dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Participants. Twenty healthy volunteers (9 females and 11 males, 22-35 years of age; mean 29. 1 years, SD 2. 7 years) with no neurological or motor deficits participated in this study. The EEG data of two subjects (participant IDs 19 and 20) was heavily contaminated by artifacts and was therefore excluded from the analysis for data validation. Nonetheless we provide the data of these two subjects for download and tag them as noisy since they might be useful for people developing tools for artifact removal. All participants reported being right handed. Research shows that footedness follows handedness in right handers, although not consistently so in left handers  38. The experimental procedures were approved by the human ethics committee of the Medical University Graz, Austria. Each subject gave signed informed consent before the experiment.
Data acquisition. The recordings were performed at the Institute of Neural Engineering at Graz University of Technology, Austria. Seven 16-channel amplifiers (g. tec GmbH, Graz, Austria) were combined to record EEG data from 108 passive scalp electrodes positioned as in the '5% International 10/20 System' (EasyCap, Herrsching, Germany)  39. Each subject's head circumference was measured to allow for selection of an appropriately sized EEG cap. The cap was aligned on the head such that Cz was 50% of the distance from the nasion to the inion along the midsagittal plane and 50% of the distance from preauricular points. Electrode locations that extended below the conventional 10-20 System layout included F9, FT9, F10, FT10, P9, PO9, P10, PO10, I1, Iz, I2 (for a schema of the electrode layout see Fig.
1b ). Reference and ground electrodes were placed on the left and right mastoids, respectively. All EEG electrode impedances were brought below 5 kΩ before recording. Electromyographic (EMG) signals were recorded from the skin over the tibialis anterior muscles of both legs using standard adhesive-fixed disposable Ag/AgCl surface electrodes. These EMG channels were also recorded using left and right mastoids as reference and ground, respectively. The EEG and EMG data were sampled at 512 Hz, high pass filtered >0. 1 Hz, low pass filtered <256 Hz and a notch filter was applied at 50 Hz to remove power line noise. Foot-ground contacts were measured by mechanical foot switches placed over the calcaneus bone in the heel of each foot. These switches produced event markers for gait cycle heel strike and heel off events.
We also recorded data from three flexible segment twin axis goniometers placed on the right hip, knee and ankle (Plux, Wireless Biosignals, Arruda dos Vinhos, Portugal). The goniometers were attached to the body using medical adhesive tape and placed such that the center of the goniometer was over the joint on the right side of the leg (compare to  29  ). The data were recorded with the TOBI SignalServer, a custom software application for data acquisition developed at the Institute of Neural Engineering at Graz University of Technology  40, 41  , and a Simulink script running on MATLAB 2013 (The MathWorks). The TOBI SignalServer, a cross-platform data acquisition system implemented in C++, is designed to support concurrent multirate acquisition from different hardware devices with a focus on performance and stability.
The code for the TOBI signal server can be obtained from tools4bci. github. io/ SignalServer. In each trial the Simulink model calculated step-by-step cadence (time interval between heel strikes) and adjusted the pacing of the auditory stimulus in the preferred (steady-state) walking condition using the mean of the most recent 6 steps of uncued walking. We also classified left versus right heel strikes in the Simulink model to make sure that the first auditory cue in each trial, as well as the cues marking the tempo changes always occurred relative to a right heel strike. An application in Ruby (https://www. ruby-lang. org/en/) was developed to play the auditory cues. To record the exact timing of the auditory cues, we recorded the auditory stimulation as a digital input to one of the amplifiers.
To this purpose we split the auditory output of the computer so as to feed the auditory cues into a digital circuit that amplified the analog signal so as to drive a transistor into saturation, thus providing a 5-V (TTL) signal to be directly connected to a digital input of the EEG amplifier. All triggers were synchronized via the hardware input of the amplifiers. The goniometers were synchronized to the EEG data using the TOBI SignalServer. During the recording we monitored the EEG data for possible large muscle and movement artifacts. We instructed the participants to relax their shoulders to avoid neck muscle artifacts and to move their head as little as possible. The amplifiers were placed next to the treadmill on two stacked tables so that they were close to the participant's head. Experimental design and procedure.
Auditory tones were generated in MATLAB (The Mathworks), and played as. wav files). Participants were asked to attempt to synchronize their heel strikes to the regular sequence of auditory cue tones, thus building an expectation of when the next cue would occur. After walking 8-12 s to auditory cues at the preferred cadence, the tempo of the cue stream was suddenly increased ('step-advance' perturbation) or decreased ('step-delay' perturbation) by one-sixth of a step cycle, plus a random ≤±25 ms jitter. The cue tempo shift always occurred relative to a right heel strike (as in  42  ). In step-advance or step-delay perturbations, the cue marking the tempo shift would thus seem to participants to arrive either 'too early' (in step-advance) or 'too late' (step-delay). Participants were instructed to adjust their steps as quickly as possible in order to synchronize their heel strikes to the tone cues at the new pacing tempo, which was maintained for 30-70 steps (see Fig.
Data Records All the published data sets are de-identified. All data files are available at OpenNeuro. org with accession number ds001971  44  , organized and archived following the EEG-Brain Imaging Data Structure (BIDS)  45, 46. The study was converted to EEG-BIDS using the EEGLAB-to-BIDS plug-in by Delorme & Pernet (github. com/sccn/ bids-matlab-tools) for EEGLAB  47  , running on MATLAB (The MathWorks). The BIDS specification  45  is a human brain research community standard for organizing and sharing brain imaging data within and between laboratories which has become widely used for archiving functional magnetic resonance imaging (fMRI) data  45. Linked standards for magnetoencephalographic (MEG)  48  and EEG data  46  have recently been published. See bids-specification. readthedocs.
io/en/stable/01-introduction. html for an overview. To preserve detailed information about experimental events occurring during the data recording, BIDS uses the Hierarchical Event Descriptor (HED, version 2. 0) system described at HEDtags. org  49, 50. Datasets are available in. fdt format containing EEG, EMG, goniometer and event data in the same files. Multiple. fdt files are available for each subject representing multiple runs (blocks in one session) of the experiment. For a description of the files available for each run see Table  1  below. For a description of channel types see Table  2. For a full description of event markers see Online-only Table  1. technical Validation EEG data. The EEG setup was carefully prepared to minimize potential artifacts and maximize data quality (see Data Acquisition above).
Quality of foot switches and temporal precision of auditory cues. The quality of the foot switch data was essential to this experiment since the timing of auditory stimulation was adjusted online to match the pace of footsteps during the experiments. The foot switches were therefore continuously monitored during the whole experiment. If there were faulty activations, the treadmill and the paradigm was stopped so that the foot switches could be adjusted. To make sure that we recorded the exact timing of the auditory cues we split the auditory output of the computer to feed the auditory cues into the digital input of the amplifier as described in the methods above. During post-processing, by subtracting the intended latencies of auditory cue onsets with the actual moments at which the cues sounded, we determined that there was a jitter in auditory cue timings of up to ±25 ms.
EMG data. The quality of the EMG signals was assessed before beginning the experiment. Participants were instructed to repeatedly execute brisk foot dorsiflexion. During analysis, the EMG data were re-referenced to bipolar derivations, then high-pass filtered above 30 Hz (using a FIR filter, order 226), then rectified and low-pass filtered below 5 Hz (using a FIR filter, order 846) to obtain the signal envelope. The data were then segmented -1 to 3 s around right heel strikes during uncued walking. We then time warped the envelopes of the signal to the median step latency (across subjects) using linear interpolation. This procedure aligned the latencies of right and left heel strikes across trials. As shown in Fig. 3a , the muscle activations can be clearly seen during the walking condition; as expected, the right leg tibialis anterior is maximally active shortly before the right heel strike.
The EMG recorded from the tibialis anterior muscle of the left leg was noisier and is not displayed. Goniometer data. The goniometers were carefully set up according to the manufacturer instructions (compare to  29  ). After attaching the three goniometers to hip knee and ankle, the signals were checked while the participant walked on the treadmill. The goniometer signals were continuously monitored to ensure minimal interference and data corruption. To visualize the goniometer data, we high-pass filtered the data above 0. 5 Hz (FIR filter order 3380) and then low-pass filtered the data below 5 Hz (FIR filter order 846). The data were then Fig. 2  Technical assessment of the EEG data. (a) Scalp maps showing the scalp distribution of log power differences in mu and beta power between walking and standing periods.
Usage Notes Since EEG data recorded during walking are usually noisy, we recommend the use of advanced artifact rejection and artifact correction methods (available for example in the EEGLAB toolbox, freely available from sccn. ucsd. edu/eeglab/index. php  47. For analysis of EEG data recorded during walking, see the following references:  22, [24] [25] [26] 31. Oth r studies have looked at the types and morphologies of movement artifacts in EEG data during walking; these reports can be helpful to identify EEG artifacts recorded during MoBI experiments (see  [57] [58] [59]  ).
acknowledgements We thank  Daniel Hackhofer  and  Laura Schulz  for assistance during the experiments. This work was supported by  Future Labs Reloaded 2013 of the Faculty of Computer Science at the Graz University of Technology , Austria, a  Marietta Blau Grant  awarded to  Johanna Wagner  by the  OeAD (Austrian agency for international mobility)  and the  BMWF (Austrian Federal Ministry of Science and Research) , by  National Institutes of Health  grant  5R01-NS047293-13  and by gifts to  UC San Diego  from  The Swartz Foundation (Sag Harbor, NY, USA).
Code Availability The code we developed to record EEG/EOG and goniometer data, run the paradigm and adapt cue rate online to the participants' cadence in each trial are based on the TOBI SignalServer freely available online (tools4bci. github. io/SignalServer) a custom SimulinkModel running in MATLAB (The MathWorks) and a Ruby application for playing the auditory cues which is available upon request from the authors.
Introduction A software family designates a collection of software systems that are similar enough to justify their management as a single entity (by considering their commonalities) rather than individually (by considering their specificities). Software product line engineering (SPLE)  [1]  gathers a set of methods which aims to produce such a group of similar software systems while reducing their development cost and their time to market, and increasing their quality and their supply. It is a software development paradigm based on mass customisation and systematic reuse: rather than individually developing each similar software, SPLE seeks to derive a set of software variants from a common set of reusable artefacts (code, requirement, architecture. ) which are structured in a generic architecture.
The term software product line (SPL) encompasses the generic architecture, the reusable artefacts and the set of derivable software variants. Variability modelling is a focal point of SPLE, whose purpose is to document what is common and what varies between the software variants; it aims to facilitate the management of a potentially numerous set of variants by factorising their common parts and identifying their specific ones. Several approaches exist to model variability, such as decision modelling  [2]  or feature modelling  [3]. In this paper, we focus on the most prevalent approach, feature modelling, where a feature is a distinguishable characteristic present in one or several variants, generally representing a high level functionality. In this approach, a software variant is represented by the set of features it owns.
Such a combination of features is called a valid configuration, and represents a high level functional description of a software variant of the SPL. Feature models (FMs)  [3, 4]  are the most commonly used notation to model SPL variability in terms of features. FMs are a family of description languages which permit to describe a set of features and relationships between them; they represent in a compact and understandable way a set of valid configurations, thus delimiting the scope of an SPL. Aside from variability representation, FMs are also used in other SPLE related tasks, as information retrieval, SPL evolution and maintenance  [5] , or product derivation  [6]. However, in some legacy systems, different variants of the same system may have been individually developed without using systematic reuse  [7].
For instance, clone-and-own is a common practice in industries to build variants of existing software systems: it consists in cloning existing softwares and arbitrarily removing or adding functionalities to these clones in order to satisfy a new set of requirements  [8]. In these cases, practitioners may be confronted to a twofold issue. On the one hand, it is difficult to maintain and manage such a group of software variants, especially when their number and complexity increase over time. On the other hand, migrating to SPLE is an arduous task which implies, inter alia, to build a variability model from an existing collection of product variants  [9]. Even with a small number of variants, a manual construction of FMs from existing products is challenging. To ease the migration, research is made about automated synthesis of FMs from product descriptions  [10, 11, 12, 13, 14, 15, 16].
They usually seek to extract correct and relevant relationships between the features present in the set of initial variants, as a basis to build an FM. However, most of the proposed approaches are defined in a functional manner based on an ad-hoc variability analysis through heterogeneous structures and techniques. Formal Concept Analysis (FCA)  [17]  is a mathematical data analysis framework for hierarchical clustering which has been used to support automated synthesis of FMs. As input, FCA takes a set of objects described by attributes, and organises these objects depending on the attributes they share: the result is a canonical structure called a concept lattice, which naturally highlights commonalities and variability of the input set of objects. FCA has successfully been applied in SPLE to structure a set of existing software variants (as objects) described by a common set of features (as attributes).
In Figure  2 , Concept 4 is an object-concept introducing v6, Concept 11 an attribute-concept introducing check, Concept 13 introducing grid and v1 is both, and Concept 9 is a plain-concept. In what follows, the set of all object-concepts of a context K is denoted by OC K , and the set of all attribute-concepts is denoted by AC K. In some applications, it is not necessary to take into account plain-concepts. These cases generally occur when FCA is used to organise by specialisation the elements of the input dataset, but not to emphasise clusters among them. In such applications, one can choose to construct the concept hierarchy without the plain-concepts: the obtained structure is a sub-order called an Attribute-Object-Concept partially ordered set (AOC-poset)  [25]. Definition 2. 5 (AOC-poset).
e. , that displays meaningful information regarding the domain. Semi-automated FM synthesis tries to reduce incoherence of the feature hierarchy by including users decisions in the process. A recent empirical study  [15]  has shown that, when it comes to FM extraction, semi-automated approaches outperform the other solutions, and thus user decisions are necessary at some point to avoid the extracted model to have a bad ontological semantics. This second challenge rises two complementary research questions: RQ1: What part of the process of FM extraction can be fully-automated without altering its semantics? RQ2: What part of the process of FM extraction necessitates user decisions? FCA is a method for data analysis, knowledge representation and information management, which provides a natural hierarchical structure to any dataset composed of a set of objects and formal descriptions.
A sound and complete variability extraction method based on FCA In the field of SPLE, FCA has been principally used to support information and logical relationship retrieval, for FM extraction from variant descriptions  [30, 10] , FM re-engineering  [5] , recovering SPL architecture  [21] , or feature identification in source code  [20, 19]. The specialisation order between the attribute-concepts emphasises relationships between features that are true for the considered set of variants. In this section, we gather the different types of variability information which can be extracted from FCA conceptual structures. We study existing methods in order to obtain an efficient generalised process which is proved sound and complete thanks to the properties given by Ganter and Wille  [17].
Applicability study of FCA on product descriptions In previous sections, we have defined formal concept analysis and showed how two of its associated structures can be used to assist consistent feature models extraction from product descriptions. In what follows, we study their applicability in the software product line reverse engineering context. We discuss two aspects: 1) the size of these structures when generated from product descriptions, and 2) the viability of their generation and management with existing algorithms. Finally, we study sizes and characteristics of ECFDs corresponding to existing FMs taken from the SPLOT repository.
Product descriptions can be found abundantly on the internet in the form of tabular data, called Product Comparison Matrices (PCMs). PCMs permit to compare a set of similar products depending on a set of features. They have been studied in the context of SPLE as product descriptions  [37, 38]. Websites as Wikipedia propose numerous PCMs, including ones about software systems. For this experiment, we took 40 Wikipedia's PCMs about software systems, and we converted them into formal contexts  3. Because features displayed in PCMs can be multi-valued, we applied binary scaling on them to obtain binary attributes: a feature with n different values in the PCM thus produces n binary attributes (one attribute per possible value) in the formal context. These 40 PCMs were chosen randomly among all the PCMs present in the software comparison category of Wikipedia 4.
This time, no heuristics is defined: all the xor-groups are presented to the user who chooses which one to keep in the hierarchy. Or-groups and co-occurrences are not studied in this paper. Partially automated strategies and Natural Language Processing techniques permit here to obtain more consistent FMs than with a fully automated method. Our work is similar to theirs, because we also propose a method relying on user decisions to build the final FM hierarchy. In comparison, we did not define any heuristics to guide the user choice as they do, because we do not consider feature descriptions in our input dataset. But our method may greatly benefit from the usage of their method to further assist the user into building the hierarchy. However, we rely on a unique AC-poset to extract FM logical semantics: our method covers all the necessary logical relationships and extract them in a sound and complete manner.
5 and >1. 5-fold change cut-off and a P-val <0. 01) were established. Each contains around 1000 differentially expressed genes (S1 Table ). These gene lists were analyzed using the functional annotation tool of the DAVID Bioinformatics Resources  [23, 24]  and the KEGG database. Results identified FoxO signaling genes and genes involved in the negative regulation of the cell cycle as the most significantly inhibited by AS1842856 (Fig  3A ). In contrast, AS1842856-treated cells showed a strong increase in the expression of molecular networks involved in cell metabolic activity. Among them, and in accordance with the mitochondrial respiration results, the oxidative phosphorylation pathway was the most affected. We also verified at the protein level that some prototypic targets of FOXO1 in T cells whose expression is positively or negatively controlled by FOXO1, such as CD62-L and IL7-R  [25]  or granzyme B  [26, 27] , respectively, were also down or up-regulated after AS1842856 treatment (Fig 3B ).
The scanned images were then analyzed with Expression Console software (Affymetrix) to obtain raw data (cel files) and metrics for Quality Controls. Microarrays CEL files were directly analyzed using the Transcriptome Analysis Console software obtained from ThermoFisher Scientific. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE125328 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE125328).
Acknowledgments We thank all members of the  "Dynamics of T Cell Interactions" laboratory  for fruitful discussions and  Margaret Buckingham  for carefully proofreading this paper. We also thank the technical staff of  IDMIT Center , the animal care and veterinary staff at  CEA (Fontenay-aux-Roses, France). We thank the  "Agence Nationale de Rercherche sur le SIDA et les Hepatites virales"  ( ANRS, France ) for support provided to the implementation of SIVART cohort in this project. We thank  Gilead  and  ViiV  for providing antiretroviral drugs for use in in vivo experiment in macaques. We are also grateful to the imaging (IMAG'IC) and genomic (GENOM'IC) facilities of the  Cochin Institute  for their great help.
Biomass was measured using crystal violet staining and error bars represent SEM (n = 3). Sterile nitrification medium with 0. 2% glucose (w/v) served as the blank control. Statistical significance, p < 0. 05, is indicated by * Fig. 4  The effect of glucose supplementation on community composition and in a 4 days biofilm of a mixed-species nitrifying community. Cells were grown in Nitrification medium (nitrification) or nitrification medium supplemented with 0. 2% glucose (w/v) (Glucose), with or without 100 µM 2-ethynylpyridine (2E) or 1 µM phenylhydrazine (P). The community composition was determined by LH-PCR (n = 3) and processed into a similarity table using PRIMER-6 software and 500 bp fragments despite the shift in community composition. However, the increased abundance of the species represented by these fragments was associated with an overall reduction in the total diversity as determined by the Shannon-Wiener diversity index, from an average of 13 fragments per sample (H′ = 2.