dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptomic Analysis RNA sequencing generated 217 million raw reads in total with 75 bp for both paired ends. After removal of adaptor sequences and improving the stringency quality, we obtained 14 gigabases of cleaned data, and 49,015,843 nucleotides (nt) were generated. To analyze gene expression, the reads were mapped against the W. magna C2c genome, resulting in 86. 1% of reads mapped. The expression of the W. magna genes was examined and 8,790 W. magna C2c transcripts (47. 4% of W. magna ORFs) were detected. Among the 8,790 transcripts, BLASTp analysis against the nr database found homologous sequences for 6,179 (70. 3%) of the transcribed genes and identified 2,611 ORFans among the transcript genes (29. 7%) (Table  S1 ). Of the ten genes with the highest coverage (mean coverage of 4,316 reads/gene), two transcripts were assigned to a function (bactericidal permeability-increasing protein/lipopolysaccharide-binding protein and histidine triad motif (HIT) family protein), five were annotated as hypothetical proteins and two as ORFans (Table  S1 ).
To further analyze the most highly expressed genes, we studied the conserved domains of hypothetical proteins and ORFans. Among the five hypothetical proteins, we found a conserved domain for three predict genes, including a glutathione S-transferase domain, a WD40 domain and a flavoprotein domain (Table  S1 ). For one of two ORFs with no match in the nr database, we identified one F-box domain (Table  S1 ).
The expression of the W. magna genes was examined and 8,790 W. magna C2c transcripts (47. 4% of W. magna ORFs) were detected. Among the 8,790 transcripts, BLASTp analysis against the nr database found homologous sequences for 6,179 (70. 3%) of the transcribed genes and identified 2,611 ORFans among the transcript genes (29. 7%) (Table  S1 ). Of the ten genes with the highest coverage (mean coverage of 4,316 reads/gene), two transcripts were assigned to a function (bactericidal permeability-increasing protein/lipopolysaccharide-binding protein and histidine triad motif (HIT) family protein), five were annotated as hypothetical proteins and two as ORFans (Table  S1 ). To further analyze the most highly expressed genes, we studied the conserved domains of hypothetical proteins and ORFans. Among the five hypothetical proteins, we found a conserved domain for three predict genes, including a glutathione S-transferase domain, a WD40 domain and a flavoprotein domain (Table  S1 ).
Mean Proteomic Information The RNA-seq analysis was supplemented with a proteomic analysis carried out on two biological replicates of W. magna grown in the bioreactor. The lysate concentrations of replicates 1 and 2 were estimated using the micro BCA method at 11. 6 and 15. 6 mg/mL, respectively. A total of 20 µg of each sample were separated on SDS-PAGE gel and proteins were visualized using Stain-Free™ detection (Figure  1 ). Expressed proteins were identified using the Peaks Studio program (Table  S2 )  [57]. A total of 106,383 spectra along with 64,260 peptides were identified. In total, we detected 3,561 proteins, which represented 19. 2% of the predicted genes and 40% of the transcriptome (Figure  2  and Table  S1 ). Expressed proteins were identified using the Peaks Studio program (Table  S2 )  [57].
A total of 106,383 spectra along with 64,260 peptides were identified. In total, we detected 3561 proteins, which represented 19. 2% of the predicted genes and 40% of the transcriptome (Figure  2  and Table  S1 ). As we processed the proteomic analysis twice, we detected 2,970 proteins from the first analysis and 3,186 proteins from the second one (Table  S2 ). The annotation using BLASTp against the National Center of Biotechnology Information (NCBI) protein sequence database assigned a putative function to 3,376 proteins, among which 3,013 had as best hits proteins belonging to N. gruberi and one best matched with a giant virus (Moumouvirus Monve) protein. The taxonomical distribution revealed a very high proportion of genes shared with eukaryotes (97. 9%), followed by genes shared with bacteria (1.
COG Enrichment Analysis To further analyze the function of the transcripts and proteins, we conducted a comparison of the latter with the COG database (Figure  3 ). As we processed the proteomic analysis twice, we detected 2970 proteins from the first analysis and 3186 proteins from the second one (Table  S2 ). The annotation using BLASTp against the National Center of Biotechnology Information (NCBI) protein sequence database assigned a putative function to 3376 proteins, among which 3013 had as best hits proteins belonging to N. gruberi and one best matched with a giant virus (Moumouvirus Monve) protein. The taxonomical distribution revealed a very high proportion of genes shared with eukaryotes (97. 9%), followed by genes shared with bacteria (1. 9%), archaea (0. 15%) and viruses (0. 03%) (Figure  S1 ).
COG Enrichment Analysis To further analyze the function of the transcripts and proteins, we conducted a comparison of the latter with the COG database (Figure  3 ). A total of 5,341 (60. 7%) and 2,723 (76. 5%) transcripts and proteins, respectively, were assigned to COG categories (Table  S1 ). Overall, these transcripts and proteins were distributed in 23 COG categories. Regarding transcripts, the category "function unknown" (1,366: 25%) was the most represented, followed by "post-translational modification, protein turnover, chaperones" (636: 12%), "signal transduction mechanisms" (593: 11%), "intracellular trafficking, secretion and vesicular transport" (366: 7%) and "translation, ribosomal structure and biogenesis" (346: 6%). Regarding proteins, the COG category in which the greatest number of proteins were assigned is the category "function unknown" (543: 20%) followed by "post-translational modification, protein turnover, chaperones" (380: 14%), "translation, ribosomal structure and biogenesis" (270: 10%), "intracellular trafficking, secretion and vesicular transport" (220: 8%) and "signal transduction mechanisms" (214: 8%).
Defense Mechanisms To characterize the defense mechanisms of W. magna, we searched for the presence of predicted pattern-recognition receptors (PRRs) (Figure  5  and Table  S6 ). W. magna contains three members of the bactericidal permeability-increasing protein/lipopolysaccharide-binding protein family and a MD-2-related protein. All these four receptors play an important role in triggering defensive responses through their interactions with lipopolysaccharide of bacteria  [61]. This gene shared a best hit in the nr database with a N. gruberi sequence. Furthermore, we reported the presence of C-and H-lectin domains (three) that belong to a large family of receptors that bind carbohydrates and induce endocytic, phagocytic and antimicrobial responses  [35]  (Table  S6 ). Four mannose-binding proteins were revealed within the W.
magna contains three members of the bactericidal permeability-increasing protein/lipopolysaccharide-binding protein family and a MD-2-related protein. All these four receptors play an important role in triggering defensive responses through their interactions with lipopolysaccharide of bacteria  [61]. This gene shared a best hit in the nr database with a N. gruberi sequence. Furthermore, we reported the presence of C-and H-lectin domains (three) that belong to a large family of receptors that bind carbohydrates and induce endocytic, phagocytic and antimicrobial responses  [35]  (Table  S6 ). Four mannose-binding proteins were revealed within the W. magna transcriptome. Among them, two best matched with N. gruberi, one with Tetrahymena thermophila and one with Rathayibacter tanaceti. These glycoproteins are involved in the endocytosis of L.
Discussion This study analyzed for the first time the transcriptome and proteome of W. magna. It provides unique data on the molecular mechanisms and metabolic pathways used by W. magna C2c maky during axenic mass culture. Firstly, high throughput RNA-sequencing technology was carried out to obtain a full set of transcripts of the amoeba in bioreactor culture condition. A similar dataset was generated for the transcriptome of Naegleria fowleri  [72]  performed with Illumina HiSeq 2000 technology. Among the 8,790 transcripts, 70. 3% matched with known proteins in the nr database. The absence of matches in the nr database for the remaining transcripts was probably due to the currently We also identified two genes with multidomains related to polyketide synthases producing natural metabolites with antimicrobial properties  [66, 67]  (Figure  S5 ).
Discussion This study analyzed for the first time the transcriptome and proteome of W. magna. It provides unique data on the molecular mechanisms and metabolic pathways used by W. magna C2c maky during axenic mass culture. Firstly, high throughput RNA-sequencing technology was carried out to obtain a full set of transcripts of the amoeba in bioreactor culture condition. A similar dataset was generated for the transcriptome of Naegleria fowleri  [72]  performed with Illumina HiSeq 2000 technology. Among the 8,790 transcripts, 70. 3% matched with known proteins in the nr database. The absence of matches in the nr database for the remaining transcripts was probably due to the currently limited set of sequenced amoebal genomes. Indeed, only the genomes of A. castellanii, Acanthamoeba triangularis, Vermamoeba vermiformis, Balamuthia mandrillaris and four genomes of the Vahlkampfiidae family (W.
Supplementary Materials: The following are available online at http://www. mdpi. com/2076-2607/8/5/771/s1, Figure  S1 : Representation of taxonomic distribution of protein assigned to a function in nr database, Figure  S2 : Phylogenetic tree for W. magna [FeFe] hydrogenase maturation protein, Figure  S3 : Phylogenetic tree for W. magna [FeFe] hydrogenase maturation protein, Figure  S4 : Phylogenetic tree for [FeFe]-hydrogenase maturation protein HydG and Figure  S5 : Representation of conserved domain of beta-ketoacyl synthase; Table  S1 : Transcriptomic and proteomic annotation, Table  S2 : Proteomic data, Table  S3 : KEGG annotation, Table  S4 : Motility study, Table  S5 : Metabolism study, Table  S6 : Mechanism defense study and Table  S7 : Secondary metabolism study.
Acknowledgments: The authors thank  Anass Jawhari  and  Sebastien Igonet of Calixar company (Lyon, France)  for their contribution to the work of total Willaertia magna proteins extraction. The authors acknowledge the financial support from  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences and Health)  within the framework of the cancer plan for Orbitrap mass spectrometer founding and  Adeline Page  and  Frédéric Delolme  for performing the mass spectrometry analysis ( Protein Science Facility ,  SFR BioSciences CNRS   UMS3444 , Inserm  US8 ,  UCBL ,  ENS de Lyon , 50  Avenue Tony Garnier , 69007 Lyon, France).
Over SWA, some recent efforts have been undertaken to shed more light on the distribution and regional importance of warm rain. Based on a 16-year TRMM precipitation radar observation,  [9]  estimated that warm rain contributes at least 2% to total annual rainfall at the immediate coastal region of SWA and that such warm rain events predominantly occur in July and August. [10]  developed a SWA-calibrated method to delineate precipitating liquid clouds using the spatio-temporally high-resolution dataset of the Spinning Enhanced Visible and Infrared Imager (SEVIRI). They found a unimodal seasonal cycle of the occurrence frequency with a distinct peak in August, which corresponds well with the results in  [9]. They further estimated warm rain frequency over SWA, and found it to be larger over the coastlines, from 5% in Nigeria to 15% in Liberia.
The study by  [14]  has proven to resolve shallow convection with this resolution. Our methodology to explore this event relies on a comparison of the simulated warm-rain cells and those observed by the X-band radar. The detection of warm-rain cells that lie, by definition, below 4-km altitude, is based on the clustering of reflectivity fields to delimit a volume of precipitating hydrometeors. This allows the characterization of the warm-rain cells in terms of their size, spatial distribution and vertical development. Then, the warm-rain cells are tracked in time, and the lifetime of each track is computed. To the best of the authors' knowledge, this is the first time that the size or lifetime of warm-rain cells over SWA are statistically quantified. The paper is organized as follows: Section 2 describes the observational datasets employed to characterize warm rain, particularly the X-band radar and the Meso-NH simulation, and the analysis approach.
Detection and Tracking of Warm-Rain Cells Warm rain is detected every 5 min from 03:00 to 08:00 UTC. To ease the processing, the native polar grid of the radar reflectivities (∆r = 500 m) is transformed into a Cartesian grid with ∆x = ∆y = ∆z = 500 m. For both the X-band radar observations and the simulation, the identification is done on the domain covering the radar range with a shared horizontal grid mesh of 1 km and the native vertical grid of each dataset. The method relies on the clustering algorithm developed by  [30]. It consists of locating the grid points where a condition is verified, and grouping the contiguous points into a three-dimensional object, herein named warm-rain cell. To apply this algorithm, the 3-D reflectivities are first projected into the horizontal, such that the resulting fields correspond to the maximum values of reflectivity over the column.
Then, a condition on the intensity of the maximum reflectivity is used to delimit the horizontal spread of the rain cells: it must be larger than 20 dBZ. Thresholds ranging from 18 to 20 dBZ are commonly used to identify light to moderate rain above 0. 5 mm h -1 (e. g. ,  [3, [31] [32] [33] ). Figure  3  shows the cumulative frequency distribution of the maximum reflectivity values to which the 20 dBZ threshold is applied, for the radar and model data. The distribution is shifted to lower values in the model with respect to the radar. While 16. 9% of the observed reflectivity values exceed 20 dBZ, only 8. 6% do in the simulation. A top is then associated with each identified cluster, defined as the highest altitude where a reflectivity of 20 dBZ is found inside the cluster for both datasets.
35% of total rainfall in Savè. Deep-convective clouds develop in the afternoon and evening, reaching 8-10 km altitude before 21:00 UTC and up to 12 km after 21:00 UTC. In the following, our statistical analysis of the warm-rain cells based on the X-band radar is done from 03:00 to 08:00 UTC, when warm rain is reported at Savè. We inspect how total rainfall is spatially distributed in the simulation, and compare that with two gridded satellite observations, the TRMM 3B42V7 product (3 h, 0. 25 • ) and the GPM IMERGV06B product (30 min, 0. 1 • ). Figure  6  shows maps of the total accumulated rainfall from 03:00 to 09:00 UTC for the three datasets, as well as warm rain for the simulation. The analysis of rainfall at ground is done from 03:00 to 09:00 UTC (instead of 08:00 UTC) due to the 3-hourly temporal resolution of the TRMM product.
Characteristics of the Warm-Rain Cells The relative distributions of the effective diameter of the warm-rain cells and the lifetime of the tracked warm-rain cells are represented in Figure  7  for the X-band radar and the simulation. The lifetime distribution is shown for a 20% geographical overlap. In both datasets, the number of warm-rain cells decreases with increasing diameter. The total number of 1057 observed warm-rain cells is in agreement with the number detected by the X-band radar, which is in average 20 per 5 min scan. Only 485 warm-rain cells are simulated. Although the simulated warm-rain cell population counts about half the observed number, the relative distribution is well captured. The large majority of warm-rain cells have diameters below 5 km, approximately 78% in the observations and 81% in the simulation.
Although the simulated 1. 2% contribution to warm rain is only calculated for a 6-h period, it compares well in order of magnitude with the 1. 35% contribution in Savè during the DACCIWA campaign, and with the 2% estimation by  [9]  in its climatology over the coastal regions of SWA. This is the first time that warm-rain amounts are quantified from a LES over SWA. The results shown here remain, however, limited, as only one single warm-rain event was studied. Other cases should be investigated. Currently unknown tendencies and seasonal and interannual variability of warm rain in SWA could be addressed by applying the reliable yet inexpensive warm-rain cell detection method used here to longer simulations or radar databases. This study reflects that uncertainties are intrinsic to any data source, which contributes to the known issue of model-observation comparison.
Data Availability The DACCIWA data from the Savè supersite are available on the SEDOO database (http://baobab. sedoo. fr/DACCIWA/). The Meso-NH output data are available from I. R. M. upon request. Author Contributions: I. R. M. designed and performed the simulation and analyses. J. -P. C. participated in the design of the simulation. J. H. provided the X-band radar and disdrometer data. I. R. M. prepared the manuscript with contributions from all co-authors.
Acknowledgments: Computer resources were allocated by  GENCI  through Project  90569. We thank  Norbert Kalthoff  ( Karlsruhe Institute of Technology ) for the ground-based campaign organization at Savè, and  Fabienne Lohou  ( Laboratoire d'Aérologie ) for the radiosonde measurements. We thank the anonymous referees for their comments, which helped to improve the overall quality of the paper.
Our primary objective was therefore to explore the robustness of sex difference in visuomotor tracking by taking advantage of a large dataset that was previously collected in our lab  [20] [21] [22]. More important, we aimed at seeking for the mechanism underlying sex differences. To do so, we carefully examined whether men and women would employ similar strategies in terms of hand movements and/or gaze control. Our first hypothesis was that men could exhibit smoother and less intermittent movements than women. Examining hand movements kinematics was found highly valuable for reporting sex difference in throwing 23 , pointing  24, 25  , and lifting 26. Here, we examined several kinematic properties of complex visuomanual trajectories such as the frequency content, the complexity, smoothness, and intermittency of hand movements to produce a detailed examination of sex differences in complex, dynamical visuomotor transformations.
Such hypothesis was motivated by several studies showing that men exhibit faster reaction times to visual stimuli than women  [36] [37] [38] [39] [40] [41]. Interestingly all previous studies suggesting a male advantage in manual tracking  [16] [17] [18] [19]  focused on the accuracy of hand movements in the spatial domain but did not characterize their precision in the temporal domain. Here, the temporal lag between hand motion and target motion would be of particular interest as it provides key information about the speed of visuomotor processing. To explore sex difference in human visuomotor behaviors, we analyzed two large datasets in which men and women were required to track a visual target that followed an unpredictable trajectory. Participants had to move a cursor using a joystick held by the hand.
By estimating the spatial and temporal accuracies of both hand and eye movements, we can tease apart between the above-mentioned hypothesis: smoother hand movements, more accurate gaze tracking or better timing in mapping target and hand/eye trajectories. Moreover, behavioral studies investigating sex differences are often limited dataset sizes. Our strategy was to perform a detailed kinematics analysis on a large database. To do so, we built a first cohort of participants by aggregating data from three previous and distinct experiments  [20] [21] [22]  , in which gaze behavior was monitored. We then tested the robustness of our fist results with a second cohort of new, naïve participants extracted from an ongoing project, in which we did not monitor gaze behavior.
Methods Participants. The first dataset comes from three separate cohorts of participants, issued from three separate experiments  [20] [21] [22]  , leading to a total of 71 participants. After the exclusion of 9 participants that participated in several studies, this number dropped to 62 (31 females, 31 males). Because the mean age of the male group was higher than the female one (28. 6 vs. 25. 0 years), 12 additional participants (oldest males and youngest females) were discarded so as to end up with similar age groups (25 females; 25. 5 ± 2. 6 years of age; 25 males; 25. 5 ± 3. 8 years of age); note however that similar observations were obtained with the full data set (with 62 participants). The second dataset was composed of 77 new participants (51 females; 21. 4 ± 2. 5 years of age; 26 males; 22.
Results We successively present the data from the first cohort in which both eye and hand movements were recorded, and then the data from the second cohort in order to test the robustness of our findings regarding manual tracking and sex differences. Manual tracking is more accurate in male. Figure  3  plots one representative trial from one male and one female participant. As can be seen hand tracking was somewhat poorer in the female participant. In the next sections we analyze in more detail this observation over the first cohort. Female participants had significantly larger tracking errors than male participants (see Fig. 4A ). Specifically, the cursor-target distance was 16% greater in women compared to men [hereafter mean ± SD; F = 1. 87 ± 0. 42 vs. M = 1. 62 ± 0. 30 cm; t(48) = 2. 43; p = 0.
22 vs. F = 1. 48 ± 0. 26 ms; t(75) = 1. 71; p = 0. 09; d = 0. 42]. These analyses conducted on a second pool of participants account for the robustness of our previous observations. Figure  8  presents the overall distribution of manual tracking performance when pooling the two cohorts of participants (127 in total). Again the cursor-target distance was found greater in women than men [F = 1. 80 ± 0. 34 vs. M = 1. 58 ± 0. Our experimental design ran over 10 consecutive trials. The difference observed between males and females could be due to a different dynamics of performance over the course of these trials. To investigate such temporal dynamics in more details, we performed a two-way ANOVA (TRIALS × SEX) for each dataset. For both datasets we found a main effect of TRIAL (p < 0. 001) and SEX (p < 0.
01). But more important, the SEX by TRIAL interaction never reached significance (p > 0. 24), thereby demonstrating that the male advantage, as evidenced by a smaller cursor-target lag, is robust over the course of our experimental paradigm. Finally to further test the robustness of a sex difference, we performed one more analysis. As explained above we carefully matched age distribution between male and female distribution by removing a few (12) participants from our complete database. To discard that such age normalization has not introduced any bias, we repeated our analysis on hand tracking over the full, aggregated database (i. e. 139 participants). Indeed, we obtained.
Acknowledgements We thank  Cédric Goulon  and  Franck Buloup  for technical support and providing respectively their ICE and Docometre software (courtesy of  Institut des Sciences du Mouvement, Marseille, France). This work was part of  Innovative Training Network 'Perception and Action in Complex Environment' (PACE ) that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement N°  642961. GSM is supported by the  Fondation pour la Recherche Médicale  ( FRM Team 2018 ). This paper reflects only the authors' view and that the  Research Executive Agency (REA)  of the  European Commission  is not responsible for any use that may be made of the information it contains.