dataesr/scientific-paragraphs-categorization huggingface dataset:
Haemoglobin was reported for only 43 patients and pulmonary artery pressure (PAP) for only 81 patients of the 125 patients. A more recent study by Bedard et al. 1  summarized the findings from 73 pregnant patients with PAH, with 29 having iPAH, 29 CHD-PAH, and 15 oPAH, with a 90-day reported mortality of 17%, 28%, and 33%, respectively. More than two-thirds of the deaths occurred within the first month after delivery. Patients with LHD-PH were not included in this analysis. As most of the cases in this cohort came from individual case reports, mean pulmonary artery pressure (MPAP), when not reported, was approximated or echocardiographic data was utilized using the Bernoulli formula (MPAP = systolic PAP estimated by echocardiogram/4 × 2. 4). In the study by Bedard et al. ,  1  therapies for PAH were used during pregnancy and delivery in 72% of patients with iPAH, 52% of patients with CHD-PAH, and 47% of patients with oPAH.
However, in the ROPAC database peak tricuspid regurgitation velocity was only rarely entered by the investigators as it was not a mandatory field. The majority of women in the ROPAC registry had mildly to moderately elevated RVSP with more than 80% having a RVSP <70 mmHg. Transthoracic echocardiography allows estimation of the RVSP from the velocity of the tricuspid regurgitation by adding right atrial pressure. 11 This diagnostic module has led to widespread use and increased documentation of the burden of PH in patients with heart disease and its association with a high morbidity and mortality. However, PH is often a neglected issue and the importance of long-term follow-up has only recently been highlighted. 16  rationale for using echocardiography in the diagnosis of PH in patients from low-to-middle income countries, where right heart catheterization is often not   17  According to our knowledge, the impact of the haemodynamic changes of pregnancy on the measurement of the RVSP by echocardiography has not been formally evaluated.
Acknowledgements We thank all ROPAC investigators, who are listed in the Supplementary material online (Appendix  S1 ). Our gratitude goes to the  EORP team  for their contributions, in particular  Elin Folkesson-Lefrancq ,  Viviane Missiamenou ,  Gérard Gracia , and. Myriam Lafay. To join the Registry please see the ROPAC website (http://www. escardio. org/guidelines-surveys/eorp/surveys/pregnancy/Pages/welcome. aspx).
Isovolumic left ventricular developed pressure (LVDP) as well as end diastolic pressure (EDP) were continuously monitored during the experiment through a water-filled latex balloon inserted into the left ventricle and analyzed subsequently using Lab Chart software (ADInstruments, Colorado Springs, CO). Hearts were paced at 4. 5 Hz with bipolar electrodes attached to the left ventricle using a Grass SD9 stimulator, and pacing was maintained throughout all protocols. EDP was adjusted initially at 4-8 mmHg. After 20 min of equilibration, one of the two protocols described in the next section was initiated.
Effect of various concentrations of ouabain on Na + /K + -ATPase activity Ouabain-sensitive Na + /K + -ATPase activity was determined in KCL-enzyme preparation in the presence of 10 À8 to 10 À3 mol/L ouabain in the reaction mixture. IC 50 (50% inhibitory constant) and relative proportion of a 1 and a 2 isoform of Na + /K + -ATPase was inferred from ouabain-sensitive Na + /K + -ATPase activity, as estimated from dose-response curves on permeabilized crude heart homogenate preparations using GraphPad PRISM Software (Version 4. 00). Curves were fit to experimental data by a nonlinear regression model using GraphPad PRISM Software (San Diego, CA).
GAPDH (Sc-20357; Santa Cruz) was probed as a loading control. Rabbit (Sc-2004; Santa Cruz), goat (Sc-2020; Santa Cruz), and mouse (Sc-2005; Santa Cruz) secondary antibodies were used in this study. Protein bands were detected using chemiluminescence and enhanced chemiluminescence, and quantified using Image J software (NIH, Bethesda, DC).
The pooled libraries (20 pM) were sequenced on an Illumina MiSeq in six consecutive runs (MiSeq reagent kit V3, 150 cycles) generating 76 base pairs (bps) paired-end reads. Raw sequences have been deposited at the NCBI Gene Expression Omnibus (GEO), http://www. ncbi. nlm. n ih. gov/geo, accession number: GSE85144.
Assembly, Mapping, and Data Analysis Raw sequences reads were uploaded in CLC Genomics Workbench 8. 0. 3. Sequences were filtered and trimmed as follows: sequence length > 55 bps, sequence quality score < 0. 01, no ambiguity in the sequence, trimming using Illumina adaptors, hard trim of 10 bps at the 5 end and 3 bps at the 3 end, resulting in a final sequence average length of 61 bps. Duplicated reads were removed from each library using the duplicate read removal plugin. The de novo assembly was performed with a wording size ranging from 20 to 54. The reads were mapped back to the assembly with a mismatch, insertion and deletion cost of 3, a coverage > 0. 8 and similarity > 0. 95. The optimal parameters were obtained with the auto-wording mode (24), bubble sizing in automatic mode. The assembly was then annotated using Blast2GO PRO version 3.
0 against the A. thaliana non-redundant database. For each library, the mapping was performed with CLC Genomics Workbench 8. 0. 3 according to the following criteria: a maximum hit per reads of 3, similarity fraction > 0. 95, a length fraction > 0. 7, a mismatch, insertion and deletion cost of 3. Mapping (using the same above-described parameters) was also performed using the available transcriptome for the variety Finola  (van Bakel et al. , 2011) , which was annotated using Blast2GO PRO version 3. 0 against the A. thaliana non-redundant database. The expression values were then calculated using the RPKM method  (Mortazavi et al. , 2008). Genes with less than 10 mapped reads, with no specifically mapped reads in at least one of the libraries, were removed from the dataset. In order to highlight the differentially expressed genes, an ANOVA with four groups (H6, H9, H15, and H20) composed by three biological replicates was performed.
A false discovery rate (FDR) correction was then applied to the dataset and genes with FDR corrected p-value below 0. 05 were selected. Hard cut-offs were finally performed on the fold change (FC absolute value > 2) and the RPKM minimum difference (RPKM difference > 10). Independent component analysis (ICA) was performed to assess the differences between the biological replicates within the online service MetaGeneAlyse v1. 7. 1  (Daub et al. , 2003). MapMan functional annotations of H6 and H20 were performed with a subset of genes (-2 > Fold Change (FC) > 2) showing a TAIR ID. GOE analysis of H6 and H20 was realized with genes displaying -2 > log2 FC > 2 using  ClueGO (v2. 1. 1) and CluePedia (v1. 1. 1) (Bindea et al. , 2009 (Bindea et al. , , 2013) )  within Cytoscape (v3. 2. 1) with the following parameters: gene ontology from level 3 to level 9, using All_Experimental_evidence, kappa score set at 0.
RT-qPCR Validation The RNA-Seq data were validated with RT-qPCR using a subset of 10 genes involved in cell wall biogenesis. Reverse transcription was carried out with the ProtoScript II Reverse Transcriptase (NEB) following the manufacturer's instructions. Primers were designed with Primer3 and validated for the absence of dimers and secondary structures (hairpin) using OligoAnalyzer 3. 1 2. qPCR runs were performed in 384 wellplates, on a ViiA7 Real-Time PCR System (Applied Biosystems) with the Takyon SYBR Green low ROX (Eurogentec). A melt curve was realized at the end of each experiment to ensure the specificity of the products. Relative gene expressions were determined with the qBase PLUS software v2. 5 (Biogazelle). The geNorm analysis  (Vandesompele et al. , 2002).
Hemp Hypocotyl Transcriptome Dynamics as Seen from a Cell Wall Angle Based on the general analysis of their transcriptomes, one can separate H6 and H9 from H15 and H20. For further characterisation of the hypocotyl transcriptome dynamics, a focused comparison between H6 and H20 will hereby be made. As a result, from the 3622 transcripts used to build the ICA and clustering (Figure  6 ; Supplementary Image S2), those associated with an Arabidopsis locus ID and showing a difference corresponding to -1 > log 2 FC (H20/H6) > 1 were used to compare H6 and H20. MapMan functional annotations highlighted the differences between these two time points (Figure  7A ; Supplementary Table  S5 ). Seven categories showed significant differences (p-value < 0. 05): photosynthesis, cell, cell wall, gluconeogenesis/glyoxylate cycle, glycolysis, nucleotide metabolism, fermentation (Supplementary Table  S5 ).
Expansins loosening the cell wall are thought to be active in 'biomechanical hotspots' where xyloglucan and cellulose are closely intertwined  (Park and Cosgrove, 2015). XTH31 was more expressed in the actively growing hypocotyl (H6), suggesting its potential role in cell expansion  (Franková and Fry, 2013). In elongating tissues, βGAL10 may have an activity on xyloglucan  (Sampedro et al. , 2012). Cell wall loosening is also controlled by peroxidases  (Francoz et al. , 2015) , which break covalent bonds in cell wall polymers. Notably, in our dataset, the ortholog of AtPRX57 was strongly upregulated at H6 (Supplementary Table  S6 ). The deduced protein sequence showed the presence of a signal peptide, a feature supporting the potential role of this peroxidase in cell wall loosening during the growth of the young hemp hypocotyl.
Transcripts Involved in the SCW Biogenesis Our data showed that the genes involved in the biosynthesis of xylan, cellulose, monolignols and in lignin polymerisation were co-expressed in H15 and H20 (Supplementary Table  S6 ). Such a co-expression may result from the regulation of SCW deposition by TFs, i. e. , NST1 for the fiber fate  (Hao and Mohnen, 2014)  and VND1 for vessels  (Zhong and Ye, 2014). In our dataset, the cellulose synthases which were more expressed during secondary growth were the Arabidopsis orthologs of CesA4, CesA7, and CesA8, as already previously observed in adult hemp  (De Pauw et al. , 2007; van den Broeck et al. , 2008). CesAs require sucrose synthase (SUS) activity for the provision of the UDP-glucose  (Doblin et al. , 2002). The Arabidopsis orthologs of SUS4 and SUS6 were found to be differentially expressed between young and old hypocotyls (Supplementary Table  S6 ).
, 2015). For instance, auxins are known to induce cell elongation via the expansin and XTH activities  (Sánchez-Rodríguez et al. , 2010). In our dataset, EXPA5, EXPA8, XTH5, and XTH8 were more expressed in H6 and H9, where the endogenous bioactive auxin content (IAA3 and PAA) is higher. Another property of auxin is to prevent lignification by promoting the degradation of IAA3  (Nanao et al. , 2014; Oh et al. , 2014). In Arabidopsis seedlings, the gain-of-function iaa3/shy2-2 mutant shows higher expression of several genes belonging to the phenylpropanoid/lignin pathway, such as PAL1, PAL2, C4H, 4CL1, CCoAOMT, and PRX52  (Oh et al. , 2014). Indeed, the lowest bioactive auxin levels, recovered in H15 and H20, correspond to the highest abundance of these genes. However, auxin may also induce the expression of some genes involved in lignin biosynthesis such as COMT and CCoAOMT through the binding of the TF BREVIPEDICELLUS to their promoter region  (Sánchez-Rodríguez et al.
Traditional approaches of mobility studies used GPS to accurately sense spatial data with a localization error bound ≤ 50m. Although it ensures the collection of fine-grained mobility trajectories (as shown in Fig. 1b ), GPS-based data collection has two main drawbacks: first, it causes high energy consumption, and second, it is constrained to a limited group of users (e. g. taxi drivers  [4]  or a group of car drivers  [5] ). GPS sensing, therefore, is not suitable for collecting large-scale data from metropolitan area populations. By contrast, cellular data provided by network operators does not suffer from these issues, and has become recently, as a result, a new source of mobility information. Signaling information from mobile network operators (CDRs -Call Data Records-) has been used as a valuable source of mobility information for large scale population  [3, 6, 7].
Thus, even though it has been studied widely, CDR is unlikely to be a good data source for the trajectory mapping problem. Considering the time sparsity drawbacks of CDRs, we use, in this work, a new passive capturing technique to efficiently extract the position of the base stations the mobile phone is connected to. This technique analyzes the signaling channel of the data mobile network in order to extract the base station locations. This way of capturing the mobility of users is scalable and provides a higher sampling rate than CDR-based sensing. The sparse cellular trajectories are collected and provided upon the request  The common approach for mapping cellular trajectories into the metropolitan transportation (usually road) network is to first collect a large amount of cellular trajectories and then to manually label each cellular trajectory with the corresponding intersection sequence, an intersection being a graph node associated with a junction between two roads.
Given the above examples and target research goals, the key issues in designing the unsupervised mobility model include: 1) Given the antenna id sequence in a cellular trajectory, retrieve the sequence of road/rail intersections that the user passes by given a database storing the multimodal transportation network -The transportation network covering and connecting multiple types of transportation modes (e. g. , rail, metro, highway , etc. ) is named multimodal transportation network  [9] , in which each node is either a road intersection or a station of a rail transportation mode (i. e. , subway, tramway and train), and each edge is a connection between intersections (e. g. , the pathway connecting a metro station and a bus stop). Obviously, it is nontrivial to extract the precise user path from the multimodal transportation network using the antenna id sequences.
The cellular trajectory might come from multiple transportation systems nearby each corresponding antenna and in different layers (underground, ground and trestle). To overcome this issue, it is necessary to build a comprehensive database storing all the intersections of the multimodal transportation network, where we can accurately retrieve the surrounding intersections of each antenna. In we propose an unsupervised HMM that does not leverage labeled data. Rather, it proposes a method to calculate the likelihood using the topological properties and other information of the transportation network. In other words, the HMM parameters are automatically derived in an unsupervised way based on a priori knowledge of transportation network properties. In summary, the main contributions of this work are: • We propose to study the problem of mapping cellular trajectories to the multimodal transportation network, in order to obtain the precise mobility of the users.
To the best of our knowledge, this is the first work addressing these issues. In particular, rather than mapping cellular trajectories using the supervised mapping algorithms with labeled mobility data, we propose an unsupervised mapping algorithm leveraging the topological properties of the transportation network, thus eliminating the tedious human labeling efforts for building the mobility model. • We propose an unsupervised trajectory mapping algorithm, namely CT-Mapper, which maps cellular location data over the multimodal transportation network. The multimodal transportation network database was built using different references of geospatial resources. The mapping algorithm is modeled by an HMM where the observations correspond to user cellular trajectories and the hidden states are associated with nodes of the multilayer graph.
Transition probability and emission score were modeled based on topological properties of the transportation network and the spatial distribution of antenna base stations. The Viterbi decoding algorithm helps reduce the complexity of finding the best match which might enable us to deploy our unsupervised mapping algorithm on large scale mobility data sets in order to estimate multimodal traffic in metropolitan areas. • We collect real cellular trajectories of a group of users in the Paris metropolitan area with the help of a French telecom operator, then evaluate our mapping algorithm using the data. Through the extensive evaluation with cellular trajectories covering more than 2500 intersection nodes and 3 physical layers , 1000 metro and subway stations, we show that our algorithm maps the cellular trajectory onto the multimodal transportation network of the Paris metropolitan area with good accuracy given the sparsity of user cellular trajectories.
Mapping Algorithms Along with mobility studies, applications such as navigation systems, traffic monitoring and public transportation tracking, have used GPS data to track individuals or any moving object  [17, 18, 19, 20, 21, 22]. A variety of statistical approaches such as Expectation Maximization (EM)  [22] , Kalman Filter  [20, 21]  and Hidden Markov Model (HMM)  [23, 17, 18, 19, 24]  were used to map noisy sequential location data over transportation networks. Most of these mapping algorithms have used GPS data as they provide accurate location data with an error of about 50 meters. Moreover, using labeled data, supervised models were trained to optimize model parameters in an automatic way. Once the models are trained, they are used to find the most likely path in the network assigned to sequences of noisy location data.
Human Mobility Modeling with CDR Cellular Trajectories Because of the expeditious growth of mobile phones, Call Data Records (CDR) have been recently providing great data sets for human mobility studies as they are collected continuously for all active cellular phones. CDRs, however, have two significant limitations: first, they are sparse in time because they are generated only when a phone engages in a voice call or text message exchange; and second, they are coarse in space and less precise than GPS location data, because they record location only at the granularity of a cellular antenna (with an average error of 175 meters in dense populated areas and up to 2 kilometers in non-dense areas). Nonetheless, the fact that almost the entire population is already equipped with cell phones  [3]  allows for studying important aspects of individual mobility such as inferring transportation modes.
Computational Complexity of the Mapping Problem in the Collected Datasets The underlying transportation network used in this study is the multimodal transportation network of Ile-de-France which is modeled by several separated graph layers corresponding each to a different transportation mode, interconnected together into a multilayer graph G. To build this graph, multiple geospatial datasets, namely the road network from the National Geographic Institute (IGN)[29] and the rail transport network (train and metro) from Open-StreetMap (OSM)  [30]  were aggregated. Each node in G is either a road in-.
Dataset for Evaluation In order to evaluate the proposed algorithm, GPS data are used as ground truth. We collected the cellular trajectories of 10 volunteer participants during one month (Aug-Sept 2014) with their corresponding GPS data. The GPS data were collected with the help of the application "Moves"  [28] , which was installed on the participants' smartphones. The data captured were the sampled positions of the phone during its movements as well as its activities classified in four different categories: 'Walking', 'Running', 'Cycling' and 'Transport'. Based on this dataset, several prepossessing steps were performed in order to extract the trajectories to be mapped over the transport networks. Trajectories whose lengths are shorter than 5 kilometers were filtered out from the database.
Given the low sampling rate of the cellular data (a data point every 15 minutes), it is not realistic to seek recovering a movement with shorter duration than this threshold. The effect of this filter on the dataset distribution can be observed in Fig. 7a  and Fig. 7b. The spatial accuracy needed to distinguish a real mobility from noise depends on the distance between two base stations. In order to discard irrelevant movements, we filtered out all the trajectories under the threshold x th such that P r (X < x th ) = q, where P r (X) is the distribution of distance between neighboring antennas. As Fig. 8  shows, for q = 0. 97, all the neighboring distances are less than 5 kilometers. The average number of observation points in each cellular trajectory is 5. 55 and the average length of a trajectory is 26.
Mapping algorithm efficiency To assess the effectiveness of our algorithm, the aforementioned labeled dataset was used for test and evaluation. We applied CT-Mapper to map the to transform one sequence into the other. We evaluate the two phases of the algorithm by calculating the edit-based similarity scores for both the skeleton and the complete mapped sequence. To have a comprehensive insight, we also calculate the average recall and precision of the results for dataset trajectories. Considering each trajectory as a set of nodes, precision is the fraction of retrieved nodes that belong to the real path. Recall (also known as sensitivity) is the fraction of correct nodes that are retrieved by the algorithm. Moreover, in the evaluation section, the Root Mean Square Error (RMSE) is used for two purposes: First, RMSE is computed to quantify the overall distance between the obtained result and the ground truth.
This is remarkable given the sparsity of the coarse grain cellular antenna positions with respect to user real trajectory (average of 5. 5 observations per trajectory in the dataset while the average length is 26. 5 km). It is important to mention that the frequency of cellular data collection is 15 minutes and, therefore, higher performances are expected if observations with higher frequencies are input to CT-Mapper. The average similarity score, for a RMSE of 1 kilometer, raises to 80%. In addition, CT-Mapper reaches a recall and a precision of around 80% when a RMSE of 1 kilometer is allowed. In addition to the metrics mentioned above, we compute the Edit distance error not as the number of required edits, but by considering the euclidean distance as the cost of each required edit. The average of Edit distances for all trajectories in the dataset is 0.
Discussion & Conclusion In this study, we proposed an unsupervised mapping algorithm (CT-Mapper) to map sparse cellular trajectories over a multimodal transportation network. We modeled and built the multilayer transportation network of subway, train and road layers for the Ile-de-France (Paris) metropolitan area. The multilayer transportation network contains around 16000 nodes and 26000 edges. Our model considers a transition probability between nodes that is inversely proportional to the average expected time over the paths between these two nodes. Nonetheless, our model does not take into account the time information stemming from the input observation sequence associated with a user trajectory. One of the reasons is that we use Sparse Cellular trajectories of data points sampled every 15 minutes.