dataesr/scientific-paragraphs-categorization huggingface dataset:
Second round of interviews Based on the analysis of the initial interviews, we created a qualitative data collection grid that was proposed in 2015 for 42 participants (2 out of the 44 dropped out). This grid, covering the 8 themes and the 3 levels of "relations to" was completed both by the interviewee and the interviewer. All interviews and exchanges around the grid were recorded and transcribed in full, which, in case of distances in the responses, made it possible to listen to each speech again. The coding and analysis were subjected to a triangulation process carried out by three members of the research team (DB, JC, MBD). The data from the 44 interviews and the second round's grids of interviews of behaviour at home were crossed-referenced in two ways: for each participant (which made it possible to produce case studies of participants), but also by speci c themes (food, physical activity etc.
Funding This received a grant from  IRESP ,  French Research Institute in Public Health  ( AAP ETP 2013-03 ), and the ERMIES trial was funded by  PHRC interregional ,  French Inter-regional Hospital Program for Clinical Research 2010 (GIRCI Sud Ouest OutreMer ;  French Ministry of Health ). The sponsor institution of the study is the  Regional Teaching Hospital of La Réunion, France. The funders bodies had no role in the design of the study and collection, analysis, interpretation of data and in writing the manuscript.
Neovascular AMD was diagnosed if the following criteria were met, as previously reported  2, 12   Primary outcome measure. The study primary outcome was the occurrence of MNV in the context of either pachychoroid disease or AMD during the observational period. The diagnostic labels recorded on the Heidelberg OCT database from both study centers (i. e. "PNV", "CNV in pachychoroid", "MNV in pachychoroid", "MNV in CSC", "CNV in CSC", "neovascular AMD", "CNV in AMD", and "MNV in AMD") were used to detect the patients of interest and matched with the diagnosis reported on the clinical records of the patients. Structural OCT and dye angiography (FA and ICGA) images were further reviewed by two independent retina specialists from both centers (P. A. and G. M. C. at Creteil University Eye Clinic, and E.
Statistical analysis. All data were analyzed using SPSS software (IBM SPSS Statistics 26. 0, Ontario, Canada). Shapiro-Wilk test was used to determine assimilability to normal distribution. Univariate comparison between the two groups' demographic characteristics was performed using two-tailed Student's t-test for independent groups and χ 2 test for quantitative and qualitative variables, respectively. For the analysis of the selected covariates (see above), continuous data (age and choroidal thickness) were transformed into categorical variables. Odds ratio calculation was performed to identify the predictors of MNV secondary to pachychoroid and univariate comparison between the two groups was performed using the χ 2 test for categorical data or Fishers Exact Test. Categorical variables shown to be significant with a Bonferroni corrected p < 0.
Results Forty-eight right eyes from 48 patients with MNV secondary to pachychoroid and 39 right eyes from 39 patients with neovascular AMD were extracted from the electronic database of Creteil University Eye Clinic and University Vita-Salute San Raffaele and considered in this study. All patients were Caucasian and the baseline demographical characteristics of enrolled patients are summarized in Table  1. A statistically significant difference between the two study groups was detected in terms of age, sex, SFCT and vPDT treatment. Among the investigated factors, age, sex, choroidal thickness, MNV type, sub-retinal fluid, intra-retinal fluid, early pachyvessels, and evidence of MNV at OCTA were shown to be significant risk factors for pMNV by univariate analysis (Table  2 ). Binomial logistic regression was performed to ascertain the effects of these factors on the likelihood that participants have pMNV rather than neovascular MNV (Table  3 ).
The repository presents a framework for: • Detecting 2D joint coordinates from videos, e. g. via OpenPose  (Cao et al. , 2019)  • Calibrating cameras • Detecting the person of interest • Triangulating 2D joint coordinates and storing them as 3D positions in a. trc file • Filtering these calculated 3D positions • Scaling and running inverse kinematics via OpenSim  (Delp et al. , 2007; Seth, 2018) , in order to obtain full-body 3D joint angles Each task is easily customizable, and requires only moderate Python skills. Pose2Sim is accessible at https://github. com/perfanalytics/pose2sim.
Statement of need For the last few decades, marker-based kinematics has been considered the best choice for the analysis of human movement, when regarding the trade-off between ease of use and accuracy. However, a marker-based system is hard to set up outdoors or in context, and it requires placing markers on the body, which can hinder natural movement  (Colyer et al. , 2018). The emergence of markerless kinematics opens up new possibilities. Indeed, the interest in deep-learning pose estimation neural networks has been growing fast since 2015  (Zheng et al. , 2022) , which makes it now possible to collect accurate and reliable kinematic data without the use of physical markers. OpenPose, for example, is a widespread open-source software which provides 2D joint coordinate estimates from videos.
These coordinates can then be triangulated in order to produce 3D positions. Aside from Pose2Sim, a number a tools are available for such triangulation: the experimental OpenPose 3D reconstruction module  (Hidalgo, 2021) , the FreeMoCap Python and Blender toolbox  (Matthis & Cherian, 2022) , and the pose3d Matlab toolbox  (Sheshadri et al. , 2020). Yet, when it comes to the biomechanical analysis of human motion, it is often more useful to obtain joint angles than joint center positions in space. Joint angles allow for better comparison among trials and individuals, and they represent the first step for other analyses such as inverse dynamics. So far, little work has been done towards obtaining 3D angles from multiple views  (Zheng et al. , 2022). However, three software applications are worth mentioning.
Anipose  (Karashchuk et al. , 2021)  proposes a Python open-source framework which allows for joint angle estimation with spatio-temporal constraints, but it is primarily designed for animal motion analysis. Theia3D  (Kanko et al. , 2021 ) is a software application for human gait markerless kinematics. Although the GUI is more user friendly, it is neither open-source nor customizable. OpenCap  (Uhlrich et al. , 2022)  has recently been released, and offers a user-friendly web application working with low-cost hardware. It predicts the coordinates of 43 anatomical markers from 20 triangulated keypoints, and imports them in OpenSim. However, the source code has not yet been released. The goal of Pose2Sim is to build a bridge between the communities of computer vision and biomechanics, by providing a simple and open-source pipeline connecting the two aforementioned state-of-the-art tools: OpenPose and OpenSim.
The whole workflow runs from any video cameras, on any computer, equipped with any operating system (although OpenSim has to be compiled from source on Linux. ) Pose2Sim has already been used and tested in a number of situations (walking, running, cycling, dancing, balancing, swimming, boxing), and published in peerreviewed scientific publications assessing its robustness  (Pagnon et al. , 2021)  and accuracy  (Pagnon et al. , 2022). Its results for inverse kinematics were deemed good when compared to marker-based ones, with errors generally below 4. 0°across several activities, on both lower and on upper limbs. The combination of its ease of use, customizable parameters, and high robustness and accuracy makes it promising, especially for "in-the-wild" sports movement analysis.
Pose2Sim workflow Pose2Sim connects two of the most widely recognized (and open-source) softwares in their respective fields: • OpenPose  (Cao et al. , 2019) , a 2D human pose estimation neural network • OpenSim  (Delp et al. , 2007) , a 3D biomechanics analysis software The workflow is organized as follows (Figure  1 ): 1. Preliminary OpenPose  (Cao et al. , 2019)   (Delp et al. , 2007)  skeletal model with OpenPose keypoints is provided, as well as scaling and inverse kinematics setup files.
2D keypoint detection It is possible to define which 2D pose estimation model is used. This choice will affect how keypoint indices will be mapped to model markers in OpenSim, corresponding to anatomical landmarks or joint centers. Only 21 of the 25 keypoints detected by the default OpenPose models are tracked, since eye and ear keypoints would be redundant in the determination of the head orientation. The OpenPose BODY_25B experimental model is recommended, as it is as fast as the standard BODY_25 model, while being more accurate  (Hidalgo, 2019). Non-OpenPose models can also be chosen, whether they are human (such as the AlphaPose one  (Fang et al. , 2017) ), or animal (such as any DeepLabCut model trained by the user  (Mathis et al. , 2018) ). Two optional standalone scripts are also provided if the user desires a visual display of the resulting 2D pose estimation (Figure  2 ).
OpenSim scaling and inverse kinematics The main contribution of this software is to build a bridge between OpenPose and OpenSim. The latter allows for much more accurate and robust results  (Pagnon et al. , 2022) , since it constrains kinematics to an individually scaled and physically accurate skeletal model. Bones are constrained to a constant length, and joints to coherent angle limits. The provided model is adapted from the human gait full-body model  (Rajagopal et al. , 2016)  and the lifting full-body model  (Beaucage-Gauvreau et al. , 2019). The first one has a better definition of the knee joint: abduction/adduction and internal/external rotation angles are constrained to the flexion/extension angle. The latter has a better definition of the spine: each lumbar vertebra is constrained to the next one, which makes it possible for the spine to bend in a coherent way with only a few tracked keypoints, without having to make it a rigid single bone.
The eyes of P6 pups were prefixed in 4% PFA for 8 min at room temperature. Retinas were dissected, blocked for 30 min at room temperature in blocking buffer (1% fetal bovine serum, 3% BSA, 0. 5% Triton X-100, 0. 01% Na deoxycholate, 0. 02% sodium azide in PBS at pH 7. 4) and then incubated with specific antibodies in blocking buffer overnight at 4 C. The next day, retinas were washed and incubated with IB4 and/ or ERG (or DAPI) together with the corresponding secondary antibody overnight at 4 C, then washed and post-fixed with 1% PFA and mounted in fluorescent mounting medium (DAKO, USA). High-resolution pictures were acquired using Leica SP8 confocal microscope with a Leica spectral detection system (Leica TCS SP8 detector), and the Leica application suite advanced fluorescence software. After segmenting each channel corresponding to the Golgi and nuclear staining, the centroid of each organelle was determined and a vector connecting the centre of the nucleus to the centre of its corresponding Golgi apparatus was drawn.
The Golgi-nucleus assignment was done automatically minimizing the distance between all the possible couples. The polarity of each cell was defined as the angle between the vector and the scratch line. An angular histogram showing the angle distribution was then generated. Circular statistics were performed using a cell polarization toolbox (https://github. com/batho2n/ec_polarization). To measure the distribution of ECs within each experiments, we created graphs representing the vascular plexus in each experiment by skeletonising images of IB4 staining via the software tool PolNet using methods previously described  (Bernabeu et al. , 2018). Images were prepared and processed using Fiji  (Schindelin et al. , 2012) , GIMP (GNU Image Manipulation Program, 2. 10. 12), and MATLAB (MathWorks, R2020a).
Graphs were simplified to only include edges which formed an angle greater than 30 °, and the capillary plexus was manually segmented out from the arteries and veins. Nuclei positions were calculated by binarizing images of Erg staining. Each nuclei was assigned to a vessel edge based on the minimum distance from the centroid to edge midpoints, yielding a dataset of cell number amongst vessel edges for each experiment. We then calculated the max cell number and variance in cell number amongst vessel edges and normalised by the mean cell number for each experiment, providing measures that were comparable to the features MC and VC in the computational data (see feature definition below). We also measured the mean edge length and total number of cells in each experiment. Statistical significance between measurements in Alk1 iKO plexuses vs.
2) We used the vmtkcenterlinemodeller tool to generate a 3D rendering of each 2D honeycomb network and 3) then the vmtksurfaceremeshing tool to produce a closed structured surface mesh. 4) To remove the caps over inlets and outlets, we used the vtkClipPolyData tool to produce a series of open structured meshes of an idealised vascular network. We used the HemeLB software  (Mazzeo and Coveney, 2008)  to simulate blood flow through each network in the sequence of static meshes, allowing us to examine the dynamic changes in the fluid flow as Vessel A collapsed. HemeLB is a high-performance lattice-Boltzmann solver developed to simulate blood flow through complex static 3D vascular geometries. The lattice-Boltzmann method is built on microscopic models and discretised mesoscopic kinetic equations to recreate the dynamics of incompressible fluid flow on a regular lattice.
FUNDING This work was produced as part of the  Foundation Leducq Transatlantic Network of Excellence  ( 17 CVD 03 , https://www. mdc-berlin. de/leducq-attract). JC is supported by an  Australian Government Research Training Program (RTP) Scholarship. MB is supported by grants from the  Engineering and Physical Sciences Research Council (EPSRC)  ( EP/R029598/1 ,  EP/T008806/1 ),  Fondation Leducq  ( 17 CVD 03 ), the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  801423 ,  Diabetes United Kingdom  ( 20/0006221 ),  Fight for Sight  ( 5137/5138 ), and  British Heart Foundation  and The Alan.
The intensities of the various isotopologs within an isotope cluster therefore depend on the elemental composition of the peptide and follows a Poisson distribution that can be accurately modeled using dedicated software, such as MIDAS  [7]. The basic principle of SLIM labeling is to manipulate the elemental composition of proteins in vivo from the natural abundance of the isotopes of the atoms present in proteins (C, H, N, O, S, P), defining the "NC" (natural carbon) condition as the condition named "12C" in which the proteins are enriched in the light isotope of carbon ( 12 C) and, eventually, nitrogen ( 14 N) (referred as to "12C14N" condition). Considering the main routes for amino-acid biosynthesis in yeast (Fig. 1 )  [8] , we hypothesized that providing yeast cells with U-[ 12 C]-glucose as the sole carbon source would result in the rapid synthesis of U-[ 12 C]- amino acids and their incorporation into newly synthesized proteins.
One critical step in the SLIM-labeling quantification procedure is the accurate extraction of the intensities for all isotopologs in each isotope cluster from the experimental spectra. In our initial study, we used commercial software, Progenesis QI for metabolomics, but it does not provide the possibility to automatically link the quantification files with the identification files  [5]. This prompted us to develop another workflow, referred to as bSLIM  [9] , in which only the intensities of the identified peptides are used to extract the data using an OpenMS node, FeatureFinderIdentification  [9] , which was modified to fit every mass trace in the isotope cluster. This approach only required us to install and run the KNIME (Konstanz Information Miner) environment for computation  [9] , together with the latest versions of OpenMS  [10] [11] [12] , and hence is fully independent of any commercial software.
Here, we present an alternative integrated procedure that takes advantage of the tools available in the proprietary software suite Thermo Scientific Proteome Discoverer. Proteome Discoverer (PD v2. 4) is a popular program for the analysis of peptide-centric proteomics data, with a high level of integration with Thermo Fisher Scientific high-resolution, high-sensitivity Orbitrap instruments. This analytical platform includes many algorithms developed by Thermo or third parties, such as the IMP Protein Chemistry facility (https://pd-nodes. org/) [13, 14]  and others. Proteome Discoverer is therefore widely used on a routine basis in many MS-based proteomics laboratories. It associates and integrates both raw spectra processing and filtering, peptide identification through database searches in data-dependent analyses, diverse quantification routines, and convenient spectra viewers.
The output of the Proteome Discoverer analyses is written in ". msf" or ". pdresult" files. A key feature of ". pdresult" files is that they are SQLite relational databases (https://www. sqlite. org) that can be queried using SQL. The possibility to visualize individual annotated spectra for peptides up to the level of their isotope clusters with their associated intensity prompted us to develop the appropriate tools to extract this data and use it as input for our bSLIM labeling quantitative proteomics strategy. We accessed the individual mass trace intensities by taking advantage of the capabilities of the newly developed node, Minora, initially designed for label-free quantification. We also present a solution to assess the robustness of the protein quantification scores calculated using bSLIM which was missing from our previous data analyses workflow.
The proteomes of the two strains are expected to be very similar and therefore represent a challenging test to assess the sensitivity and specificity of our quantification methods. Overall, we expect that these alternative solutions implemented in the bSLIM data analysis workflow will be useful for proteomics laboratories running Orbitrap-based mass spectrometers, which are very familiar with Proteome Discoverer. This is an original way to combine the completeness and reproducibility of routine proprietary software with the power of open-source tools. 11. Low-binding microcentrifuge tubes. 12. 4-12% polyacrylamide gradient gels. 13. Coomassie blue (MS friendly, such as SimplyBlue SafeStain, Invitrogen). 14. Bradford protein assay reagent. 15. An instrument setup for LC-MS/MSMS data acquisition (see Note 2).
5. Carefully measure the protein concentration using the Bradford Protein microassay and validate the protein measurement by running an aliquot on an SDS-PAGE gel and staining with Coomassie Blue. 6. Precipitate a 50-μg protein aliquot using 6 vol. cold acetone for 2. 5 h at À20 C. 9. Remove all solvents by vacuum drying. 10. Resuspend the peptides in 0. 1% formic acid. 11. Carefully mix an equal amount of peptides from the NC-and 12C-conditions. 12. Inject the samples, typically 5 μg in 5 μL, into the LC-MSMS instrumental setup (see Note 4). 13. Ensure that your instrumental setup allows the isotopic resolution of all the peptides analyzed (see Note 5). 14. Save the ". raw" files for data processing and signal extraction. 15. Create a folder to gather all the ". raw" files from one project together.
The workflow is a modification of the original workflow presented in our previous study  [9]. The adaption is very simple (disconnection between metanode 1. 2 "FFiDData filtering" and connection with the Row filter "erase ModifiedPeptides"). This workflow contains three main parts. (a) The ". pdresult" file, which extracts all data concerning every peptide, including their identification and the intensity of the isotopologues in the isotope clusters. This procedure uses an R script written specifically for this study. It is embedded in a dedicated RSnippet as a part of the KNIME quantification workflow (see Note 8 on SQL request formalism). (b) The peptide/protein quantification workflow based on our previous study  [9]. As previously described, two biological conditions are considered: complete labeling of the proteins (true wild-type strain) or partial labeling of the proteins (strains that are auxotrophic for specific amino acids).
Statistics and Graphical Assessment of Score Significance For statistical analysis of differential expression, we reproduced the SAM methodology, adapting it to the specifics of the quantitative measurements of protein abundance that are obtained at the end of the bSLIM workflow (Fig. 5 ) (see  Note 10). Workflows available at https://zenodo. org (DOI 10. 5281/zenodo. 4467882). 1. Aggregate the protein quantification results from individual experiments (replicates) into a single ". tsv" table: Accession/ "name of column-2"/ "name of column-3"/. / "name of column-n. " Typically, column-2 to -n represents the log2(Fold change) of protein abundance per experimental condition. 2. Load the R scripts developed in this study to compute the scoring functions, and save the analysis. 3. Use the graphical package ggplot2, within the script, to produce the figures showing differentially expressed proteins.
Significant scores are those that are higher (colored in blue) or lower (colored in yellow) than the random scores. False discovery rates are finally calculated, comparing for each protein score, the average number of other scores in the table of sorted values (from each sampling, see (b)) that are higher (respectively lower) than the number of scores that are higher (respectively lower) in the original dataset. This is the same method as detailed in  [15]  C18 precolumn and a Pepmap-RSLC Proxeon C18 column. These devices are all from Thermo Fisher Scientific (Bremen, Germany and San Jose, CA, USA). 3. In the case study presented here, the S. cerevisiae strains S288c (MATα SUC2 gal2 mal2 mel flo1 flo8-1 hap1 ho bio1 bio6)  [16]  and its isogenic derivative BY4742 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0) are grown on a synthetic medium made of 6.
The data are then incorporated in the KNIME workflow. (b) The computation is rapid and can be performed as a side analysis during the bSLIM experiment. In cases of auxotrophy, only the amino acids synthesized by the yeast are labeled, whereas the exogenous amino acids that need to • LcmsPeaks, which is the most important in this study, because it contains the abundance of each individual isotopolog contained in each independent identified isotopic cluster. (b) To extract and produce the final output table from the database (". pdresult"), we created an original workflow to: • Import the ". pdresults" file. • Create explicit path names to access the requested data, using the Uniform Resource Identifier (URI) node of KNIME. • Embed the SQL commands into an RSnippet to retrieve the expected data and create two tables (Fea-ture_Data and Peak_Data in the R code).
These tables are further joined using a connection link between them as an intermediate table used as a "dictionary" of ID equivalents. • Define the ordered rank of the isotopologs extracted by sequential numbering of the lines related to each PSM (Protein Spectrum Match). (c) Within the R script, the complete SQL request for generating Table Feature_Data is as follows: (f) We restrict the number of isotopologs during extraction in the final dataset to five, as we only require M 0 and M 1 for further quantification calculations. 9. The produced table is used in the bSLIM workflow for complete or incomplete labeling with the correct exogenous (nonlabeled) amino acids given in parameters. The code proceeds to the ratio of M 1 over M 0 to quantify the molar fraction, the key variable for the quantification.