dataesr/scientific-paragraphs-categorization huggingface dataset:
They form our reference set of systems (Table  S1 ). This reference dataset was used to build the models and the corresponding HMM protein profiles (see below) of each system using MacSyFinder. This software is publicly available  25. A detailed explanation of this program can be found in its original publication  25. Here, we focus on the features that are pertinent for this work. A model in MacSyFinder defines the components of the secretion system, the minimal acceptable number of components, and their genetic organisation. Among other things (see http://macsyfinder. readthedocs. org for full documentation), one can specify the following relevant information. 1) Systems are encoded in a single locus (single-locus system) or in several loci (multi-loci system). 2) Core components (ubiquitous and essential) are defined as mandatory.
7) Two components are co-localised when they are separated by less than a given number of genes (parameter d = inter_gene_max_space). 8) A component defined with the loner attribute does not need to be co-localised with other components to be part of a system (e. g. , OMF in T1SS). 9) A component that can participate in several instances of a system (e. g. , OMF in T1SS) receives the multi_system attribute. These different properties can be combined when necessary. The models for the different protein secretion systems were described using a dedicated Extensible Markup Language (XML) grammar  25. The files with the models were named after the system (e. g. , T1SS. xml, File S1). Models can be easily modified on the standalone version of MacSyFinder. The webserver allows the use of the pre-defined models and the modification of the most important search parameters.
MacSyFinder was used to identify protein secretion systems in bacterial genomes in three steps (for corresponding command-lines see the README file in File S1, and for a full description of the software see  25  ). Firstly, components were identified using protein profile searches with HMMER  41. Hits with alignments covering more than 50% of the protein profile and with an i-evalue < 10 -3 were kept for further analysis (default parameters). Secondly, the components were clustered according to their proximity in the genome using the parameter d. Finally, the clusters were validated if they passed the criteria specified in the model.
johnsoniae (or P. gingivalis when the gene was absent from F. jonhsoniae) for Blastp searches against our database of complete genomes, and retained the best sequence hits (e-value < 10 -20 ) to constitute protein families. A similar approach was taken to build protein profiles for the T6SS ii , based on the Francisella tularensis subsp. tularensis SCHU S4 FPI system displayed in Table  1  of  39. The largest families were aligned and manually curated to produce hidden Markov model profiles with HMMER 3. 0  41.
Availability. Detection and visualization of all systems described in this paper can be performed online on the Mobyle-based  108  webserver TXSScan: http://mobyle. pasteur. fr/cgi-bin/portal. py#forms::txsscan. Detection can also be performed locally using the standalone program MacSyFinder  25  , and the sets of models and profiles described here. MacSyFinder is freely available for all platforms at https://github. com/gem-pasteur/macsyfinder. Models and required protein profiles are available as supplemental material (File S1) at https://research. pasteur. fr/en/tool/txsscan-models-and-profiles-for-protein-secretion-systems. The models are provided as simple text (XML) files, so they can be easily modified and extended by the user. The results of MacSyFinder can be visualized with MacSyView, available online at http://macsyview.
Acknowledgements We are grateful to  Elie Dassa ,  Olivera Francetic , and  Marc Garcia-Garcerà  for fruitful discussions, and  Hervé Ménager  for its contribution to MacSyView. We thank  Eric Duchaud  for discussions and comments on T9SS. This work was supported by the  CNRS , the  Institut Pasteur  and the  European Research Council  (grant EVOMOBILOME, number  281605 ). JC is a member of the  French doctoral school "Ecole Doctorale Interdisciplinaire Europé enne Frontières du Vivant   ED474 ".
Model Systems detected in the validation dataset * (detected/total) Systems detected in the reference dataset (detected/total) T1SS 6/6 8/8 T2SS 14/18 9/9 T3SS See 24 See 24 T4SS See 67,107 See 67,107 T5aSS 4/4 NA (PFAM) T5bSS 2/3 6/6 T5cSS 2/2 NA (PFAM) T6SS i 8/8 # 9/9 T6SS ii NA 1/1 T6SS iii NA 3/3 T9SS NA 2/2.
Stimuli Both experiments were written in MATLAB (RRID: SCR_ 001622) using the Psychophysics Toolbox 3 (RRID: SCR_ 002881,  Brainard, 1997; Pelli, 1997). The stimuli were presented on a black background using a cathode ray tube monitor operated at 100 Hz and situated in a dark room. Target stimuli consisted of lowcontrast Gabor patches tilted 10 degrees, and with a diameter of 0. 75 degrees of visual angle. The stimuli were displayed at 10 degrees of visual angle to the left or to the right of the fixation cross for a duration of two frames (0. 02 s). For both experiments, stimulus side and tilt were counterbalanced. In Experiment 1, the target stimulus was present in 60% of the trials and absent in 40% of the trials, during which a blank screen was presented for a duration of two frames (0.
EEG recording and preprocessing A 64-channels ActiveTwo system (Biosemi) was used to record continuous EEG sampled at 1024 Hz while the participants performed the experimental tasks. Electrodes were placed according to the international 10 -10 system. The horizontal and vertical electro-oculograms were recorded by attaching additional electrodes at the lateral canthi of both eyes and below the eyes, respectively. The EEGLAB toolbox version 11 running on MATLAB (R2010b; The MathWorks) was used to process and analyze the data  (Delorme and Makeig, 2004). Data were rereferenced to the mastoids, epoched from Ϫ2000 to 1500 ms relative to target onset, down-sampled to 256 Hz, and bandpass filtered between 0. 25 and 80 Hz, as recommended by  Widmann et al. (2015). Major artifacts (eye blinks, and noisy data segments) were screened manually, and entire trials were discarded when a blink occurred within a 1 s time window preceding target onset, to ensure that participant's eyes were open at stimulus onset.
Time-frequency analysis Time-frequency analysis was performed using a wavelet transform (Morlet wavelets, frequency range: 1-30 Hz, number of cycles increasing linearly from 1 to 12). Thus, a wavelet at 10 Hz was 4. 4 cycles long and had a temporal resolution t of 0. 14 s and a spectral resolution f of 4. 53 Hz. Frequencies and time points were sampled every 2 Hz and 40 ms, respectively. Because we were primarily interested in the prestimulus time range, no baseline correction was applied. Because wavelet analysis is computed by convolution of the data with a function that is extended in time, it is conceivable that prestimulus effects close to stimulus onset are actually affected by poststimulus data. To determine the extent of this contamination, we applied a timefrequency transform using the same settings as in the main analysis to synthetic data (i.
e. , a sinusoidal oscillation of exactly 10 Hz) (Fig. 3 ). The extent of temporal contamination caused by a wavelet is determined by the wavelet's temporal resolution t , which is defined as twice the SD of the Gaussian envelope  (Tallon-Baudry et al. , 1996). This simulation illustrates that prestimulus time points are indeed contami- nated by poststimulus data points. However, the magnitude of this contamination is virtually null at time points earlier than onset: t. Thus, we consider effects as truly "prestimulus" only if they occur before this limit, which is indicated by a red line in Figures  4 5 6 7. The MATLAB code used for this simulation can be downloaded from https://github. com/LucaIemi/Iemi2017_JNeurosci. EEG behavior analysis. To analyze how oscillatory power influences performance, we first identified trials with particularly weak and strong power, and then tested how these trials differed in performance measures.
The data might also be inconclusive (e. g. , due to insufficient statistical power). Thus, to directly estimate evidence for the null hypothesis, we used BF analysis  (Rouder et al. , 2009). We first estimated the JZS BF for the negative t statistics of the difference maps, setting the prior on effect size following a Cauchy distribution with a scale factor 0. 707, as recommended by  Rouder et al. (2009). BF indicates whether there is evidence for the alternative hypothesis (difference between bins, H 1 if BF Ͼ 3) or for the null hypothesis (no difference between bins, H 0 if BF Ͻ 1/3) or whether the evidence is inconclusive (if 1/3 Ͻ BF Ͻ 3). We then counted for each time point the proportion of electrodes and frequencies showing evidence for H 1 and H 0.
Methods Micromagnetic simulation. The micromagnetic simulations are carried out by using the open source software package MuMax3 available at http://mumax. github. io/. The three-dimensional time-dependent dynamics of DW motions is described by the modified Landau-Lifshitz-Gilbert (LLG) equation including the SOT terms (SHE and Rashba effect) shown as γ α β τ τ ∂ → ∂ = - ∂ ∂ → × → + → × ∂ → ∂ -→ ⋅ ∇ → → + → × → ⋅ ∇ → → -→ -→ t M E m m m t u m m u m m m ( ) [ ( ) ] (6).
Acknowledgements The authors would like to thank the supports by the projects from the  National Natural Science Foundation of China  ( Nos 61504006  and  61571023 ), the  International Collaboration Project  (No. 2015DFE12880 ) from the  Ministry of Science and Technology of China , the European  FP7 Project  (No. FP7-MAGWIRE ) and the  French National Agency Project  (No. ANR-DIPMEM ).
Experimental protocol and data The details of the study are illustrated in a previous work  (Ristagno et al 2014). Briefly, 12 male pigs (38 ± 1 kg) received anesthesia by intramuscular injection of ketamine (20 mg kg -1 ) and completed by ear vein injection of sodium pentobarbital (30 mg kg -1 ). Animals were mechanically ventilated with a tidal volume of 15 ml kg -1 and FiO 2 of 0. 2 l. The arterial blood pressure was continuously measured by a fluid-filled 7F catheter placed in the thoracic aorta from the right femoral artery. Myocardial infarction was induced in a closed-chest preparation by intraluminal occlusion of the left anterior descending (LAD) coronary artery  (Ristagno et al 2014). For inducing ventricular fibrillation, a 5F pacing catheter was advanced from the right subclavian vein into the right ventricle.
Data preprocessing The ABP signals were subdivided into five epochs. The portion of the recording just before the LAD occlusion was selected as the pre-CA phase. We had no a baseline phase before the anesthesia induction due to technical reasons. The other epochs were the post-resuscitation (Pr) phases consisting of segments of about 15 min within the 4 h after the stabilization of the animal and named as Pr 1 h, Pr 2 h, Pr 3 h and Pr 4 h. All the clinical events, the occlusion as well as the CPR, were annotated by the medical surgeon as a time marker in the signal files. For each phase, a window of ABP with a length ranging from 10 to 15 min was extracted by eyeballing. The windows were chosen to maximize the percentage of good quality heart cycles and were stationary. Each window was further divided into 50% overlapping 3 min segments.
All the analyses were performed in a MATLAB ® environment. For each segment we applied an automatic algorithm for the identification of the onset of heart cycle on the ABP waveform  (Zong et al 2003). The systolic arterial pressure (SAP) and diastolic arterial pressure (DAP) were identified respectively as the local maximum and minimum in time interval following the onset. The pulse pressure (PP) was estimated as the difference between the SAP value of the current cardiac cycle and the DAP value of the previous one. The heart cycle duration was estimated by considering the heart period (HP), which is the difference of two consecutive ABP onsets, considered as a surrogate of the RR time interval. The MATLAB code for these preliminary analyses is open source and freely available at www. physionet.
For example, in the 0-300 m layer RMS (root mean square) differences between analyzed fields and observations reach 0. 25 psu and 1. 25 • C in the western boundary currents and 0. 1 psu and 0. 75 • C in the open ocean. The impact of the Argo data in reducing observation-model forecast differences is also significant from the surface down to a depth of 2000 m. Differences between in situ observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow, the Gulf Stream region and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. Therefore, Argo observations matter to constrain the model solution, even for an eddy-permitting model configuration.
Introduction Argo is the first ever in situ ocean observing system providing in real-time observations at global scale. The initial target of 3000 profiling floats drifting in the ocean was reached by the international Argo program in November 2007. Mean coverage is one float in every 3 • × 3 • box. Every 10 days, each float measures temperature and salinity profiles from the surface to 2000 m and delivers data in real time, mostly for operational oceanography. Rigorous scientific quality control is applied by data centers in delayed time to guarantee optimal quality for this data set (e. g. ,  Cabanes et al. , 2010; Wong et al. , 2008). The use of Argo data is widespread in the ocean and climate research communities. Argo also provides critical observations to constrain ocean analysis and forecasting systems, together with satellite observations.
The evaluation of the degradation in quality of the resulting analyses and forecasts is then used to quantify the impact of the observations withheld. In the last decade, several studies based on OSEs have analyzed the impact of different components of the global ocean observing system for ocean analysis and forecasting. Balmaseda et al. (2007)  studied the statistical impact of Argo on analyses of the global ocean for the period  2001 -2006. Oke and Schiller (2007)  analyzed the importance of the combination of Argo, sea surface temperature (SST) and altimeter data on a regional eddy-resolving ocean reanalysis. Other relevant studies (e. g. ,  Vidard et al. , 2007; Tranchant et al. , 2008; Guinehut et al. , 2012  or more recently  Fujii et al. , 2014 and Lea et al. , 2014)  have focused on different observing systems and assessed their impact on analysis and forecasting systems.
The GODAE OceanView program (https://www. godae-oceanview. org/), created to promote and coordinate operational oceanography worldwide, has set up a specific OSE task team (OSEval-TT) to formulate requirements for the enhancement of the global ocean observing system  (Oke, 2015a, b). The Tropical Pacific Observing System 2020 GOOS project  (Global Ocean Observing System; OOPC, 2014)  has also identified the importance of OSEs for assessing the role of the Tropical Pacific Observing system from a data assimilation perspective. In this paper, we focus on the impact of the Argo observing system on the analyzed temperature (T ) and salinity (S) structure of the ocean in the context of short-term real-time ocean analysis and forecasts. Several OSEs are performed to assess the importance of Argo T and S profile data assimilation with the real-time Mercator Ocean global 0.
25 • system. All other data sets, non-Argo in situ data, SST and alongtrack SLA (sea level anomaly), are assimilated as it is done in the real-time system. The paper is organized as follows: the real-time analysis and forecasting system and the experimental strategy are described in Sect. 2. Section 3 details the results. The differences seen in the analyzed T and S fields depending on the amount of Argo data assimilated are first described. We then evaluate the Argo data assimilation impact on the forecasted fields by looking at the observation forecast misfit to the in situ T and S profiles, SST and SSH (sea surface height). The conclusions are given in Sect. 4.
Data assimilation system The Mercator Ocean 0. 25 • operational global ocean analysis and forecasting system  (Lellouche et al. , 2013)  (hereafter referred to as PSY3) was used for this study. The PSY3 system has been operational since 2005. It routinely assimilates SLA, satellite SST and in situ data. The model is Version 3. 1 of NEMO (Nucleus for European Modelling of the Ocean; Madec and the NEMO team, 2008) with a 0. 25 • tri-polar ORCA grid. The horizontal resolution is 27 km at the Equator and decreases to 6 km toward the poles. A total of 50 vertical levels are used and discretization decreases from 1 m resolution at the surface down to 450 m at the bottom, with 22 levels within the upper 100 m. The NEMO system uses the OPA (Océan PArallélisé) ocean model coupled with the LIM2 ice model  (Fichefet and Morales Maqueda, 1997).
The 2012 assimilated observation data sets are real-time along-track altimeter SLA data from SSALTO/DUACS (Segment Sol multi-missions dALTimetrie, d'orbitographie et de localisation précise/Data unification and Altimeter combination system;  Dibarboure et al. , 2011). Mean dynamic topography (MDT), used as a reference for SLA data assimilation, is based on the "CNES-CLS09" MDT derived from observations and described in  Rio et al. (2011). The assimilated SST observations are the NCDC/NOAA daily high-resolution SST analysis at 0. 25 • resolution  (Reynolds et al. , 2007). Real-time in situ temperature and salinity profiles from the Coriolis data center are also assimilated. A subsampling is done before assimilation to keep only one observation per platform per day, within a distance of 0. 1 •.
In situ observations The 2012 in situ data set is extracted from the real-time Coriolis database, where automated quality controls are applied. Coriolis is the in situ component of the French operational oceanography infrastructure. It provides real-time and qualified ocean in situ measurements to the European MyOcean project (Copernicus Marine Service) and to research and climate communities. Coriolis collects, controls and standardizes temperature and salinity profiles from different types of instruments including Argo floats, CTDs from research vessels, expendable bathythermographs (XBTs), moorings, sea mammals, gliders and drifting buoys. In term of data number, Argo is currently by far the most important source of information for in situ temperature and salinity profiles. Argo floats provide measurements of temperature and salinity from the surface to 2000 m every 10 days at the global scale.
The 2012 coverage of the in situ data set is shown in Fig. 1. Figure  1a  and b each correspond to 50 % of the Argo data sets. To ensure that all Argo profiles were selected, we sorted data from the instrument type variable, WMO_INST_TYPE, specified as "VERTICAL PROFILING: observation" for Argo floats in the Coriolis data set. The most realistic way of dividing up the Argo data set and of keeping coherent spatial and temporal resolution was therefore to sort it by platform numbers. Odd-numbered Argo platforms are shown in Fig. 1a , even ones in Fig. 1b. One of the most striking features of these two plots is the global and dense coverage of the oceans. The sparse distribution of the No Argo data set (green dots) is also remarkable. Some regions are rather more densely sampled by No Argo platforms than others.
Experiment design The OSEs presented here focus on the impact of the Argo observing system on temperature and salinity analysis and forecasts. Three experiments were performed from 18 January 2012 to 26 December 2012. This corresponds to 50 analyses with an assimilation cycle of 7 days. The three experiments assimilate SLA and SST data and differ only as regards the in situ assimilated data sets: -The experiment entitled Run-Ref assimilates SLA, SST and all in situ data (Argo + "non-Argo in situ data"). -The experiment entitled Run-Argo2 assimilates SLA, SST, 50 % of the Argo data and all the "other No Argo in situ data". -The experiment entitled Run-NoArgo assimilates SLA, SST and all "other No Argo in situ data". For the three experiments above, the strategy is to start from the same initial conditions of the PSY3 operational system that assimilates all the data and then withdraw part of the Argo data set for the OSEs.
Impact of Argo data assimilation on analyzed fields In the next two subsections, we discuss the differences in analyzed temperature and salinity fields from the surface down to a depth of 2000 m due to the Argo data set assimilation. In each subsection, we study a snapshot of the daily difference between OSEs with and without Argo data assimilation to illustrate the effect of Argo data assimilation. We then use the spatial RMS of the daily differences in the 0-300 and 700-2000 m layers to provide quantitative information on the realism of the analyzed T and S fields compared to in situ observations. Heat and salt content in the 0-2000, 0-300 and 700-2000 m layers are also analyzed. This shows the sensitivity of the analyzed temperature and salinity fields to Argo data assimilation. We then verify that the in situ observation-analysis differences were efficiently reduced by the assimilation process.
5 • C. The influence of Argo observations at 1000 m is striking in the North Atlantic area of the outflow from the Mediterranean: differences there can be larger than 0. 3 • C. As a consequence, Argo data assimilation is crucial to correct the water mass properties in the ocean interior of the model forecast. Figure  6  shows the RMS of the temperature differences between Run-Ref and Run-NoArgo. It is calculated from the daily differences of the last 6 months of the experiment. It quantifies the spatial distribution of the impact of Argo assimilation on the PSY3 data assimilation system. We chose to focus on the 0-300 m layer, where SST and SLA assimilation also plays a major role, in order to evaluate the importance of Argo temperature measurements in the upper ocean, and on the 700-2000 m layer to assess the impact of Argo profiles in this specific layer, where Argo is almost the only in situ observing system available.
Temperature innovations Figure  13  shows the global average RMS of the temperature innovation from 0 to 2000 m for the last 6 months of the Run-Ref, Run-Argo2, Run-NoArgo and Free Run experiments. For each experiment, the RMS is greatest at approximately 100 m and decreases with depth. The amplitude of the RMS temperature innovations below 1000 m is very low compared to the mixed layer depth values, but global variability at that depth is obviously also very low. The RMS of temperature innovation decreases with increasing quantity of Argo data assimilated. Atmospheric forcing and assimilation of SST and SLA may explain the good surface results for Run-Ref, Run-Argo2 and Run-NoArgo. Figure  14a  shows the temporal mean RMS temperature innovation profile of the previous 6-month time series from the surface down to 2000 m.
This is a standard procedure for characterizing the performance of a forecasting system and evaluating the impact of an observing system  (Oke and Schiller, 2007; Vidard et al. , 2007; Fujii et al. , 2014; Lea et al. , 2014; Guinehut et al. , 2012). The maximum RMS is found at 100 m for the four experiments, but the RMS of the innovations ranges from 1. 4 • C for Free Run to 0. 9 • C for Run-Ref. The RMS of temperature innovations is improved in the whole water column when Argo temperature profiles are assimilated. However, at depths greater than 1400 m, the RMS for temperature innovation of the Free Run experiment is lower than the RMS for temperature innovation of the Run-NoArgo. This result shows the importance of Argo T and S profile assimilation for ensuring a good projection at depth of the SLA and SST innovation by the multivariate data assimilation system.