dataesr/scientific-paragraphs-categorization huggingface dataset:
Differences were considered statistically significant when p < 0. 05. experiments, and contributed to the manuscript preparation. Rafael Miñana-Prieto performed the experiments. Sylvia Dyballa designed the ZeCardio software, contributed to its development, and contributed to writing the manuscript. Els van Doornmalen contributed to the experimental design and analysis of the results. Helma Rutjes provided reagents, and contributed to the project design and data analysis. Thierry Savy developed the ZeCardio software. Davide D'Amico and Javier Terriente conceived of the project, coordinated the work, and contributed to the manuscript preparation.
Int. J. Mol. Sci. 2017, 18, 864 13 of 20 mold to allow their appropriate orientation under the fluorescence stereo microscope (Olympus MVX10). Individual fluorescent hearts were recorded during 60 s each (Figure6A). Videos were acquired with a high-speed recording camera (Hamamatsu C11440 ORCA-flash 2. 8) and analysed with the ZeCardio ® β software to extract different cardiac parameters-heart rate, cardiac arrest, QTc prolongation, and Ejection Fraction (EJF) (Figure6B).
Acknowledgments: The  ZeGlobalTox project received financial support as an innovative CRACK IT Solution-Agreement  of 1 June 2015-by the  National Centre for the Replacement, Refinement and Reduction of Animals in Research  ( NC3Rs ), legally represented by the  Medical Research Council (MRC) ,  United Kingdom. The  Spanish Ministry of Economy and Competitiveness  financially supports  Simone Calzolari  and  Sylvia Dyballa , through a  Torres Quevedo Fellowship (Simone Calzolari :  PTQ-14-07351 ;  Sylvia Dyballa :  PTQ-15-07995 ) and  Carles Cornet  with an  Industrial Doctorate Fellowship  ( DI-14-06969 ). Noldus Information Technology, Wageningen ,  The Netherlands , has contributed by providing the DanioVision TM for the behavioural analysis.
TreeArchitecture is written in Matlab. The second algorithm, known as "PlantScan3D"  (Boudon et al. , 2014) , has been developed in the INRIA laboratory. It was created in a context of biology and agronomy for generating quantitative models of plant development and for comparing different methods of tree reconstruction implemented inside. For our test we have chosen the skeletonization method of  Xu et al. (2007)  which seems to provide realistic results and which differs from our method. This method consists in connecting the points of the point cloud to form a graph. Then, by a computation of distance, the algorithm produces a skeleton ordered as a "Branch Structure Graph" (BSG). This BSG format allows us to keep the topology of the branch structure. Afterwards, among the different methods proposed in the tool for reconstructing the volume of branches, we have chosen that which computes the mean diameter of each part of the skeleton.
This method seems to provide better results than the others that were implemented. The open source version of PlantScan3D used in this paper is the 6. 0 (June the 17 th , 2016) and it is written in C++ and Python. The third algorithm tested in this work is "SimpleTree", developed by  Hackenberg et al. (2015). It was created to build quantitative structure models (QSM) of trees for forestry applications. This type of model allows the quantification of the above ground biomass in a hierarchical order. The volumetric model, created by SimpleTree, involves a fitting of cylinders by using the idea of cutting sphere surfaces. These spheres allow the extraction of the skeleton nodes and the thickness of the point cloud cut by each sphere. Thus, it will be interesting to compare this method of reconstruction to the others which are totally different.
For the pruned silver lime tree with shoots, the reference model was created by manual digitalization of the shoots on the point cloud. Reference skeletons of the other trees were obtained by using the PlantScan3D module, allowing manual modifications of a first raw skeleton automatically created. These processes were fastidious but they allow to produce reference models which are required to assess the accuracy of the created models. The DBH estimation is compared to manual field measurements on trees at 1. 3 meter height. From the skeleton analysis of each model (Table  6 ), we can affirm that the algorithm which reconstructs most of the branches is PlantScan3D (M2b). However, a solution to remove the duplicate errors (explained in subsection 4. 2) that this algorithm creates (M2a) must be found.
ACKNOWLEDGEMENTS The authors would like to thank  Marine Lempereur , INSA master student, for performing the reconstruction of the trees with the three algorithms and also  Frédéric Boudon , INRIA member, and  Jan Hackenberg , INRA member, for providing help, respectively for the use of PlantScan3D and SimpleTree. The authors would also like to thank the  Eurometropolis of Strasbourg  for providing them access to 2D and 3D data over Strasbourg.
It presents a new approach based on past experiences and couples simulation with learning to automatic acquisition and updating of a CTS base. The coupling between simulation and learning (AI technique) is a promising solution where simulation is used as a technique for generating empirical knowledge for learning. On the one hand we propose a Representation of Observations in form of CTS Algorithm (ROCTSA) which describes the possible evolutions of the system to diagnose as a set of CTS. The algorithm allows to model normal behavior of the system as normal CTS and faulty behavior as abnormal CTS. This set constitutes the learning base. On the other hand, an incremental learning module is introduced to learn new CTS and to update the CTS base based on past experiences. The remainder of this paper is organized as follows.
 Model based approaches: Are problem solving techniques based on models representing either the system to diagnose or the faults that may exist in the system. For example,  (Guerraz and Dousson, 2004 ) developed a petri nets based method for the generation of chronicles necessary for diagnosis from the fault model of the system to diagnose. The proposal does not require knowledge of the global behavior of the system. Another solution is described in  (Saddem and Philippot, 2014)  to translate a timed Atomaton model of a diagnoser into CTS. The method ensures the completeness of the CTS data base but it is done manually.  Data based approaches: They rely on historical data by extracting significant features using temporal data mining techniques. One of the first examples is suggested in  (Dousson and Duong 1999) ,  (Cordier and Dousson, 2000).
Labeling of CTS The operation of the model in a normal mode and abnormal mode (triggering failures) allows the labeling of each instance of CTS automatically without the need for an expert accompanying the data formatting process and it does not need to give advice (normal or faulty behavior). The normal functioning of the model is represented as a set of normal CTS, while the faulty one is defined as a set of abnormal CTS. Both types of CTS are stored in a CTS Base. This base is the learning base.
Learning Module For new CTS learning, we rely on the learning data stored in the CTS base obtained during the simulation module. Each new observation is transformed into a new CTS (nCTS) through ROCTSA. The principle of this module consists in extracting, for each nCTS, the similar or nearest CTS from the CTS Base. The research is based on the use of a similarity metrics that calculates the degree of similarity between the new CTS and the past CTS. For this reason, we use a similarity calculator that has as input a nCTS and the CTS base. Its outputs are the similarity values between the nCTS and all the past CTS (pCTS) stored in the base. Then, the new CTS will inherit the result of the CTS having the greatest similarity and will be stored in CTS Base.
EXPERIMENTATION To validate our proposal, we exploit the Interactive Training System for PLC (ITS PLC) proposed by the Portuguese company Real Games (www. realgames. pt). ITS PLC is an education and training tool dedicated to programming the PLC and validating the control algorithm through a real time interactive experience  (Riera et al. , 2010). It offers 3D simulations of Operative Parts (OP) of 5 industrial systems (sorting, batching, palletizer, pick and place and automatic warehouse). Each system a graphical simulation of an operative part including its sensors and its actuators and allowing a PLC to control it. We use the beta version of ITS PLC in this study which allows: (a) using scripts in IronPyton (http://ironpython. net) to write its own controllers in a language close to the ST (Structured Text).
(b) accessing to an Interactive IronPython Interpreter allowing the user to interact with each simulated system by accessing for example to its inputs / outputs through the IO object. IO. Actuators and IO. Sensors respectively return the actuators and sensors signals.  The first one allows controlling the sorting system without the need for a real API  The second one allows access to the inputs / outputs of the simulated system each the 16ms (ITSPLC cycle duration), to implement the ROCTSA (simulation module) and the similarity calculator (learning module). Note: we chose the duration of a temporal constraint of period type (d) is 5ms. During the simulation module, the set of normal and abnormal CTS are recorded in the CTS base to form the learning base. Figure  5   ↓c5, ↑c4, [2816, 2821] ) -> N This signature describes the passage through the different states of path A (introduced above).
The learning module uses these past experiences to add new CTS to the CTS base (to promote learning). Example: We propose to add an nCTS and search the most similar using our similarity calculator. ROCTSA starts generating an nCTS: nCTS: (↑c4, ↑S4,  [65664, 65669] )* (↑S4, ↓c4,  [80, 85] )-> ? It does not exist in the CTS base. Consequently, the similarity calculator can be launched. The nCTS inherits the (normal or faulty) behavior of the CTS which has the minimum distance and will be stored in the CTS Base. In this example, CTS 6 has the minimum distance (D=0. 166). Therefore, the nCTS inherits the normal behavior of CTS 6 and is stored in the CTS Base.
CONCLUSIONS In the context of diagnoses, we suggest a new approach based on past experiences which couples a simulation with learning for automatic acquisition and update of a set of CTS. We present ROCTSA algorithm allowing to model the normal behavior of the system to diagnose as a set of normal CTS and the faulty behavior as a set of abnormal CTS. A learning module is introduced to learn new CTS and to update the CTS base. The proposed approach has many advantages: (i) An easy update for the CTS Base. Indeed, when a new behavior occurs in the APS, a new CTS will be added to the CTS base that models this new behavior. (ii) It is a generic approach that can be applied to any APS. (iii) It does not require the presence of an expert who might be reluctant to acquire a CTS base. As a prospect, to improve the expressiveness of ROCRSA, we will express the absence of events (negation operators).
A large number of these N-termini were associated with various welldefined maturation processes occurring on organelle-targeted proteins (mitochondria, chloroplast and peroxisome), secreted proteins or membrane-targeted proteins. It was also possible to highlight some protein alternative starts, splicing variants or erroneous protein sequence predictions. Conclusions: The EnCOUNTer tool provides a unique way to extract accurately the most relevant mature proteins N-terminal peptides from large scale experimental datasets. Such data processing allows the identification of the exact N-terminus position and the associated acetylation yield.
In addition, the determination of NTA yield is a difficult task with the tools currently available. As an exemple, Mascot Distiller (MD) allows NTA quantitation using peptides N-terminally labeled with d 3 -(heavy, H) or d 0 -(light, L) acetyl  [8]. Although this tool was used to define Lys ε-acetylation yield  [9] , it is originally dedicated to provide protein differential quantitation. The determination of the NTA yield for each proteoform (especially for the mitochondrial and the plastidic mature proteins) is not easily available and requires some additional processing  [10]. Therefore, the development of a new tool designed to perform the extraction of the data computed by Mascot and Mascot-Distiller is required. The combination of the outputs must provide a list of the mature N-termini and the associated accurate NTA yields.
Although some alternative tools could be able to perform H/L ratio quantitation such as MaxQuant, the EnCOUNTer script is not able, presently, to handle other input file format than the Mascot and Mascot-Distiller ones. The EnCOUNTer tool (Extraction and Calculation Of Unbiased N-Termini) uses a stepwise approach. First, the characterized peptides are scored to discriminate between protein N-termini at position 1-2 and downstream N-termini (DNT) of the protein sequence. This determination is based on a curated experimental dataset. Second, EnCOUNTer recalculates the average NTA yield taking into account the first residue of the characterised mature proteins. Finally, it provides an exhaustive list of the processed N-termini with the recalculated unbiased NTA yield. The EnCOUNTer tools was trained using a manually validated dataset (Additional file 1: Table  S1 ).
As a proof of concept, the optimized parameters were used against a complex Arabidopsis thaliana experimental dataset obtained after an enrichment of the mature protein N-termini using the SILProNAQ approach  [2]. Such experimental data set provides 584 DNT peptides (related to 383 distinct proteins) of which 338 were quantified for NTA yield. Some of these Ntermini (112 hits), were experimentally validated and their positions well correlated with known cleavage sites of signal peptides, mTPs or cTPs (based on UniProtKB/ Swiss-Prot annotations). Some others (224 hits) were in accordance with transit peptide cleavage site predictions within a range of ± 2 residues. In addition, 648 protein N-termini were characterized at positions 1 or 2 (on the initiator methionine or after its excision) of which 303 were also quantified for protein NTA yield (Additional file 2: Table  S2 ).
Sample preparation and raw data aquisition Proteins extracted from A. thaliana Col. 0 seedling were used to perform N-terminus enrichment using SCX chromatography. Rapidly, 1 mg of protein was denatured and reduced followed by cysteine alkylation with iodoacetamide. After cold acetone precipitation, proteins were resuspended in 50 mM NH 4 HCO 3 and digested by 1/ 100 (w/w) of TPCK treated porcine trypsin (Sigma-Aldrich) for 1. 5 h at 37 °C, twice. Peptides were desalted with Sep-Pak columns and the retained material was eluted with 80% acetonitrile (ACN), 0. 1% TFA and then evaporated to dryness. The collected material was resuspended in Strong Cation eXchange (SCX) LC buffer (5 mM KH 2 PO 4 , 30% ACN and 0. 05% formic acid) and injected into an Alliance HPLC system using a fluorimeter detector (Waters) equipped with polysulfoethyl A column (200 × 2.
1 mm, 5 μm 200 Å; PolyLC, Colombia, MD). Peptides were eluted with a KCl gradient (SCX-LC buffer B: 350 mM KCl in SCX-LC buffer A; 0-5 min, 0% B; 15-40 min, 5-26% B; 40-45 min, 26-35% B). Fractions were collected every 2 min for 40 min and the solvent was evaporated to dryness before storage at -20 °C. Fractions eluted from SCX columns with retention times of 3 to 22 min were analyzed as previously described 1  with an Easy Nano-LC II (Thermo Scientific) coupled to a LTQ-Orbitrap™ Velos (Thermo Scientific). Finally, data processing usually combines a few acquisition files, i. e. 10 files related to each individual SCX fraction (1 h analysis) and 6 files related to combined fractions (for more details, see  [2] ). Furthermore, acquisition files obtained from SCX fraction 5 and 6 were used as training dataset and testing dataset, respectively.
Mascot Distiller/Mascot data processing and *. xml exports Regardless of the number of Orbitrap-MS acquired files, Mascot Distiller (Ver. 2. 5. 1, Matrix Science, London, UK) combines all acquired files together for a unique processing event (Fig. 1 ). The EnCOUNTer script was first used to extract data from the raw files followed by protein identification using the Mascot protein identification tool (Ver 2. 4, Matrix Science, London, UK). Precursor detection was obtained after peak regridding of 100 points per Da for a peak half width of 0. 02 Da. Precursor ion charge was determined directly from the Orbitrap survey scan and limited at 1 to 5. The "time domain aggregated MS/MS spectra" was regridded at 20 points per Da for a peak half width of 0. 2 Da and MS/MS spectra containing more than 10 peaks were retained.
MS/MS spectra of similar precursor masses were not combined at any time and considered separately for peptide identification. Additional filtering parameters such as correlation threshold and minimal signal over noise ratio were defined at 0. 7 and 1, respectively for the MS and MS/MS in the relevant mass range (400-20000 Da and 50-10000 Da, respectively) with a maximum peak iteration of 500. Alternative high resolution mass spectrometers could be used but the MD parameters applied for raw data extraction should be optimized accordingly. MD extracted data were submitted to Mascot 2. 4 software for protein identification and post-translational modification characterization. The database used was "The Arabidopsis Information Resource" (TAIR ver. 10; www. arabidopsis. org [11] ). The parent and fragment mass tolerance were 5 ppm and 0.
Irrelevant and false positive peptide hits generated at this step were filtered out at the final stage of the EnCOUNTer process. EnCOUNTer also required protein identification data generated by Mascot. These data were automatically exported in xml files with the same MD parameters for the P-value and the Mascot score threshold. Additionally, the "MudPIT Scoring" and "Bold Red peptides" option were selected. These exported files contain all "Protein Hit Information" except pI and Taxonomy ID and all "Peptide Match Information" except the frame number and the unassigned queries.
EnCOUNTer processing Basically, the EnCOUNTer tool requires the MD exported file, the associated Mascot results and a parameter file. Although the tool could be used with the default parameters provided (Additional file 4), an optimization of the scoring parameters using a relevant training dataset has been performed. During the scoring Fig. 1  Overview of the EnCOUNTer processing scheme: from sample to mature N-terminus position and NTA yield parameter optimization, the EnCOUNTer tool required an additional files containing the list of "curated Ntermini" (True / False N-termini; Additional file 1: Table  S1 ). At the end of the optimization, a file containing all optimized values was generated (*. json). This file could be applied on other experimental datasets (from a similar origin) without the optimization of the scoring scheme.
Parsing function The EnCOUNTer tool parses the pre-processed data exported from MD and Mascot identification tool. Mascot matched queries (only Mascot first-ranked peptide sequences) and associated protein AC were extracted from the MD xml file. Each of these entries were enriched with information, e. g. peptide sequence, starting position, MD processing results such as H/L ratio and signal quality coefficients. Then, the collected results were complemented with data extracted from the Mascot exported files such as the peptide identification score, identification E value… Of note, some peptides were not proteotypic  [12]  and shared with few distinct proteins or, alternatively, to different translational isoforms of the same protein (especially for TAIR database). The redundancy is noted and these data could be easily removed at will.
N-terminus scoring function The EnCOUNTer tool should discriminate internal peptides from the mature protein N-termini. Biological details associated to nuclear encoded mitochondrial/ plastidic proteins TP such as sequence composition and average length  [13] [14] [15] [16]  (also observed from experimental dataset  [3, 17, 18] ), highlighted some features useful to define relevant scoring coefficients (Additional file 5: Figure  S1  and Additional file 6: Figure  S2 ). To this end, we defined a scoring function based on six distinct coefficients related to i) peptide "starting position", ii) residues around the "starting position", iii) characterized N-terminal modifications, iv) alternative start positions at the vicinity of the "starting position", v) matched peptide redundancies and finally iv) the "Localization" score.
Some of these features could be optimized from the training dataset (such as "starting position" or the "residues around the starting position") whereas some other should be defined by the users to valorize/penalize experimental observations (such as "data redundancy" or "multiple transit peptide cleavage sites". Peptide "starting position" score (Bound Score) Based on the experimental training dataset, EnCOUNTer determines the optimal range (OptiMin and OptiMax) where "true" N-termini are the most frequently distributed. The Matthews Correlation Coefficient (MCC) was determined for all possible combinations of positions between the two endpoints of the N-terminal distribution range of the "True" hits for the DNT candidates (defined as ExpMin and ExpMax). The optimum range defined with the higher MCC provides the optimum endpoints (OptiMin and Opti-Max).
This positional range is associated with a scoring weight of 2 to favor the characterization of these N-termini. This calculation was associated to a "K fold cross validation" (using 10 randomized fractions) to determine the robustness of the prediction and the results of the investigation were exported in the *. bound file (specifically for the "bound" K fold test) and *. json (all optimized values). Nevertheless, some relevant candidates (Experimental "True" N-termini) were still present outside of these optimal values, i. e. in between ExpMin/OptiMin and OptiMax/ExpMax. Since the experimental dataset may be slightly different compared to the training dataset (considering the ExpMin and ExpMax values extracted from the training dataset), the ExpMin and ExpMax values were pondered by the standard deviation observed during the "K fold cross correlation" as an estimation of the dataset variability (defined as Min and Max respectively).
Both ranges, i. e. Min/ OptiMin and OptiMax/Max, were associated with a scoring weight of 1 (neutral effect on the result) that prevented their elimination at this stage. All others positions are associated with a scoring weight below 1 (e. g. 0. 1) to penalize such less biologically relevant positions. Starting positions 1-2 were subjected to a special scoring detailed below. Residues around the starting position ("Spec" Score) Based on the training dataset, EnCOUNTer is able to provide a scoring matrix associated to the amino acid presents around the experimentally characterized starting position. For each position located between P n to P - n , a binary classification was performed for each of the 21 possible amino acids. Such investigation provides a distribution of True Positive (True N-termini candidate has the defined residue at P i ), True Negative (False Ntermini candidate has not the defined residue at P i ), False Positive (True N-termini candidate has not the defined residue at P i ) and False Negative (False N-termini candidate has the defined residue at P i ).