dataesr/scientific-paragraphs-categorization huggingface dataset:
The observed distribution of polarization angles indicates that the POS B-field in the CMZ has a dominant orientation of 22 • measured clockwise with respect to the Galactic plane (Figure  5 ). There are no obvious structures in the PILOT total intensity maps that correspond to this characteristic orientation. -Within our observed field, the POS magnetic field structure tends to become more parallel to the Galactic Plane in re-gions with high 240 µm brightness, such as the well-known Sgr B2, 20 km/s and 50 km/sclouds (Figure  7 ). -The polarization angles measured by PILOT are in good agreement with the polarization angles measured by Planck at 217 and 353 GHz across the same region. The PILOT data shows poorer agreement with the distribution of polarization angles measured by Planck at lower frequencies, which is likely due to the impact of contamination by the CO line and synchrotron emission in in the Planck 70, 100 and 143 GHz channels.
DETECTORS AND DATA SET In this analysis, we use data from the two aLIGO detectors in Hanford, Washington (H1) and Livingston, Louisiana (L1). No data from Virgo  (Acernese et al. 2015)  or GEO600  (Dooley et al. 2016)  was used because of their lower sensitivity. 2 Three of the pipelines use data up to 2 kHz; STAMP also uses data up to 4 kHz. Both detectors in their O2 configuration had their best sensitivity in the 100-200 Hz range, with significantly less sensitivity in the kHz range (e. g. a factor ∼4 worse in strain at 2 kHz)-see Fig. 1. For the lower analysis cutoff of each pipeline, see Sec. 4. Starting from a rounded GW170817 coalescence time of t c ≈ 1187008882 s, the HMM pipeline uses 9688 s of data (until the first gap in H1 data), ATrHough uses 1 day of data after t c , FreqHough analyzes from 1 to about 18 hours after t c in different configurations, and STAMP analyzes the whole 8.
The possibility of co-simulation through real-time data exchange between those two types of software could allow training or designing session in dynamic virtual environments. Functional Mock-up Interface (FMI) is a software independent standard to support both model exchange and co-simulation of dynamic models using a combination of xml-files and compiled C-code  (FMI, 2018). It is a particularly appropriate standard to develop CPPS and data exchange between simulation and virtual reality applications. For example,  Waurich and Weber (2017)  propose a Functional Mock-Up Unit (FMU) developed in C++ that allows the visualisation in virtual reality software (Unity) of an excavator model driven by simulation software (OpenModelica). Unity is used to provide a graphically satisfying view of the model within a realistic environment while the use of OpenModelica ensures a relevant behaviour of the system model.
Then, the proposed technical architecture allowing cosimulation between the digital twin and the virtual reality environment is presented. This architecture is illustrated by the co-simulation of an UR10 6-DOF cobotic arm where simulations of motion are performed within a digital factory suite (Dassault Systems, 2018) and virtual reality interactions and simulations within a dedicated software based on Unity. Then, the proposed co-simulation architecture and tools are studied as an assessment tool for the operator's safety and ergonomics during the design of industrial workstations. Perspectives to exploit this co-simulation environment for virtual reality operator training are also discussed.
Digital twin As presented in the introduction, a digital factory suite (Dassault Systems, 2018) is used for the digital twin and more particularly two modules: Catiafunctional and logical design and Modelica, to model the system and the associated constraints (indicated with (1. a) in Figure  4 ). The logical model of the robotic arm is constituted by: the physical constraints which represent the gravity and mass of each part of the system (see (1. a) Physical in Figure  4 ), the kinematic chain constraints which represent connection between each part and the parameters associated (friction, limits), and the engine constraints which represent the engine force and torques applied (see (1. a) Kinematic chain in Figure  4 ). Once those parameters are set, the digital twin version of the system is behaving in a realistic way.
From that point, it is necessary to send data about the simulation to the virtual reality environment. As presented in introduction, a co-simulation architecture based on Functional Mock-up Interface is retained and a FMU is developed (see (1. b) in Figure  4 ). It is composed of three major components: • a ModelDescription. xml file, which defines input/output data managed by the FMU. Each of those can transmit the state, in real-time, of a component of the system and are included within a < scalarVariables> tag in the xml file. The naming convention is {Name of the part}_{type of action}_{element concerned} (example: Axis1_rotate_x); • a ModelDescription. xslt file, which automatically parses the ModelDescription. xml file to transform it in a C++ header file (modelDescription. h). C++ variables can thus be used in the FMU Core C++ program;.
Mechanics & Physicals Constraints Digital Twin Visualization Interactions Data server Context Figure 3. Co-simulation workflow between the digital twin and the virtual reality environment. • a FMU Core C++, a binary file which is a compiled plugin (see (1. b) in Figure  4 ). This particular program is developed to transmit all the scalar variables, generated by the simulation done in the digital twin, to the data server, through a socket. Once compiled, the FMU becomes a new component, in the Modelica software, usable by any person used to work with it. As a result, FMU makes it possible to transmit the logic behaviour of a system to another tool in real-time. In the FMU UR10 presented in this paper, the position and rotation of each part of the robot arm are sent in JSON format to the data server.
Data server: coordinator of the data transmission The role of the data server is to deal with the data in order to transmit it to the right virtual reality environment in real time. The data server act as a coordinator and the digital twin and the virtual reality environment represent the clients. In order to manage several clients simulating different part or behaviours of the systems, SimulationID is used. Hence, only clients using the same SimulationID will share their data. To communicate in real time, the socket technology is chosen. As each digital twin is using different languages, we have chosen the ZeroMQ framework since it has been developed to connect any code to any code. Moreover, Client-Server communication with ZMQ is done asynchronously, that is, processing will not be blocked while another operation is in progress.
Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. We profiled the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire Plasmodium berghei life cycle. We then used our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a eukaryotic parasite and an open-access reference dataset for the study of malaria parasites. Single-cell RNA sequencing (scRNA-seq) is revolutionizing our understanding of heterogeneous cell populations, revealing rare cell types, unraveling developmental processes, and enabling greater resolution of gene expression patterns than has previously been possible  (1).
Single-cell resolution transcriptional variation provides insights into gene usage To begin to build the Malaria Cell Atlas, we profiled 1787 single-cell transcriptomes across the entire life cycle of Plasmodium berghei using a modified Smart-seq2 approach  (5). Purification methods were adapted to isolate each stage of the life cycle, including challenging samples such as rings, which have low levels of RNA, and ookinetes, which are difficult to sort (fig. S1 ). Ninety percent of sequenced cells passed quality control (1787/1982 cells) and poor-quality cells were identified in each stage according to the distribution of the number of genes per cell (fig. S2 ). After quality control, we detected a mean of 1527 genes per cell across the entire dataset; however, the number of genes detected was highly dependent on parasite stage (P < 0.
001; fig. S2 ). Transcriptomes were normalized with TMM (trimmed mean of M-values) in groups of related stages for further analysis. For samples expected to be overlapping or heterogeneous (e. g. , the blood stages), we used k-means clustering to delineate stages and confirm their classification based on known marker genes and correlations with bulk reference datasets (figs. S3 and S4). This allowed for differentiation of male, female, and asexual stages in the blood, as well as between ookinetes and oocysts in the heterogeneous population of parasites taken from the mosquito midgut during ookinete invasion. All cell transcriptomes were visualized using uniform manifold approximation and projection (UMAP) (6) (Fig. 1B ) and the first three components of a principal components analysis (PCA) (Fig.
We constructed a force-directed k-nearest neighbor (k-NN) graph, where each node represents one of 5156 genes detected in the dataset (data S1). Graph spectral clustering (8) was used to assign each gene to one of 20 modules based on the graph distance matrix (Fig. 2A  and data S1). Clusters were enriched for genes involved in specific biological processes and displayed distinct patterns of expression throughout the life cycle (Fig. 2B , fig. S7 , and data S1). Some gene clusters (1 and 2) consisted mainly of housekeeping genes and ribosomal RNA components highly expressed across the full life cycle. At the other extreme, several clusters (clusters 18 to 20) showed low mean expression across cells and were primarily composed of genes from rapidly evolving multigene families (pirs and fams), which have no 1:1 orthologs with P.
CCA clustering showed good representation of all stages in both Smart-seq2 and 10x datasets within the IDC (fig. S13 ). Using scmap-cell, 94% of cells in the 10x data were assigned to a Smart-seq2 cell with high confidence, allowing us to align datasets (Fig. 3A ). The additional P. berghei 10x data increased the coverage of cells in our atlas and confirmed our ability to evaluate single cells characterized by different methodologies. To account for the continuous cyclical nature of the data, we ordered the 10x cells in pseudotime by fitting an ellipse to the first two principal components and calculating the angle around the center of this ellipse for each cell relative to a start cell (Fig. 3B  and methods ). To annotate the orientation of the cycle with real time, we correlated each single-cell transcriptome with published bulk reference datasets and observed a high correspondence between bulk time point and pseudotime order (Fig.
3B  and fig. S14 ). Additionally, we generated a 10x dataset comprising 6737 cells from the IDC stages of the human parasite P. falciparum. We used scmap-cell to assign each P. falciparum and P. knowlesi cell to the P. berghei 10x reference index built with 1:1 orthologs (data S3), thus enabling us to align the developmental trajectories of these three species (Fig. 3C  and fig. S15 ). We used this alignment to examine patterns of transcription in all three species in a time-resolved manner across the IDC. We calculated RNA velocity  (20) , which measures the rate of change of mRNA molecule abundances, across this deeply sampled set of Plasmodium parasites. We found that the rate of change varies markedly over the IDC in P. berghei, with peak velocity occurring in late rings, consistent with bulk studies of nascent RNA transcription (Fig.
S16 ). Transcriptomic studies of both in vivo and in vitro malaria parasites are often confounded by multiple life stages within a single sample. The Malaria Cell Atlas can be used to deconvolve bulk transcriptomic data and identify the specific life stages that were present in a bulk RNA-seq sample. We demonstrated this with the use of published bulk RNA-seq datasets from P. berghei  (22, 23)  and P. falciparum  (24)  (fig. S18 ). Future bulk RNA-seq studies can use the atlas to identify differences in cell type composition, potentially regressing these out to calculate more accurate differential expression between conditions or samples. Furthermore, scRNA-seq data generated by other groups (25) using a different droplet-based technology (Drop-seq) are also easily classified using scmap and the atlas data provided here (fig.
The Malaria Cell Atlas as a reference for clinical samples In vitro systems, although critical for experimental studies of Plasmodium parasites, are unlikely to fully capture the breadth of expression variation of parasites circulating in naturally infected carriers. Moreover, there are six phylogenetically distinct human-infecting species (Fig. 4A ), several of which do not have any existing expression data and cannot be cultured in vitro. We therefore explored whether scRNA-seq data from wild parasites taken straight from infected people could be used to place wild parasites in developmental time using our atlas. We developed a methanol-based preservation protocol that produced Smart-seq2 transcriptomes with equivalent quality to unpreserved cells in the lab (fig. S20 ). Next, we used the protocol to preserve samples from three naturally infected asymptomatic carriers in Mbita, Kenya, which we then sorted and sequenced in the UK.
falciparum, P. malariae late stages do not sequester in deep tissue (Fig. 4, A  and B ). As a proof of concept, we have shown that parasite species that have previously been inaccessible for expression analysis can now be characterized by combining scRNA-seq with the atlas. The Malaria Cell Atlas reference dataset comprises 15,858 parasite transcriptomes covering every life stage along the parasite's life cycle at single-cell resolution, and spans different technologies and different parasite species. The data are freely accessible as a processed dataset and through a user-friendly web interface  (27, 28). As such, this will be a key resource for the malaria community in the study of transcriptional regulation and control of developmental progression at the highest resolution. Because the Malaria Cell Atlas provides a foundation for studying the biology of individual parasites directly from their natural environment, it represents an important endeavor toward characterizing phenotypes critical for malaria control, including those related to pathogenicity, drug resistance, and transmission biology.
Mapping and generation of expression matrices for scRNA-seq transcriptomes Smart-seq2 mapping-Single-cell Plasmodium transcriptomes were mapped as reported previously  (5). Briefly, trimmed reads were mapped using HISAT2 (v 2. 0. 0-beta) (37) to the P. berghei v3 genome (October 2016), and using STAR (v 2. 5. 0a) to the P. falciparum v3 (January 2016) and P. malariae v1 (March 2018) genomes using default parameters  (38). Reads were summed against genes using HTseq (v 0. 6. 0) (39). For the coexpression of HeLa cells and liver-stage parasite analysis, both HeLa cells and parasites were mapped to respective genomes with STAR (v 2. 5. 1b) using default parameters  (38). 10x data alignment, cell barcode assignment, and UMI counting-Cell Ranger single-cell software (version 2. 1. 0) was used to process sequencing reads, assigning each read to a cell barcode and UMI using standard parameters  (17).
After barcode assignment, the cDNA insert read was aligned using Cell Ranger (v 2. 1. 0) to a combined reference genome of P. knowlesi (March 2014) and P. berghei (July 2015), and the P. falciparum run was aligned to the 3D7 genome v3 (January 2016). These reference genomes were all obtained from www. sanger. ac. uk/resources/downloads/protozoa/.
Filtering and normalization of scRNA-seq data Smart-seq2 filtering and normalization-Poor-quality cells were identified on a perstage basis according to the distribution of the number of genes per cell, given the high variability of genes detected between stages (fig. S2, A  and B ). Cells with fewer than 1000 genes per cell and 2500 reads per cell were removed from the liver-stage parasites, trophozoites, male and female gametocytes, ookinetes, ookinetes/oocysts, and oocyst stages. Cells with fewer than 500 genes per cell and 2500 reads per cell were removed from schizonts and injected sporozoites. Cells with fewer than 40 genes per cell and 1000 reads per cell were removed from merozoites, rings, and gland sporozoites (fig. S2  and table S1 ). Additionally, we removed genes from further analysis that were detected in fewer than two cells across the entire dataset.
The final dataset contained 1787 high-quality single cells from 1982 sequenced cells and 5156 genes out of 5245 genes with annotated transcripts. Transcriptomes were normalized with the weighted TMM method  (40). Cells were normalized either all together or in five groups containing biologically similar stages; groups included IDC, liver-stage, gametocytes, ookinetes/oocysts, and sporozoites. Visual inspection of the relative expression plot (fig. S2D ) showed little difference between normalization by biological group versus all together. Unless otherwise specified, further analysis was done on cells normalized by biological group. 10x filtering and normalization-For P. berghei, the output filtered matrix from Cell Ranger was read into Seurat (v 2. 3. 4) (18). Low-quality P. berghei cells with fewer than 230 detected genes were removed from further analysis.
Initial inspection of filtered cells in the P. knowlesi and P. falciparum datasets showed that early-stage and late-stage IDC parasites were missing. These stages express fewer genes per cell relative to later stages based on our Smart-seq2 data, and we have previously observed a lower detection of genes per cell in P. falciparum  (5) , suggesting that these cells may have been removed by Cell Ranger's default thresholding. Using the raw output matrices for these species, we adjusted thresholds to retain cells with >100 genes per cell for P. falciparum and >150 genes per cell for P. knowlesi. Intraspecies doublets were identified and removed from all three species using doubletFinder (v 1. 0. 0) (41). For the P. berghei/P. knowlesi run, we identified interspecies doublets as cells that contained >50 UMIs that mapped to each species (fig.
Gene clustering and visualization of Smart-seq2 data- The gene count matrix was normalized by dividing by the mean counts for each gene and log scaling. This was done to reduce the amount to which gene clusters were driven by total gene expression and instead focus on the pattern of expression across cells. A k-NN graph was formed on the genenormalized expression matrix with the Nearest Neighbors subpackage of python's scikitlearn version 0. 19. 2 with parameters of k = 5 and a manhattan distance metric  (45). We chose a k value of 5 because it was smaller than the smallest cluster we were interested in detecting, and the graph appeared robust from k =3 to k = 20. We then performed spectral graph clustering on this k-NN graph using the SpectralClustering subpackage of python's scikitlearn version 0.
Second, marker genes were identified by defining a core set of genes for each stage as all genes that are expressed in more than 50% of cells. To avoid bias from the number of cells sampled within a stage, 60 cells were randomly selected per stage (data S2). We defined the unique core transcriptome as genes from each stage's core that were unique to that stage's core (i. e. , not also found in more than 50% of cells from any other stage) (data S1). Motif discovery-Motif discovery was performed using DREME, which searches for short (8 bp) motifs expressed as regular expressions (consensus sequences allowing for wildcards but not variable length gaps) in a given set of sequences  (51). The 1000 bp upstream of the start codon for each gene detected in the Smart-seq2 dataset was used in the analysis.
For each cluster, the input dataset was the upstream regions of each gene within that cluster and the negative set was the upstream region of genes that were not in that cluster. Clusters 18, 19, and 20  were not included in this analysis because they contain many paralogous genes from gene families with large duplicated upstream regions. The top motif of each cluster was compared to motifs from  (12, 13, 52, 53)  using Tomtom  (54).
Illumina reads from these libraries were mapped to the P. berghei v3 genome sequence using HISAT2 v2. 0. 0 (37), with-rna-strandness RF-max-intronlen 5000. The data were viewed using Artemis v18. 0. 0  (56). 5′ UTRs were defined as the region between the start codon and where RNA-seq coverage dropped to zero in at least two of the three samples. Upstream intergenic regions were defined from the start of the 5′ UTR to the next, upstream increase in coverage from one or more RNA-seq libraries. The upstream intergenic regions were BLASTed against each other (blastall 2. 2. 25, -p blastn -e 1e-20). The sequences involved in each hit were extracted, excluding those overlapping others with lower E-values. These sequences were then BLASTed against each other (blastall 2. 2. 25, -p blastn -e 0. 01) and the resulting similarity matrix was used to cluster them with MCL v12-068 (57) with the inflation parameter set to 1.
10x single-cell transcriptome analysis Cell clustering-To identify male and female gametocytes in the P. berghei data, data were log-normalized, and clusters were identified using the shared nearest neighbor modularity optimization-based clustering algorithm in the FindClusters() function in Seurat  (18). Two clusters corresponded to gametocytes based on expression of marker genes. These clusters were removed when comparing the data to the Smart-seq2 via CCA, as well as for the pseudotime assignment, and alignment of the three datasets in scmap (Fig. 3 ). The three species IDC PCAs were generated on TMM normalized data in scater (version 1. 6. 3) (60). Additionally, we identified clusters using the CCA in Seurat to compare the two methods of scRNA-seq (Smart-seq2 and 10x)  (18). We identified nine clusters of cells that had good representation in both datasets (fig.
S13C ). One cluster, 8, contained only 15 cells across the two datasets and was removed from further analyses. SCmap-We used scmap (version 1. 1. 5) (19)  to compare datasets. We built three sets of cell indices that could be queried with the scmapCell() function that would allow each individual cell in the query dataset to be mapped to a reference index  (19). To compare the Smart-seq2 and 10x data, we built an index of the blood-stage Smart-seq2 data (including gametocytes) and mapped the full P. berghei 10x dataset (including gametocytes) onto it. Because the IDC consists of a continuous set of cell stages and not discrete clusters, we modified the cell assignment method in scmap: Cells were assigned based on the top nearest neighbor. If the top cell had a cosine similarity of greater than 0.