise >50% or cases where the sky-radiances from the sunphotometer were recorded in a small number of viewing angles) from the input dataset (losing completely the information), and for all other data we accounted for their quality through the covariance matrix of the observations. In future studies we plan to screen out low-quality observations that do not provide any information to the retrieval, but incorporate the information of the low-quality data that contain useful information for the retrieval (providing their quality through the covariance matrix). The definition of the corresponding quality thresholds will be derived through inversion of synthetic datasets of measurements. Regarding the covariance matrix, herein we assumed it to be diagonal, with a constant relative uncertainty for each type of observation (i.
However, the EARLINET SCC products are derived using the Klett-Fernald algorithm, which is an independent method as it does not depend on the forward model or the numerical inversion approach utilized within GRASP/GARRLiC and thus can be considered as an evaluation for the latter. For the columnar properties, although AERONET and GRASP algorithms share many similarities, the additional information coming from the lidar signals is missing from the stand-alone sun-photometer inversions. In future studies we plan to augment the datasets used for the evaluation of our results, using e. g. (ground-based and/or airborne) in-situ measurements.
We expect that the lidar information does not significantly influence the retrieval of aerosol columnar microphysical properties. This is observed for several cases, however there are few cases where a change in the size distribution, in particular of the smaller particles, is obtained combining the instruments with respect to the sun-photometer-only (AERONET) product. A possible explanation may be the additional spectral information introduced by the lidar measurements at 355 nm, or it may be a bias due to the assumption of constant atmospheric density profile. It should be noted that the above conclusions are obtained considering the capability of reproducing correctly the input measurements. For an ill-posed problem optimizing the inversion based uniquely on the capability to reproduce the input measurements should be further supported by evaluation of the results with independent datasets (e.
Acknowledgements We are grateful to EARLINET (https://www. earlinet. org/, last access: 12 April 2023) and ACTRIS (https://www. actris. eu, last access: 12 April 2023) for the data collection, calibration, processing and dissemination. The authors would like to acknowledge the use of GRASP inversion algorithm software (http://www. grasp-open. com) in this work. We thank the https://doi. org/10. 5194/amt-2023-76 Preprint. Discussion started: 12 June 2023 c Author(s) 2023. CC  BY 4. 0 License.
Protocol for automated multivariate quantitative-image-based cytometry analysis by fluorescence microscopy of asynchronous adherent cells Here, we present a protocol for multivariate quantitative-image-based cytometry (QIBC) analysis by fluorescence microscopy of asynchronous adherent cells. We describe steps for the preparation, treatment, and fixation of cells, sample staining, and imaging for QIBC. We then detail image analysis with our open source Fiji script developed for QIBC and present multiparametric data visualization. Our QIBC Fiji script integrates modern artificial-intelligencebased tools, applying deep learning, for robust automated nuclei segmentation with minimal user adjustments, a major asset for efficient QIBC analysis.
Using a data visualization software, the protein parameters can be cross-analyzed in the context of the cell-cycle at the individual cell level, thus providing in depth information from asynchronous cell populations. The detection by immunofluorescence technics of gH2AX and 53BP1 signals according to cell-cycle stage has provided important information about the molecular processes underlying the DNA damage response. 21, 22  For this the QIBC pipeline solution was applied to gH2AX and 53BP1 signal analysis.
Fiji installation and plugins implementation Timing: 30 min (for steps 11 to 16) The Fiji software is installed and set for the Stardist plugin. 11. Download the Fiji software at https://imagej. net/software/fiji/downloads. 12. Download Fiji updates, go to Menu>Help>Update. 13 Images are analyzed by Fiji with in-house developed scripts (Macros) available for download on the Zenodo platform (https://doi. org/10. 5281/zenodo. 8014997). Multiple parameters are extracted from the automatically defined nuclei ROI on the selected channels. Then, for the protein of interest, the mean signal intensity per nucleus is extracted along with the protein foci count per nucleus, with the find maxima function in Fiji, when discrete foci are formed. The measured parameters are exported in an Excel file. 17.
Download the ''Macros. zip'' file containing the Fiji-script ''00_Convert Files to TIF''. Note: Fiji-scripts for QIBC analysis require ''. tif'' or ''. TIF'' image files extensions. Microscope company provide different type of image file saving formats. For this reason we propose the open source Fiji-script ''00_Convert Files to TIF'' adapted from a script developed by Olivier Burri, BioImaging and Optics Platform -EPFL, to convert your microscope file (. LIF;. CZI;. VSI;. ND2) into individual ''. tif'' image files to fit the requirement for QIBC analysis by two in house developed Fiji-scripts. Each image channel is saved as a separate image file which name must include the letter ''C'' or ''c'' followed by the channel number (C1, C2, C3, C4 or c1, c2, c3, c4). Image analysis for QIBC experiment is done on 2D images (no Z series are accepted).
Note: The in house developed Fiji-script (01_Shading-Dark correction_v2. ijm) applies dark and illumination corrections on all images. 23, 24  The Dark-current image is first subtracted from each sample image and from the shading image. In a second step, an illumination correction image is generated for which an illumination factor for each pixel is determined. For this, the intensity of each pixel of the shading image is divided by the mean pixel intensity of the shading image. The illumination correction image is used to calculate the corrected sample image obtained by dividing the sample image by the illumination correction image. The corrected image files automatically generated by the Fiji-script (01_Shading-Dark correc-tion_v2. ijm) are copied in a new folder distinct from the folder containing the raw image files.
Note: The in house developed Fiji-script (02_StarDist-QIBC_v12. ijm) is applied on all corrected sample images. DAPI channel is used to automatically extract nuclei as region of interest (ROI) with the «Versatile fluorescent nuclei » model present in the Fiji plugin for StarDist  15  and are added to the ROI manager window. ). ORCA-Fusion Digital sCMOS camera with pixel size of 6. 5 mm 2 was combined with the selected objective for maximum image resolution. The effective pixel size on the image is 0. 1625 mm. Motorized stage enabling automatic capture of multiple fields of view (XY dimension, no Z series are captured). The nD-SCAN module is the software implemented in the imaging software MetaMorphâ (Version 7. 10. 4. 407) to set up the 2D scanning area during multidimensional acquisition.
32. Dark-current images are generated for each experimental wavelength at the end of the acquisition session. Note: Correction to remove the noise intensity value coming from the electronic of the camera requires dark-current images for the execution of the Fiji-script ''01_Shading-Dark correc-tion_v2. ijm'' (step 35). Dark-current images are generated for each experimental wavelength by averaging three frames and are acquired for each wavelength with the fluorescent lamp switch OFF with the exposure time used during samples acquisition.
Part 5: Image analysis and data extraction Timing: 2 h per condition Note: Proceed directly to step 34 if the images files extensions are ''. tif'' or ''. TIF'' and that each channel is saved as a separate image file which name includes the letter ''C'' or ''c'' followed by the channel number (C1, C2, C3, C4 or c1, c2, c3, c4). 33. Execute the Fiji-script ''00_Convert Files to TIF''. a. drag and drop the script file in Fiji and click on the RUN button. b. Select the folder containing the microscope file. c. Select from the list the type of microscope image files. d. A folder is automatically created where each channel image file is saved individually with the extension (c1, c2, c3, c4). From this step, you can start the analysis with the two Fiji-scripts for QIBC analysis. Note: If foci count per nucleus is to be extracted with the ''Number of Foci/Spots'' Fijiscript function, then proceed with step 34 to determine the prominence value that is the value used to define local maxima where a maxima is considered as a foci/spot.
Otherwise go directly to step 35. the Find Maxima function. f. Repeat this process on 4 to 5 channel images of an experimental condition to confirm that the selected prominence value allows for the detection of foci/spot. g. Verify that the selected prominence value is not leading to fauls positive foci/spot by running the Find Maxima function on the experimental negative control images (samples where the EdU pulse was omitted or sample where the primary antibody was omitted). h. Repeat steps 34 for all channel images from which the foci/spot count is to be extracted. 35. Execute the Fiji-script ''01_Shading-Dark correction_v2. ijm''. a. drag and drop the script file in Fiji and click on the RUN button. b. Define the analysis settings in the opening dialogue box: i. Select the image folder using the "Browse" menu.
Note: The corrected image files are copied in the sub-folder ''Corrected'' automatically created by the Fiji-script in the folder containing the raw image files. 36. Execute the Fiji-script ''02_StarDist-QIBC_v12. ijm''. a. Drag and drop the script file in Fiji and click on the RUN button. b. Define the analysis settings in the opening dialogue box: i. In the ''Image directory'' select the ''Corrected'' image folder generated step 35 using the "Browse" menu. ii. Specify the name of the analyzed condition (e. g. , Untreated, Irradiated).
Finally, our observations recapitulate previous studies  12, 37  showing that the number of 53BP1 foci normalized to nucleus DNA content is higher in G1 and gradually decline as cells progress through the cell cycle, in both untreated and IR-treaded cells (Figures  3  and 4 ). Overall, our QIBC pipeline protocol including publicly made available QIBC-scripts for Fiji, is providing a rapid and automated analysis with minimal end-user intervention for unbiased nuclei segmentation and multi-parameter extraction.
LIMITATIONS Unbiased multi-parameters extraction for QIBC analysis relies on robust single nucleus segmentation a function provided by the Fiji-plugin for StarDist that is implemented in the open source QIBC-script for Fiji presented here. StarDist includes multiple deep-learning based models for automated objects segmentation. Applying the ''versatile (fluorescent nuclei)'' built-in model with default parameters is well adapted for the automated nuclei segmentation of cell lines with ovoid-round nuclei such as the U-2 OS or RPE1-hTERT cells. However, the authors have not yet applied the script to other cell lines. Users who wish to apply this QIBC pipeline to cell lines with The cross-analysis of DAPI total intensity per nucleus and mean EdU intensity per nucleus parameters allows for cell-cycle staging of individual cells from asynchronously growing adherent cells.
Additional information about proteins of interest such as the mean signal intensity per nucleus or protein foci count per nucleus can be extracted and cross analyzed with cell-cycle parameters. Foci count per nucleus can be extracted using the Fiji Find Maxima function with a user defined prominence value. However, the identification of discrete foci in samples showing high intensity, high foci density or heterogeneous foci intensity and density is challenging. This is illustrated in the camptothecin treated sample resulting in global nuclear staining for gH2AX in S-phase cells (see Figure  2 ). Therefore, users are advised to apply the ''Number of Foci/Spots'' Fiji-script function with careful consideration. In this protocol, the Spotfireâ software (TIBCO) was used to generate the Multivariate data visualizations presented in the figures.
ACKNOWLEDGMENTS This study was supported by grants from the  Institut Curie , the  CNRS , and the  Fondation LIGUE Nationale Contre Le Cancer ''Equipe Labellise  ´e  2020  ( EL2020LNCC/Sal ) to S. A. E. L. The equipment used in this work was supported by the  Fondation pour la Recherche Medicale  ( FRM N DGE20111123020 ), by the  "Dispositif d'aide urgente aux laboratoires pour financement   immediat ,.
Fluor  488 5-Carboxamido- (6-Azidohexanyl),  Bis ( Triethylammonium Salt )), 5-isomer)  Thermo Fisher Scientific Cat #  A10266 4 0  , 6-Diamidine-2 0  -phenylindole dihydrochloride (DAPI)  Merck Sigma-Aldrich Cat #  D9542-5MG ; CAS:  28718-90-3 1 , 4-Diazobicyclo-[2. 2. 2]-octane (DABCO)  Merck   Sigma-Aldrich Cat #  D27802-25G ; CAS:  280-57-9  Glycerol, anhydrous,  R99. 5% (GC)  Honeywell  Cat#  49770 ; CAS:  56-81-5 Mowiolâ 4-88 Merck   Sigma-Aldrich Cat #  813381-50G   UltraPureä DNase/RNase-Free Distilled water Thermo Fisher Scientific Cat #  10977-035  Continued  REAGENT  or  RESOURCE SOURCE IDENTIFIER Fiji  plugin for  StarDist -Object Detection with Star-convex Shapes. net/StarDist Fiji plugin for CSBDeep (Require to use StarDist) https://github. com/CSBDeep/CSBDeep_fiji SpotFire (Desktop Version 11.
Data and code availability The open-source Fiji-script reported in this study (00_Convert Files to TIF) and the in house developed Fiji-scripts (01_Shading-Dark correction_v2. ijm) and (02_StarDist-QIBC_v12. ijm) are available from the open repository Zenodo. Additional files available from the open repository Zenodo, include corrected image files from each of the experimental condition described in this protocol article and generated from the raw images using the Fiji-script (01_Shading-Dark correction_ v2. ijm). Corrected images are ready for data extraction using the Fiji-script (02_StarDist-QIBC_ v12. ijm), the corresponding excel file generated by this Fiji-script (Corrected Images and QIBC analysis. zip) are also available. A solution with ready to use template (Canevas Spotfire) accompanied by instructions (Spotfire_Application to QIBC) applicable for data visualization and cross parameter analysis is proposed.
ijm) from Olivier Sutter Instrumentâ Semrockâ/AHF Cat# L200S Cat# SCAN IM 127x83 Zenodo DOI (https://doi. org/10. 5281/zenodo. 8014997) Cat# LB10-3 https://gist. github. com/lacan/16e12482b52f5397 95e49cb2122060cc Cat# F66-988 Fiji-script (01_Shading-Dark correction_v2. ijm) Band-pass filter for DAPI excitation AT350/503 Fiji-script (02_StarDist-QIBC_v12. ijm) Canava (SpotFire) for QIBC multivariate Band-pass filter for FITC excitation ET490/203 representations Step-by-step Fiji-script tutorials and video Band-pass filter for CY3 excitation ET555/253 This paper Chroma Technology/AHF This paper analysentechnik AG This paper Chroma Technology/AHF analysentechnik AG This paper analysentechnik AG Chroma Technology/AHF Cat# F34-000SG Zenodo DOI (https://doi. org/10. 5281/zenodo. 8014997 Zenodo DOI (https://doi.
org/10. 5281/zenodo. 8014997 Cat# F49-490SG Zenodo DOI (https://doi. org/10. 5281/zenodo. 8014997 Zenodo DOI (https://doi. org/10. 5281/zenodo. 8014997 Cat# F49-555SG Experimental models: Cell lines Band-pass filter for CY5 excitation ET645/303 Chroma Technology/AHF Cat# F49-645SG U-2 OS analysentechnik AG RRID: CVCL_0042 or RESOURCE Oligonucleotides Band-pass filter for DAPI emission ET455/50m SOURCE analysentechnik AG Chroma Technology/AHF IDENTIFIER Cat# F47-456 Antibodies Non-targeting Pool siRNA (siCTL) Band-pass filter for FITC emission ET525/36m Dharmacon Chroma Technology/AHF Cat# D-001810-10-05 Cat# F47-526 Mouse monoclonal antibody anti-H2AX-phospho-serine139 (anti-gH2AX) IgG1k, clone JBW301, ON-TARGET plus Smart pool Human TP53BP1 siRNA (si53BP1) Band-pass filter for CY3 emission ET605/52m Merck Sigma-Aldrich Dharmacon analysentechnik AG Chroma Technology/AHF Cat# L-003548-00-0010 Cat# 05-636-I; RRID:AB_2755003 Cat# F47-606 use at 1:500 dilution Software and algorithms analysentechnik AG Mouse monoclonal antibody anti-p53 Binding MetaMorph Microscopy Automation and Image Band-pass filter for CY5 emission ET705/72m Merck Sigma-Aldrich Molecular Devices Chroma Technology/AHF Cat# MAB3802; RRID:AB_2206767 RRID:SCR_002368 Cat# F47-705 Protein 1 (anti-53BP1) IgG1, clone BP13, use Analysis Software (Version 7.
Functional diversity is widely used and widespread. However, the main packages used to compute functional diversity indices are not flexible and not adapted to the volume of data used in modern ecological analyses. We here present fundiversity, an R package that eases the computation of classical functional diversity indices. It leverages parallelization and memoization (caching results in memory) to maximize efficiency with data with thousands of columns and rows. We also did a performance comparison with packages that provide analog functions. In addition to being more flexible, fundiversity was always an order of magnitude quicker than alternatives. fundiversity aims to be a lightweight, efficient tool to compute functional diversity indices, which can be used in a variety of contexts. Because it has been designed following clear principles, it is easy to extend.
Introduction Functional diversity, the diversity of traits across scales, is a major facet of biodiversity  (Pavoine and Bonsall 2011). It has been widely used across ecological contexts  (Cadotte et al. 2011 ) and has been shown to relate to ecosystem functioning  (Díaz and Cabido 2001; Leps et al. 2006). Many indices exist to characterize it across its three dimensions: richness (how much?), evenness (how regular?) and divergence (how different?) (Pavoine and Bonsall 2011). Ecologists rely on computational tools to compute these indices in a reproducible fashion, mainly in the R programming language  (Lai et al. 2019; www. r-project. org). The FD package is the main tool available for functional diversity indices, having now accumulated more than 1200 citations  (Laliberté et al. 2014). But FD was released in 2009 and received only minor updates that stopped in 2015.
During this time, software development practices have changed dramatically and new, higher-performance tools have emerged in the R ecosystem. Additionally, since 2009, the size of ecological datasets has grown exponentially  (Farley et al. 2018 , Wüest et al. 2020 ) and high-performance computing (HPC) environments have become standard. There is, therefore, a dire need for a modern alternative using state-of-the-art software development techniques and tools. The main function of the FD package dbFD() lets users compute a dozen functional diversity indices in a single call from raw trait data  (Laliberté et al. 2014). While great for exploratory analyses, this can increase computation time when only a single index is needed. Furthermore, it does not enforce the good practice of choosing beforehand the appropriate functional diversity index for the question(s) asked  (Schleuter et al.
2010 , Mason et al. 2013 , Legras et al. 2018). It encourages the user to fish for the functional diversity index matching predicted relationships (a form of p-hacking). This can lead users to report all computed functional diversity indices even when there are no clear expectations about different functional diversity facets, or to report correlated indices  (Schleuter et al. 2010 , Mason et al. 2013 , Legras et al. 2018 , McPherson et al. 2018). Computing all indices in a single function also makes long-term maintenance and addition of new indices harder. The average size of datasets analyzed in ecology increased severalfold in recent years  (Wüest et al. 2020) , calling for an increase in performance of computational tools. This increase is particularly urgent considering that many diversity analyses use null models that increase the data size by two or three orders of magnitude  (Gotelli and Graves 1996).
Discussions are increasingly being held regarding scientific software robustness and reliability in ecology  (Poisot 2015 , White 2015 , Mislan et al. 2016 , Wilson et al. 2017). Mainly because most ecologists are self-trained in programming  (Farrell and Carey 2018) , these virtuous practices are rarely applied in ecology  (Barraquand et al. 2014). For example, unit tests use predefined inputs to compare outputs to expectations  (Poisot 2015). Unit tests have also become standard in R packages since the release of packages streamlining this process, such as testthat and tinytest  (Wickham 2011 , van der Loo 2021). Very few R functional diversity packages provide unit tests to assess that the functions behave expectedly. Automatic tests of one's code are crucial when developing a tool for a wider audience, as it may be used across different contexts.
These choices have been shown to have strong consequences on the values of downstream indices  (Leps et al. 2006 , de Bello et al. 2013 , Maire et al. 2015 , Mammola et al. 2021). fundiversity chooses to enforce better practices by leaving trait space choices to the user. Parallelization can vastly decrease computation speed by leveraging the architecture of modern computers. Most functions in fundiversity can be parallelized out of the box. fundiversity provides parallelization through the future backend  (Bengtsson 2020). Parallelization is toggled through a single function call using future::plan() before using fundiversity functions. Thanks to the flexibility given by the future backend, the code to use will not change whether parallelizing across several cores on a single computer, across multiple computers or on a remote high-performance cluster.