dataesr/scientific-paragraphs-categorization huggingface dataset:
The trike was mounted onto a home trainer (In'Ride 100, B'TWIN, Decathlon, Villeneuve d'Ascq, France). All data was handled by a Raspberry Pi (Model 3B, Raspberry Pi Foundation, United Kingdom) using a standard ANT + Dongle (ANTUSB-m, Garmin Ltd. , Olathe, KS, USA), its I2C and GPIO interface. A software platform called IMUSEF (Inertial Measurement Unit pour Stimulation Électrique Fonctionnelle, CAMIN team, Inria, University of Montpellier, France), written in Python (Version 2. 7) was responsible for data processing and controlling an electrical stimulator (FESBox 4, BerkelBike B. V. , Netherlands) at a sample rate of 100 Hz. The software itself was controlled by a separate personal computer (MSI GS60 2PE Ghost Pro, Micro-Star International, Zhonghe District, Taiwan) which allowed for a graphical representation of the analysed data.
In contrast to previous works  [17] [18] [19]  detecting automatic stimulation intervals on motorised ergo-cycles, our approach is not relying on a constant angular velocity provided by a motor and allows to process alternative control signals (i. e. cycling phase measured via IMUs). The algorithm, as shown in Fig. 2 , was integrated into our existing FES-Cycling platform (IMUSEF) as a software module (Python 2. 7). As the main software itself, OIDA operates at a sampling frequency of 100 samples / s. Due to the different delays of the input signals obtained from the power-meter and the IMU sensors, it was necessary to align the recorded data prior to processing. Thus, the cycling phase data was shifted by 40 ms, while the data for crank angle and torque were shifted by 200 ms. The torque distribution of a muscle was recorded throughout a pedalling cycle, while the contralateral leg was passively moved by the investigator.
The net-torque (i. e. active torque minus passive torque) is used to determine an optimal interval (i. e. start and stop value of the crank-angle) for applying stimulation After recording torque data for the specified number of passive cycles, stimulation was switched ON and the same number of active cycles was recorded during continuous electrical stimulation. Due to an expectable sudden movement at the onset of stimulation, the first active cycle is rejected from analysis. To compensate for different cycle durations, each full cycle was interpolated to match a template vector (i. e. crank-angle: 0-360° in 1° steps; cycling-phase: 0-100% in 1% steps). The mean torque of all normalized passive cycles and the mean torque of all active cycles were used to calculate the nettorque (i. e. active contribution of the stimulated muscle).
Each muscle (i. e. left quadriceps LQ, right quadriceps RQ, left hamstrings LH and right hamstrings RH) was tested separately by passively moving the contralateral leg. The algorithm recorded 3 passive and 3 active (with stimulation) cycles at a cadence of 5 RPM with a tolerance of 50% for each muscle. If the examiner was moving the leg too fast (> 7. 5 RPM) or too slow (< 2. 5 RPM) the cycle was rejected. After a successful measurement, the recorded data was graphically presented along with the determined stimulation intervals.
Cadence dependent delay compensation The stimulation pattern obtained by the OIDA describes an interval of crank-angles or cycling-phase-values, at which the corresponding muscles should be active in order to produce a positive net-torque. As these intervals were obtained at a very low cadence, it is necessary to compensate potential delays to allow for smooth cycling at higher cadences. Sensor delays (Delay Sensor ) amounted to: (a) 200 ms for the crank power-meter (i. e. crank-angle) due to the wireless data transmission or (b) 40 ms for the signals obtained by the IMUs (i. e. cycling-phase) due to the Kalman filtering implemented in the BNO055 modules. Furthermore, the muscle itself also requires a certain amount of time (Delay Muscle ) to be active (between 100 and 300 ms  [22] ). In our application we empirically estimated a muscular delay of 200 ms for our pilot.
Validation of stimulation patterns The stimulation patterns (i. e. start and stop values for each muscle group) obtained by the OIDA were validated during a 5 min FES-Cycling test without load on a home trainer. The aim of this test was to determine whether the stimulation patterns obtained by the OIDA allowed for stable and autonomous cycling-i. e. without interventions of the pilot or examiner (e. g. manually pushing the legs to avoid dead points). (1) After manually initiating the cycling motion by the examiner, IMUSEF (control software for FES-Cycling) delivered delay compensated stimulation according to the previously determined pattern. The stimulation amplitude was gradually increased by the pilot to allow for autonomous cycling at a targeted cadence of between 50 and 60 RPM. To counteract fatigue, the pilot was instructed to adjust the stimulation amplitude towards a maximum of 120 mA.
Optimal stimulation intervals A custom-developed algorithm for detection of optimal stimulation intervals was used to determine a stimulation pattern for FES-Cycling involving the quadriceps and hamstrings muscles. Figures  4  and 5  graphically present the results for each investigated muscle group and indicate the optimal intervals for stimulation between the start and stop markers, respectively for crank-angle or cycling-phase based stimulation. Data was obtained from 3 passive (without stimulation) and 3 active (with continuous stimulation) cycles and averaged to reduce potential variation. The corresponding values of the stimulation patterns can be found in Table  1. Please note that the OIDA measurements for the different control signals (i. e. crank angle and cycling phase) were performed at different time points throughout a single measurement day, thus causing differences in the peak values of the measured torques.
Availability of data and materials All required data and material has been included in the manuscript. High quality copies of the figures are submitted in separate files enclosed to the manuscript. The source code (Python 2. 7) for communicating with the power meter 2INPOWER from Rotor as well as an installation guide is made available online at https:// gitlab. inria. fr/ camin-soft/ ant4r otor. The software package allows for connection and configuration of the power meter in order to receive data at a sample rate of 50 Hz using its "fast-mode".
, 2010; Beauchemin and Handel, 2011) , co-receptor switch  (Alizon and Boldin, 2010)  and virion loss due to cell entry  (Beauchemin et al. , 2008; Gonçalves et al. , 2020). We implemented a few of these models in the R code MoWPP accompanying this manuscript in addition to the three kinetic models presented in Section 2. 1. Another perspective is to provide alternative choices in the genetic component of our hybrid model. The basic substitution model used here may notably be replaced by more realistic mutation processes  (Kimura, 1980; Tavaré, 1986; Nishimaki and Sato, 2019) , which would constrain the frequencies of different sequence modifications and might possibly modify the observed diversity patterns. While viral load was modeled as a continuous process (using ODEs) like most of the standard virus kinetics models found in the literature, sampling of virus genomes during replication and within-host infection was modeled as a discrete process for numerical tractability, with genotype growth and genome mutation occurring at a discrete time step (once per day).
Fitting profiles of filamentary structure We characterized properties of filamentary structure using the python-based Filament Characterization Package (FilChaP;  Suri et al. 2019) , which was designed to work together with DisPerSE. Here we give a brief explanation of the procedures implemented in FilChaP and refer the reader to  Suri et al. (2019)  for more detailed descriptions. FilChaP takes as input a list of filament coordinates. At each sample point along the filament, a radial profile perpendicular to the filament spine is extracted. The radial profile extends to ±130 pixels, which we chose so that a baseline estimate includes true background emission at any location in the image 7. The radial profiles extracted at four consecutive sample points of the filament are averaged together.
Acknowledgements. We thank  Nicola Schneider  for sharing data products, and  Frits Sweijen  and  Alex Mechev  for maintaining software on the grid infrastructure and in Leiden. K. L. E. thanks the  Green Bank Observatory  for hosting her as a (remote) guest during the completion of this work. The authors thank the anonymous referee for their time and efforts reviewing this manuscript. K. L. E. , H. J. A. R. and A. G. G. M. T. acknowledge financial support from the  Netherlands Organisation for Scientific Research (NWO)  through  TOP  grant  614. 001. 351. G. J. W. gratefully acknowledges the support of an  Emeritus Fellowship  from the  Leverhulme Trust. R. Jv. W. acknowledges support from the  VIDI research program  with project number  639. 042. 729 , which is financed by  NWO. A. G. G.
Single-cell lineage tracing permits the labeling of individual cells with a heritable marker to follow the fate of each cell's progeny. Over the last twenty years, several single-cell lineage tracing methods have emerged, enabling major discoveries in developmental biology, oncology and gene therapies. Analytical tools are needed to draw meaningful conclusions from lineage tracing measurements, which are characterized by high variability, sparsity and technical noise. However, the single cell lineage tracing field lacks versatile and easy-to-use tools for standardized and reproducible analyses, in particular tools accessible to biologists. Here we present CellDestiny, a RShiny app and associated web application developed for experimentalists without coding skills to perform visualization and analysis of single cell lineage-tracing datasets through a graphical user interface.
We demonstrate the functionality of CellDestiny through the analysis of (i) lentiviral barcoding datasets of murine hematopoietic progenitors; (ii) published integration site data from Wiskott-Aldrich Symdrome patients undergoing gene-therapy treatment; and (iii) simultaneous barcoding and transcriptomic analysis of murine hematopoietic progenitor differentiation in vitro. In summary, CellDestiny is an easy-to-use and versatile toolkit that enables biologists to visualize and analyze single-cell lineage tracing data.
This point is critical, because the analysis of lineage tracing datasets is non-trivial, with data characterized by high variability, sparsity and technical noise. Complex analysis pipelines have been developed to tackle these challenges, but they require significant expertise in both computer programming and statistics to implement and evaluate, precluding their usage by biologists who cannot code. This is particularly limiting for the biological interpretation of the data in which the biologists play an active role together with bioinformaticians. There is therefore a need for tools that permit the exploration of lineage tracing datasets, without needing to write computer code. Increasing the availability of open-source tools will make lineage-tracing analysis more reproducible, transparent, and accessible-as well as permitting meta-analyses across independent studies  (31, 32).
Currently available software tools to perform lineagetracing analysis can be divided into two key categories:  (1)  Preprocessing toolkits that perform recovery, filtering and quality control of genetic sequences from raw sequencing reads and (2) Analysis toolkits that perform visualization and analysis of barcode abundances across biological samples (Figure  1 ). Pre-processing toolkits are typically context-specific, in that the retrieval, QC and quantification of genetic sequences will vary depending on the nature of the barcode. The open-source toolkit genBaRcode has been developed to address this issue, performing sequence retrieval and processing across a range of different genetic labeling strategies, as well as some data visualization tools  (33). Xcalibr is also a pre-processing toolkit, that focuses only on the extraction and demultiplexing of barcodes from sequencing data  (18, 19).
With regards to analysis toolkits, barcodetrackR has been developed for the analysis of clonal tracking data over time (31) (Figure  1 ). BarcodetrackR is a versatile tool that places an emphasis on longitudinal analyses. However it does not permit the visualization of key QC metrics, such as the consistency of measurements across technical replicates, which are an important consideration when drawing biological conclusions from the data. In addition, for parameters other than time, such as treatments or organ localization, the visualization of the data is limited. Here, we present a new complementary analysis toolkit called CellDestiny, enabling the visualization and analysis of single-cell lineage tracing data through an easy-to-use web application. CellDestiny is an easy to use app that facilitates data exploration and extraction of biological insights from single cell lineage tracing data without needing to write computer code.
Overview of CellDestiny CellDestiny is a data analysis tool developed for lineagetracing experimentalists without coding skills. Focusing on this user group is important as nuanced experimental details can influence the interpretation of the results, and so experimentalists need to play an active role in the analysis. The purpose of this app is to provide users who cannot write computer code with greater autonomy to analyze, explore and extract meaningful information from lineage tracing datasets. Cell Destiny is available as a Rshiny app or web application. The R package associated with CellDestiny is a collection of plotting and analysis tools available to experimentalists who prefer to write their own code rather than using the graphical user interface. CellDestiny requires two key inputs: (1) a count matrix that has undergone sequence and experiment level quality control (QC) preprocessing where each row (x) is a unique barcode Overview of the CellDestiny analysis toolkit.
(Step 1) A count matrix containing technical replicates for each sample after data quality control and normalization steps, for example using the genBaRcode package, can be uploaded along with associated metadata detailing the variables used for the analysis such as organ, time point, individual etc. (Step 2) The quality of the data can be visualized to assess the correlation between duplicates and also the frequency of repeat-used barcodes. These steps will help the user to assess the impact of technical artifacts on the biological interpretation of the data. ( Step 3) The data summed or averaged over replicates can then be uploaded and visualized through a set of different graphs to assess clonal diversity, clone-size distribution and fate outcomes. Importantly, these graphs offer several customization options such as grouping data across individuals, cell types and organs.
sequence and each column (y) is a sample. The numeric value for each x,y pairing then represents the number of times the barcode label x was measured in sample y. (2) A metadata table providing information about each sample which may include the experimental timepoint, cell type and individual identity. Additional information such as pre-calculated QC metrics and information about experimental design such as sequencing batch can also be uploaded through the metadata feature and overlaid onto QC and analysis plots. In the CellDestiny workflow (Figure  2 ), the user can first visualize QC from pre-processed data for each sample independently (see next section) before performing analyses such as the quantification of barcode sharing, clone size, barcode diversity and lineage biases across individuals and samples.
CellDestiny allows the user to choose which data to visualize using several grouping options. As the grouping option depends on the biological question being addressed by the experiment, CellDestiny has been designed to allow greater flexibility in data exploration than previous tools. Each graph generated can be exported as an image, as well the associated reformatted matrix to be uploaded for statistical analysis with external software packages such as Prism. Cell Destiny is not tailored for statistical analysis. A graphical overview of the package is provided in Figure  1 , and a table comparing CellDestiny to the complementary barcodeTrackR toolkit is provided in Supplementary Table  1.
Quality controls Quality control (QC) is an important part of the analysis of single-cell lineage tracing data but there is currently no clear consensus on how data QC should be done. Different guidelines have been published  (34, 36)  and while some software packages exist to perform pre-processing of genetic sequences (33) many pipelines are custom-built for each distinct barcode design. It is therefore challenging to create a fully automated QC pipeline that is applicable across all single-cell lineage tracing technologies. While we advocate that data QC should be done by bioinformatic experts, we believe that QC data be made accessible to biologists with no coding skills, such that they can better draw biological interpretations from the data. Quality control steps for lineage tracing experiments can be subdivided into two key sub steps: (i) sequence level QC assesses the genetic sequences encoding heritable markers and (ii) experiment-level QC that assesses the consistency of results within and between biological and technical replicates (Figure  1 ).
To this end, CellDestiny is designed to allow experiment-level QC visualization (Figure  2 ). There are two important experiment-level QC steps before proceeding to analysis of barcoding data,  (1)  the correlation between PCR technical replicates and (2) the evaluation of repeat usage of barcodes  (34). Firstly, PCR technical replicates allow to measure the degree of confidence in barcode detection to establish lineage relationship between cell types. If the replicates do not show high correlation, there may an issue with barcode contamination or it may suggest poor recovery of daughter cells or poor expansion of progenitors. In CellDestiny, technical replicates can be uploaded and visualized for each sample. Secondly, the integration of the same barcode into multiple cells, called repeat usage, is also an important QC metric that should be considered in a lineage tracing analysis pipeline, as a high incidence of repeat usage may lead to false lineage relationship assignments.
The transfer of progenitors from the same transduction batch into at least two separate mice, followed by subsequent comparison of the barcodes recovered from those mice, can be used to estimate the frequency of repeat barcode use within one mouse. In CellDestiny, this QC can be visualized with 2D plot of barcode sharing between two mice or with a heatmap when analyzing sharing across more than 2 mice (Figures  2A, 4 ). It is important to state that PCR/sequencing errors and multiple integrations are not the only source of confounding factors in lineage tracing experiments. Insufficient cell sampling can lead to poor barcode recovery and greatly influence the interpretation of the data and so must be considered when interpreting the results. Where available several tissue sampling replicates can be compared in CellDestiny through the metadata functionality.
In the following sections we demonstrate the functionality of CellDestiny through 3 case studies: (1) lentiviral barcoding of murine hematopoietic progenitors (2) lentiviral hematopoietic stem cell gene therapy in patients with Wiskott-Aldrich Syndrome (3) lentivirus barcoding and transcriptomic analysis of murine hematopoietic progenitors, cultured in vitro. A table summarizing each dataset, along with key metadata and information about how to access the raw data is provided in Supplementary Table  2.
Data quality control Prior to drawing biological insights from a lineage tracing experiment, it is important to assess whether there are technical factors that can confound the interpretation of the data  (34). Before CellDestiny, the samples have been filtered as described in the material and methods giving a total of 60 barcodes represented in the cDC lineages for all 3 mice (m1 = 32 barcodes, m2 = 17 barcodes, m3 = 11 barcodes). Applying the CellDestiny workflow to our case-study lentiviral barcoding dataset, we see a high correlation between technical replicates (example for mouse 2 in Figure  4A ) (Spearman's ρ = 0. 85 ± 0. 08, p-value = 0. 01 ± 0. 02). By plotting repeat usage diagnostic plots (Figures  4B, C ), very few shared barcodes (3. 4%) appear across multiple individuals (Figure  4C ).
Thus, the QC visualization of CellDestiny confirms that our data are of good quality for biological analysis. In the subsequent sections we use CellDestiny to assess clonal diversity and clone-size distributions, as well as lineage bias parameters to study the developmental history of lung-resident cDCs. Analyzing the impact of sequencing errors and repeat use barcodes (A) comparing technical replicates for cDC1 and cDC2 samples of mouse 2 from case study 1 to control for sequencing errors On these plots each dot represents a single barcode and the Spearmans correlation coefficient is given at the top of each plot (0. 79 for the cDC1 sample and 0. 93 for the cDC2 sample). This is a representative plot showing data from 1 mouse, figures for the other mice can be found in Supplementary Figure  1.
Clonal diversity and clone size in cDC1 and cDC2 To study the ontogeny of differentiated cell types, CellDestiny provides functionalities to assess: (i) clone size distributions and (ii) clonal diversity. CellDestiny provides visualization of how many cells are produced per single barcoded progenitor, here referred to as clone size. Clone size analysis allows to quantify the cellular outputs of barcoded progenitors. In addition to clone size, clonal diversity measures the number of progenitors that give rise to a differentiated cell population and so gives an indication of the amount of active progenitors contributing to differentiation. There are different ways to estimate clonal diversity using CellDestiny, the simplest being the total number of unique barcodes that are measured per sample, however, more complex metrics such as Shannon and Simpson indices that consider how frequently each barcode is measured are also available.
Case study 3: Combining single-cell RNA sequencing and lineage tracing information in single Cells Overview Recent technical developments in the lineage tracing field now permit simultaneous fate and gene expression measurements in single cells (42) (Figure  10 ). To illustrate how CellDestiny can be used to analyze such datasets we present a case study analysis wherein cKit + (LK) and cKit + Sca1 + (LSK) murine bone marrow cells were barcoded using the LARRY lentiviral barcoding library and then cultured in vitro using a cocktail of cytokines and growth factors that support pan-myeloid differentiation (Figure  10A )  (35). In this published study, cells were processed for single cell RNA sequencing at days 2,4, and 6 after lentiviral barcoding and barcode information was recovered at the RNA level directly from InDrop sequencing libraries along with associated gene expression measurements.
To convert this dataset into a format compatible with CellDestiny we generated a count matrix, where count values represent the number of times each barcode was found in each cell type (refer to the materials and methods section for further details). Cell type definitions were taken from the original publication and were obtained by k-means clustering of gene expression measurements and then annotating the clusters using marker genes from the literature (Figure  10A ). This matrix was then loaded into CellDestiny along with metadata about each starting cell population (LK and LSK). The aim of this analysis is to visualize barcode abundance and diversity across different cell types as well as visualising barcode clone size distributions, and patterns of barcode sharing between myeloid cell types that are highly represented in this dataset.