dataesr/scientific-paragraphs-categorization huggingface dataset:
The importation links between the various modules of OntoPaRON are illustrated in Fig  2 , and the metrics for each module of the ontology are shown in Table  2. OntoPaRON is available from https://bioportal. bioontology. org/ontologies/ONTOPARON. The ontology respects a certain number of metadata according to the prescriptions of  [29]. To see the classification after reasoning, download the ontology from Bioportal, load it in Protegeand start reasoning with Hermit. The final reclassification is visible in the "class hierarchy (inferred)" view.
Construction of fully defined concepts In this case of use, we wished to define the elements of the care trajectory of the patient and to determine whether these elements were expressed in a similar manner for all patients. We hypothesized that semantic annotation of the event database of the ALS-IDF network with our annotation tool, OnBaSAM, using the OntoPaRON ontology as a reference, would help us to understand the care trajectories of patients. We wanted to know whether certain themes, such as 'exhaustion', 'obtaining human assistance', or 'seeking help to find a healthcare professional', were frequent in patient care trajectories or whether they concerned all patients. Exhaustion during patient management may be expressed in several different ways: the spouse may explicitly report being exhausted, the use of respite care (requested by the patients or their families, or proposed by a healthcare professional), or the exhaustion of teams caring for the patient at home.
We created fully defined concepts to bring together all the concepts relating to the same theme. The organization of knowledge into an ontology made it possible to construct fully defined concepts with sufficient and necessary conditions. Fully defined concepts make it possible to group together all concepts linked to the same theme by the same object property, in the same class. These concepts may be found at different levels in the ontology or in different ontology modules. The HermiT reasoner infers the membership of all concepts sharing a relationship to a fully defined concept defined with this same relationship. The fully defined high-level concepts found in the core module are illustrated in Fig 3. The 'domain of exhaustion' thus includes the exhaustion of a carer from the family or of a professional carer during patient  management.
The concepts relating to this theme are found in two ontology modules: the concepts 'request for respite care' and 'respite care proposal' are found in the coordination module, whereas the concept 'need for respite care' and 'patient carer exhaustion' are present in the socio-environmental module. These three concepts have the object property aPourThématique ThématiqueEpuisement and are therefore inferred under the fully defined concept of 'Exhaustion Domain'. Fully defined concepts are constructed and created as variables of interest for the clinical analysis of patient care trajectory, and their frequency is an indicator of the problems encountered by patients. After annotation of the corpus by the OnBaSAM system, all the related quantitative data linked to the occurrence of exhaustion in caring for a patient can be extracted.
Level of alignment with other terminological and ontological resources Our studies of existing terminological/ontological resources (TORs) identified none including the dimensions of the patient care trajectory. This finding justifies our ontological approach. Once the complete ontology is obtained, it can be interesting to revalidate the approach a posteriori and to check whether any known terminological/oncological resources cover the domain modeled. We therefore assessed the level of coverage of each module of the Onto-PaRON ontology by the ontologies present in HeTop (https://www. hetop. eu/hetop/), using the terms in French. The HeTop terminology server provides access to 85 TORs. Automatic unsupervised alignment identified found 9,906 alignments for 51 TORs. The results are summarized in Table  3.
Annotation and assessment tools: The Ontology-Based Semantic Annotation Module and Pronto annotator Within the Medical Informatics and eHealth Knowledge Engineering Laboratory (LIMICS), we decided to develop a semantic annotator using OntoPaRON ontology as a semantic reference. We created this annotator OnBaSAM, with resources available from the GATE, a text analysis platform providing open source resources. These resources are used in various biomedical research projects  [33]. Using the available resources, we constructed annotation chains for textual documents. Based on GATE which allows to build a semantic annotation string using an ontology. We have built a specific processing chain that we have adapted to our use case. Indeed, GATE is used and developed in English. Based on GATE, we have built a specific processing chain for our problem, which allows us to take into account the French language, the negation, as well as the export of annotations.
The detection of missing concepts by the experts allowed us to enrich the ontology. We used three widely used standard measurements  [34] , to evaluate annotation performance and the relevance of the concepts: precision, recall, and the F-measure. Five experts evaluated 410 events. The resulting scores yielded a precision of 0. 91, a recall of 0. 9, and an F-Measure of 0. 91. The results presented may be moderate, given certain biases in the evaluation  [34]. Indeed, the presence of annotations on the corpus influences the annotation of the evaluators who will focus on the present annotations and not on the non-annotated data. Based on an analysis of these results, we modified OntoPaRON and used the new version to annotate a corpus of 928 patient files. We used JMP software for statistical data processing.
Results We annotated 928 patient files from the ALS-IDF network between January 2, 2013 and December 31, 2017. These years were chosen on the basis of the start and end dates of this project (2014-May 2019). The annotation of these 928 files represented 31,260 events, or more than 1,000 000 words. The sociodemographic data for the patients are summarized in Table  4. The population consisted of 52% men and 48% women (sex ratio of 1. 09). The patients had a spinal form of the disease in 65% of cases and a bulbar form in 34%. The bulbar form was more frequent in women and the spinal form was more frequent in men  [35]. Mean age at diagnosis was 65. 2 years, and mean age at inclusion in the ALS-IDF network was 66 years. The mean duration of support from the ALS-IDF network was 550 days. Most of the patients lived in families (78%) and were married (64%) and all lived in the Ile-de-France region.
We validated this hypothesis by exploring this issue at a finer level of granularity of the OntoPaRON ontology through the extraction of annotations for the concept of 'Request to find a healthcare professional'. This concept is defined as the explicit formulation of a request for coordinators to find a healthcare professional (e. g. physiotherapist, speech therapist, GP). We investigated whether the occurrence of this request was related to the resulting coordinating action, 'Search for a healthcare professional' (r = 0. 28; p < 0. 001). We transformed the quantitative data into dichotomous discrete data (0 = no demand or not seeking of a healthcare professional; 1 = request made or looking for a professional) for this analysis, to determine the total number of patients concerned by these two concepts.
A bottom-up approach could also be envisaged, starting from real-life patient data. We chose to follow this second approach, using textual data from a coordination network for people living with ALS in the Ile-de-France region. We created a modular ontology for the processing of such data, taking all aspects of the patient care pathway into account: the medical, socio-environmental, and coordination dimensions. The choice of a modular system and the creation of defined concepts made it possible to group together concepts dealing with the same theme from different ontology modules under a defined concept. The themes for the defined concepts were chosen on the basis of published data for ALS. Like Grau  [23] , we observed the positive aspects of modularity. However, modularity requires constant attention to the positioning of defined concepts and the management and attribution of relationships between concepts.
An absence or interruption of care can have a major impact on the care pathway of the patient, who may require hospitalization due to a deterioration of their medical condition or the exhaustion of their carers. We found that alignment was better in the medical domain than, for example, in the socioenvironmental domain. The granularity of our ontology, related to our original objective of annotating specific textual data and the specificities of the French social and medical sector, explain why these classes were not found in many ontologies, and fully justifies the creation of the various modules of the OntoPaRON ontology. Our study had several limitations. First, only a limited number of patients. The ALS-IDF network provides support for a large proportion of patients with ALS in Ile-de-France, but these data may not be generalizable to all patients with ALS in France, as the monitoring and care services available differ between the regions of France.
Our results cover all the patients included, revealing specific features for some. A more detailed cluster analysis would make it possible to observe the trajectories and determine whether certain sequences (of events or difficulties) are identifiable. We have also used OntoPaRON to annotate coordination corpora for Parkinson's disease. The initial results showed that two of the modules (coordination and socio-environmental) were suitable for report annotation, but the corpus volume was too small validate the results. In addition, we have started tests to use our approach in psychiatry. The first results show that the semantic annotator works in another domain but this remains to be further investigated. As described in section "Modularity of OntoPaRON", we organize our ontologies with a core ontology built for a long time in our laboratory, TopMenelas.
It allows us (i) to have a foundational ontology to subsumerate, and (ii) to ensure consistency between the different ontologies developed. But we are aware that there are, among others, two federative initiatives, DOLCE with the core BTL2 ontology (http://biotopontology. github. io) and BFO with the core OGMS ontology (https://bioportal. bioontology. org/ontologies/OGMS). We have begun a work of alignment of TopMenelas with these two ontologies, in order to take advantage of the eco-system of these two initiatives. In conclusion, we present one approach, based on the reasoning capacity and possible inferences in ontologies, for creating defined concepts with not only a semantic aspect but also a real dimension and clinical expression. The modularization of ontologies and their association with the automatic NLP tools, as developed here, will make it possible to annotate french corpora and to extract knowledge.
Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map, the Quantization Error (SOM-QE). Shape pairs with perfect geometric mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, longer choice RT for 'yes' responses relative to symmetry.
Symmetry may be exploited in pattern detection and classification by neural networks, which may have to learn multiple copies of one and the same object representation displayed in different orientations. Encoding symmetry as a shape prior in the network can, therefore, help avoid redundant computation where the network has to learn to detect the same pattern or shape in multiple orientations  [31]. Symmetry-based feature extraction and/or representation  [32]  by neural networks using deep learning can, for example, help discover the most informative representations in large image databases with only a minor amount of preprocessing  [33]. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns, the problem of uncertainty still requires additional attention, especially in ambiguous data.
Experimental display The images were displayed for visual presentation on a high resolution computer screen (EIZO COLOR EDGE CG 275W, 2560x1440 pixel resolution) connected to a DELL computer equipped with a high performance graphics card (NVIDIA). Color and luminance calibration of the RGB channels of the monitor was performed using the appropriate Color Navigator self-calibration software, which is delivered with the screen and runs under Windows. As stated here above, visual symmetry uncertainty in the shape pairs was varied by giving the local shape elements variable color appearance in terms of hue, lightness and saturation. We have four variations for each of the single-color shape pair configurations, labeled as 'RED' and 'BLUE', and two variations for the multicolor shape pair configurations, labeled as 'MULTICOL'.
The task communicated to each participant was to: "decide as quickly as possible and as soon as an image appears on the screen whether or not the two patterns in the given image appear to be symmetrical or not". They had to press '1' for 'yes', or '2' for 'no' on the computer keyboard, their index and middle fingers of their dominant hand ready above the numbers to be able to press a given key without any motor response delay. Each coded response choice was recorded and stored next to the choice response time in a labeled data column of an MS Excel file. The choice response time corresponds to the time between an image onset and the moment a response key is pressed. As soon as a response key was pressed, a current image disappeared from the screen. 900 milliseconds later, the next image was displayed.
Results With 20 images per individual session, two successive sessions per participant, and 15 participants, a total of 600 choice response time data were recorded. A shape pair corresponding to a single factor level relative to shape appearance and color was presented twice in a session with 20 images to allow for left and right hand side presentation of a given appearance factor level in the shape pairs. The labels of the individual factor level associated with each shape pair are given in Figure  1. With two repeated sessions per participants, we have four individual response time data for each single factor level. All data analyses relative to choice response times were run on the 15 average response times for each factor level from the 15 participants. Since all shape pairs in all the images were mirror-symmetric, 'no' responses occurred only very rarely in the experiment (17 of the 600 recorded choice responses signaled 'no', which corresponds to less than three percent of the total number of observations), as would be expected.
The fully conscious detection of symmetry in choice response tasks most likely involves information processing through brain networks with neurons that display large receptive field areas and a massive amount of lateral connectivity  [75]. Other machine learning approaches have exploited low-level and global features of natural images or scences to train artificial symmetry detectors on the basis of symmetry-axis ground-truth datasets  [76, 77] , demonstrating the importance of local features such as color in boosting symmetry detection learning by machines compared with gray-scale image data. In human perception, however, the attentional weight of visual information enabling symmetry detection depends not only on the contrast of local features to their local surroundings (saliency), but also on the importance of the features with respect to the given task (relevance).
In auditory behavioral and EEG experiments, the variability of stimulation solutions, for both software and hardware, adds unnecessary technical constraints. Currently, there is no easy to use, inexpensive, and shareable solution that could improve collaborations and data comparisons across different sites and contexts. This article outlines a system composed by a Raspberry Pi coupled with Python programming and associated with a HifiBerry sound card. We compare its sound performances with those of a wide variety of materials and configurations. This solution achieves the high timing accuracy and sound quality important in auditory cognition experiments, while being simple to use and open source. The present system shows high performances and results along with excellent feedback from users.
neurobs. com/), OpenSesame  (Mathôt et al. , 2012) , Eprime  (Taylor and Marsh, 2017) , Psychopy  (Peirce, 2007) , and Psychtoolbox  (Borgo et al. , 2012) , the latter in MATLAB. All of the software allows for use of experimental control and management, resulting in good time performances for experiments. They vary both in terms of specialization of specific functionality and research area. However, all of the software is still dependent on computer hardware, drivers, and operating systems (OS), resulting in variability of configuration settings and installations  (Plant et al. , 2004; Garaizar et al. , 2014). In addition, the trigger information from the stimulation computer (or other devices, such as response boxes) to the acquisition computer (e. g. , in EEG recording) is generally sent through the parallel port (PP; also named DB25 or LPT [line printing terminal] port, from the "D-Sub" connector family), which is reliable, easy to use, and has excellent timing accuracy  (Stewart, 2006; Voss et al.
, 2020). Others have shown that an Arduino could be easily used for steady-state visual evoked potential  (Mouli et al. , 2015). However, these systems have been developed for very simple protocols and materials (beeps) and could be improved both in timing accuracy and audio quality. The need to develop a more generalized protocol player, a highly sound and timing efficient, shareable, and transportable system is part of a general move in research toward more open science. More and more possibilities are being created for sharing of data, analysis [e. g. , Open Science Framework (https://osf. io)], and protocols such as the OpenBehavior Project  (White et al. , 2019). Here we show that a Raspberry Pi 3, together with a HiFiBerry (additional sound card to Raspberry Pi), allows for high stereo sound quality and good timing performances (in terms of latency and jitter) while being powerful, easy to use, inexpensive, and in an open source ecosystem (software and hardware).
We developed a Python library allowing the presentation of any auditory material for behavioral or electrophysiological studies, from simple to sophisticated paradigms. We also provided specifics for a state-of-the-art stimulation box for auditory experimental protocols. This box includes a parallel port for EEG synchronization of triggers, a Raspberry Pi 3, HiFiBerry, and battery. This portable system box can be used in fundamental and translational research settings. It can significantly reduce variation between studies and/or centers as well as simplify the setup of this kind of protocol. It has been developed in a multisite collaboration involving laboratory and clinical environments, thanks to the CogniComa project, and the box is actually used with four protocols in Lyon and Toulouse (e.
Playframe The playframe is the input of the reading process and consists of a simple table with the following three columns: the name of the. wav file to be played, the associated trigger value, and the associated timing (Fig. 1 ). The latter consists of time in milliseconds from the end of the current sound to the beginning of the next sound [i. e. , the interstimulus interval (ISI)]. This table should be named "Playframe," saved as a ". csv" or ". xls" file, and constitutes a protocol playlist in a pandas (https://pandas. pydata. org/) dataframe style. It is protocol specific and generated independently of the player. An example of playframe generation with pandas is available in https://github. com/ PyOpenProto/PyOpenProto/tree/e-neuro2021/examples/ playframe_generation_example. The playframe could then be set on an external USB key, with relative audio stimuli and plugged in to the stimulation box to play the corresponding protocol.
Software The dissociation of protocol content and playing process allows the player implementation to focus on general core accuracy in timing and sound quality while bypassing specific ordering complexity (Fig. 1 ). The use of such external session list imports is generally provided in most presentation software. The Python programming language for the playing process was the obvious coding solution. It allows for interoperability (coupled with a wide range of available powerful libraries), readability, efficiency, and is associated with a large collaborative community  (Muller et al. , 2015). The Python in neurosciences ecosystem provides an excellent unifying solution addressing the needs of both scientists and engineers, from data acquisition to treatment and analysis. We designed a Python library named PyAudioProtocol, which is a part of the more general project 'PyOpenProto' available on GitHub (https://github.
com/PyOpenProto/ PyOpenProto/tree/e-neuro2021) PyAudioProtocol relates to pure audio stimuli presentation coupled with parallel port triggering, subject to strong constraints in sound quality and timing accuracy. This library hosts two versions for auditory stimulus presentation. One version is intended for classical computers ('core_gui. py' file), which expects the presence of a parallel port to send the triggers to the EEG acquisition system. The second version allows the use of the Raspberry Pi3-based hardware described in the next section ('core_r-pi_nogui. py' file). This version simulates a parallel output via GPIO (general purpose input/output). Both versions were tested, compared, and used, as described in the Results section and follow the same simple code architecture described below.
The PyaudioProtocol reads the playframe (Fig. 1 ) and loads the data, after which it initializes a thread using the python-sounddevice (https://python-sounddevice. readthedocs. io) module, which provides the bindings to the PortAudio (http://www. portaudio. com/) library to benefit from precise audio-streaming management. At the same time, a trigger is sent using pyparralel (https://github. com/pyserial/pyparallel) or Rpi. GPIO (https://pypi. org/project/RPi. GPIO/) from PC or Raspberry Pi3 versions, respectively. The next trigger and sound will be sent to the output device after the given ISI. In parallel to this, interruption management allows the protocol to be stopped at any given time. Finally, PyaudioProtocol allows the correct initialization and closing down of material and programming objects used.