dataesr/scientific-paragraphs-categorization huggingface dataset:
From this character lattice, we define the lattice of IUPAC motifs of length L for P as follows. Nodes are IUPAC motifs of length L and there is an edge between two motifs M 1 and M 2 , if M 1 and M 2 differ at exactly one position, named i, and the ith letter of M 1 is directly connected to the ith letter of M 2 in the IUPAC character lattice. To construct this data structure, we start by building the nodes of all exact L-mers present in P, that are given by the hashtable. This constitutes the bottom of the lattice. We then gradually generalize each motif by adding one ambiguous character at a time (see Fig. 2. 3). To do that, we treat all positions of a given motif by replacing the current nucleotide with an alternative nucleotide. For example, for the word CACT, we first look at the first position and test whether AACT, GACT and TACT are present in the hashtable.
Simplification of the IUPAC lattice with Mutual Information The previous subsection described the method to organize the IUPAC motifs present in the positive dataset P. The next step is to identify motifs that are significantly over-represented in P in comparison to the negative control dataset N. Different scoring functions have been used in the literature to achieve this task, for example, the Fisher's exact test P-value  [18] , the Z-score  [11] , the compound Poisson approximation  [16] , mutual information  [19] [20] [21] , and many other metrics  [22]. Here, we use mutual information (MI) to first explore the lattice and simplify it, and then Fisher's exact test to rank the selected motifs. The reason is that Fisher's exact test can be used to determine if a given motif is significantly over-represented in a particular dataset.
But the motifs cannot be compared with each other based on their Fisher's exact test P-Value. Indeed, a better P-value can be simply due to a larger count. On the contrary, the MI (mutual information) captures the dependency between a motif and a dataset independently from its number of occurrences. The MI of two random variables is a measure of the mutual dependency between the two variables. In our case, the mutual information measures the dependency between the condition and the motif. We use MI(Occurrence; Condition) to compare distinct motifs during the degeneracy procedure. It is defined as follows: MI(O; C) = i∈[0,1],j∈[P,N] p(O i , C j ) log 2 p(O i , C j ) p(O i ) × p(C j ) (1) The random variable Occurrence (O) corresponds to the absence/presence of the motif m (0 for absence, 1 for presence in a sequence) and the random variable Condition (C) describes the two possible conditions (P or N ).
Overlapping motifs In the scanning mode, the algorithm has to take into account overlapping motifs. For example, if the motif AMGT is over-represented in the dataset, then MGTN, GTNN, NAMG that all overlap with AMGT could also be detected as over-represented motifs. In order to avoid this and return only the representative motifs, an optional post-processing is added, which consists in clustering the over-represented motifs based on their sequence similarity. The motif with the highest MI is first selected as a reference motif, and all other motifs are aligned against it, with one position shift at a time. In this alignment, we consider that two IUPAC symbols can match, if they are identical or included in each others. In order to avoid clustering too many motifs in the case of datasets of low complexity, we do not allow the extension of the main motifs to more than half of their size on either side (Fig.
Results and discussion We applied DiNAMO on multiple datasets. The first one is a synthetic dataset with implanted motifs. The two others are empirical case studies corresponding respectively to peak sequences for ChIP-Seq application and to genomic regions prone to systematic sequencing errors. We also compared DiNAMO with three other programs, MEME-CHIP  [28] , HOMER  [13]  and Discrover  [19] , that use different models for motif representation. MEME-CHIP is from the MEME suite and runs two motif discovery algorithms, MEME  [29]  and DREME  [18]. The MEME algorithm uses expectation maximization to discover motifs modeled by PWMs. DREME uses regular expressions together with the Fisher exact test P-Value. Discrover is based on HMMs and uses the Baum-Welch training algorithm. In contrast, HOMER uses a simple mismatch model and the hypergeometric distribution to score the enrichment of oligos.
Evaluation on synthetic datasets In this experiment, we constructed a series of simulated datasets that allowed us to control the number, frequencies, and global level of degeneracy of the over-represented motifs. This setting also allowed us to measure the sensitivity and specificity of the tools, since we know exactly which motifs have been implanted. Generation of random sets of IUPAC motifs. We constructed several sets of IUPAC motifs of fixed length 6 to implant them in the positive dataset, with the following varying parameters: the number of motifs in the set and the IUPAC content of the motif. The number of motifs ranges from 1 to 4. The IUPAC content is calculated by summing up the degeneracy level of all the motif letters (see Fig. 1a ). The allowed values are 6,8,10,12 and 14. The lowest value 6 corresponds to exact motifs with no degenerate characters, while the largest value 14 corresponds to motifs such as ANANAH, MRNWYY or BAVCHB.
We considered all possible combinations of those two parameters, which gave a total of 20 combinations. For each combination, we generated 5 sets of motifs, giving rise to 100 different sets of motifs (Additional file 1, Table  S1 ). Implantation of the random IUPAC motifs. Two files of 5000 random DNA sequences were built with the RSAT sequences generator  [11]  using independent and equiprobable nucleotide distribution. These two files are used for each set of motifs. The first one serves as a negative control dataset. The second one is for the positive signal dataset, in which we implanted motifs at 6 different frequencies: 5%, 4%, 3%, 2%, 1% and 0. 5% of the number of initial sequences. Each IUPAC motif is uniformly represented by its instances, and all motifs in the set are implanted with the same frequency.
Results We used the nucleotide level correlation coefficient (nCC) to evaluate the performance quality  [30]. The nCC is a balanced measure that captures the sensitivity and the specificity of the predictive method. nCC = TP × TN -FN × FP √ (TP + FN)(TN + FP)(TP + FP)(TN + FN) ( 3 ) where TP/TN/FP/FN are the number of nucleotides in the dataset that are estimated to be true positives/true negatives/false positives/false negatives. The nCC takes on values between 1 (perfect prediction) and -1 (perfect inverse prediction). An nCC equal to 0 means that there is no correlation between the prediction and the actual occurrences of the motifs. To summarize the results of the repeated experiences for each set of parameters, we calculated the average nCC. Results are reported in Fig. 4. DiNAMO achieves the best results for the detection of degenerate motifs compared to all other tools (best nCC value).
4c ). In Additional file 1, Figure  S2 , we provided more details about the impact of each parameter at variable frequencies on tools performance. We also evaluated the specificity of each tool with 100 randomly generated datasets without any implanted motifs. The specificity (SPC) measures the proportion of identified true negatives in the dataset (SPC = TN/N, where N corresponds to the total number of nucleotides in the dataset). As for implanted motifs tests, we ran the tools with the default parameters, and we searched for motifs of length 6 with up to 6 degenerate positions. Results shows that all tools have a high specificity (> 0. 98) (see Additional file 1 Figure  S3 ).
ChIP-Seq datasets A main application of motif discovery is to extract DNA binding site motifs from peaks obtained from ChIP-seq data. We used the five datasets introduced in  [18]  for the evaluation of their tool DREME: three mouse embryonic stem cell datasets  [31] , corresponding respectively to the transcription factors Oct4, STAT3 and Sox2, and two mouse erythrocyte datasets  [32, 33] , corresponding respectively to Gata1 and Klf1. The number of peak sequences in each dataset is reported in Table  2. For each of those five datasets, the positive file was constructed by extracting sequences of 100 bp length centered around each peak, and the negative dataset by shuffling these sequences with the dinucleotide shuffling tool from the MEME suite  [8]. We ran DiNAMO, MEME-CHIP, HOMER and Discrover with IUPAC motifs of length L = 7 containing up to 3 degenerate letters (d = 3).
Like all the TFBS (Transcription Factor Binding Site) discovery tools, we used the sliding window mode in DINAMO and screened each position of the sequences. Then, we applied the clustering procedure described in Section Overlapping motifs. Predicted IUPAC motifs are then identified by comparing them against the frequency matrices of the JASPAR database  [34] , using the TOMTOM tool of the MEME suite  [8]. Since each motif can match multiple frequency matrices in JASPAR, we considered the ten first significant matches of TOMTOM. All methods performed well on the full dataset (100%), and correctly identified the expected transcription factor. Moreover, they found several secondary motifs. For each dataset (GATA1, SOX2, OCT4, STAT3, KLF1 respectively), 18,17,13,17,5 cofactors are found by DiNAMO, 11,16,10,3,2 by MEME-CHIP, 9,10,12,7,6 by HOMER and 3,4,4,2,2 by Discrover.
Most of these motifs have been already validated experimentally as co-factors of the principal transcription factor (see Table  S2  in the Additional file 2). Each positive dataset was then downsampled in order to evaluate the performance of the methods according to the sample size. We performed seven different sample sizes: 50%, 20%, 10%, 5%, 2%, 1%, 0. 5% of original peak files. For each sample size, the experiment was repeated 100 times, and we counted the number of times that the expected motif, corresponding to the transcription factor binding site, was correctly found (Fig. 5 ). We noticed that DiNAMO and MEME-CHIP predict the true TF motif in most cases (nearly 100% of cases until 5% of peak sequences), but DiNAMO has a better sensitivity with low frequencies. It is important to notice that in fact, MEME-CHIP use two tools (MEME & DREME) to achieve such sensitivity, which also affects the program running time (Table  3 ).
For each dataset, we do a sampling with different sizes. The x-axis shows the amount of token sequence from the original file. For each percentage the sampling experience was repeated 100 times. The y-axis represents the number of times the motif was detected among the 100 repetitions analysis. For the sensitivity of mutation calling method, we usually use low thresholds to call a mutation  [38]. On the other hand, to obtain a good specificity, we sequence the interesting regions with a high coverage (≥ 100×) to be able to separate more easily low allelic variants from sequencing errors. However, high-coverage sequencing does not eliminate all errors, especially the systematic sequencing errors, which are a major issue in high-throughput sequencing platforms  [40]. On the contrary, it has been shown that the number of systematic errors that are called as true mutations (False positive rate) increases with sequencing coverage  [41, 42].
This errors occur mostly at specific positions in the reads and can be confused with true genomic variations. In  [43] , the authors showed that systematic errors depend on the upstream context and that there are overrepresented motifs upstream of the sequencing error positions in Illumina reads, in particular GGT motif. This observation was also corroborated in  [4] , where the authors applied a motif discovery approach and found a similar motif. This last approach, however, did not take into account the full IUPAC alphabet. It was limited to exact motifs with only N letters, and not adapted for large genomes, like the human one. GATK  [44]  integrates the motifs information also by recalibrating the bases quality score in the BQSR module, but their method is limited to exact motifs of length 2 for mismatches and 3 for indels.
We applied DiNAMO to analyze in a more comprehensive way the upstream regions of systematic sequencing errors. For that, we used the monocyte dataset described in  [43]. This compilation contains 3272 genomic coordinates of sequencing errors on the Human genome (Hg18). We kept only the positions not located in chromosomal extremities (3249 positions). For each of them, we retrieved a window of length 42 (the error position and the 41 upstream nucleotides) on the two genome strands, giving a total of 6498 sequences. These sequences were then split into two equal fragments of length 21. Sequences that contain the error position constituted the positive dataset, while the other sequences constituted the negative dataset. In this way, we made sure of having the same nucleotide frequencies to withdraw potential bias due to codon usage.
Conclusion In this article, we presented an exact algorithm for IUPAC motif discovery, which achieved excellent results on different types of biological applications. In all cases, DiNAMO was able to detect subtle signals with high sensitivity. The method successfully explores all degeneracy levels of the IUPAC alphabet and does not require any prior knowledge, other than the length of the motif and the maximal number of degenerate positions in the motif. The second major advantage of the method is that it is tractable in practice. In Table  3 , we report the execution time and the memory space required to run DiNAMO. Due to the data structures involved, it needs significantly more memory space, but can still be run on a desktop computer. All these characteristics make DiNAMO an efficient and universal algorithm that is well-suited for any type of applications.
The provision of high-resolution in situ oceanographic data is key for the ongoing verification, validation and assessment of operational products, such as those provided by the Copernicus Marine Core Service (CMEMS). Here we analyze the ability of ARMOR3D-a multivariate global ocean state estimate that is available from CMEMS-to reconstruct a mesoscale anticyclonic intrathermocline eddy that was previously sampled with high-resolution independent in situ observations. ARMOR3D is constructed by merging remote sensing observations with in situ vertical profiles of temperature and salinity obtained primarily from the Argo network. In situ data from CTDs and an Acoustic Doppler Current Profiler were obtained during an oceanographic cruise near the Canary Islands (Atlantic ocean). The analysis of the ARMOR3D product using the in situ data is done over (i) a high-resolution meridional transect crossing the eddy center and (ii) a three-dimensional grid centered on the eddy center.
INTRODUCTION The growing capacities of new products developed in operational oceanography  (Schiller and Brassington, 2011; Bell et al. , 2015; Chakraborty et al. , 2015; Le Traon et al. , 2015; Hernandez et al. , 2015; Kaurkin et al. , 2016; Sotillo et al. , 2016)  imply a need for ongoing assessment using independent high-resolution in situ data sets. Such efforts help ensure the provision of high quality services by, for example, the Copernicus Marine Service. Thus, advancement in our knowledge of the ocean depends on high quality three-dimensional (3D) observations of the global ocean. However, cruise-based in situ oceanographic sampling typically only covers small targeted regions of the ocean during short periods of time, providing knowledge of specific oceanographic features. Sustained observations in the framework of the Global Ocean Observing System (GOOS) address only a limited number of observational targets, and open ocean high-resolution measurements targeted at the mesoscale are not yet considered  (Legler et al.
, 2016). In order to take advantage of the global coverage provided by remote sensing observations and the available in situ vertical profiles of temperature and salinity,  Guinehut et al. (2012)  made the first 3D operational multi-observation estimate of the ocean state by merging remote sensing observations (sea level anomaly and sea surface temperature) with in situ temperature and salinity vertical profiles (XBTs, CTDs, moorings and Argo profiles). This multivariate observation-based product, named ARMOR3D, provides global 3D data sets of temperature, salinity, geopotential height and geostrophic currents on a 1/4 • horizontal regular grid with weekly temporal resolution. Mulet et al. (2012)  perform a validation of the ARMOR3D product by comparison with model reanalysis and in situ observations (ANDRO current velocities from Argo float displacements and velocity measurements from the RAPID-MOCHA current meter array).
, 2009; Barceló-Llull et al. , 2017b). Barceló-Llull et al. (2017a)  used these 3D in situ fields to infer the QG vertical velocity distribution within the anticyclonic intrathermocline eddy, as well as the vertical velocity obtained from the integration of a generalized omega equation valid for high Rossby numbers which includes the forcing contribution of the ageostrophic horizontal velocities. First, we perform a revision of the hydrographic eddy signature comparing ARMOR3D fields with high-resolution in situ data from the surface to 1,000 m depth. Then, we proceed with the analysis of ARMOR3D-derived dynamical variables such as the QG vertical velocity that has previously been computed and analyzed from ARMOR3D fields but has not yet been validated with in situ data. The final objective is to evaluate the ability of the ARMOR3D product to resolve mesoscale eddies for a specific regional case (the Canary Eddy Corridor) and reproduce their hydrographic and dynamic 3D structure.
The SeaSoar provided vertical profiles from 10 m down to 325 m every 4 km, while the rosette stations were 10 nautical miles apart with a nominal depth of 400 m. The 3D survey was conducted over 5 days (6-11 September) and the Le Tourmalet transect was sampled over 3 days (12-15 September). The 3D sampling was centered at (20. 1 • W, 26. 1 • N) while the Le Tourmalet transect crossed the eddy center at 20. 3 • W as the eddy moved westwards with a translation speed of 4 km d -1 derived from SLA nearreal-time maps. Raw data files were processed with Sea-Bird SEASOFT (http://www. seabird. com/software/softrev. htm). We used TEOS-10 algorithms to compute conservative temperature, , absolute salinity, S A , and potential density excess, σ  (Feistel, 2003 (Feistel, , 2008)). In all phases, current velocities were measured continuously using a hull-mounted Acoustic Doppler Current Profiler (ADCP) working at 75 kHz.
, 2017b). Geostrophic velocities were diagnosed from the 3D density through thermal wind balance, imposing the ADCP velocity at the reference level (325 m depth) (Appendix). See  Barceló-Llull et al. (2017b,a)  for detailed description of the sampling strategy and data processing. For the present comparison we use data from (i) the highresolution meridional transect crossing the eddy center, and (ii) the 3D grid covering the eddy region.
Then, this first step estimate is combined with in situ data through objective analysis. From the resulting global 3D data set of temperature and salinity, the geostrophic currents are estimated through the thermal wind relation considering the reference level at the surface, where geostrophic currents are derived from altimetry  (Mulet et al. , 2012). The multivariate ocean state data set is freely available for the Copernicus Marine Environment Monitoring Service (CMEMS) users at the webside http://marine. copernicus. eu/servicesportfolio/access-to-products/?option=com_csw&view=details& product_id=GLOBAL_ANALYSIS_PHYS_001_020. The version used here is the near-real-time ARMOR3D L4 analysis (the product available at the time of the study) which uses near-realtime altimetry maps that benefit from the same improvements done for altimetry reprocessing  (Capet et al.
38 for the ARMOR3D product. In situ geostrophic velocity magnitude is double the ARMOR3D geostrophic velocities with a similar deceleration of the flow at the northern part of the eddy (Figure  4 ). The scaled geostrophic vertical relative vorticity, ζ g /f , provides an estimate of the local geostrophic Rossby number. Figure  5  shows different vertical distributions of ζ g /f for each data set related to the subsurface intensification of the in situ flow in contrast to the surface intensified ARMOR3D flow. The ARMOR3D field has an extreme value at the eddy center of ζ g /f = -0. 16 while with the in situ data we obtain a ζ g /f = -0. 46, almost three times larger (Table  3 ). QG vertical velocity is estimated for both data sets. For the ARMOR3D case, we derive w QGar for two consecutive weeks: 03-09-2014 and 10-09-2014, as the in situ 3D sampling was carried out in the middle of these two dates.
FUNDING During the drafting of the manuscript BB-L was supported by a pre-doctoral grant from the Spanish National Research Program associated with the PUMP project  (BES-2013-065459). Now she is supported by a post-doctoral contract in the framework of the PRE-SWOT project (CTM2016-78607-P) funded by the Spanish National Research Program and the European Regional Development Fund (MINECO-FEDER). This paper is a contribution to the PUMP project (CTM2012-33355) funded by the Spanish National Research Program. EM is supported by the Copernicus Marine Environment Monitoring Service (CMEMS) MedSUB project. AP acknowledges support from the PRE-SWOT project (CTM2016-78607-P) funded by the Spanish National Research Program and the European Regional Development Fund (MINECO-FEDER). SM acknowledges support from the European Copernicus Marine Environment Monitoring Service (CMEMS).
Such approaches have made significant progress in recording and altering brain activity while allowing full body movements outside laboratory environments. This special issue contains eight published works (six original research articles and two review articles) selected from 17 submitted articles addressing novel trends of various portable and wearable brain technologies that can be used for neuroenhancement and neurorehabilitation. O. Dehzangi and M. Farooq present the design of a portable braincomputer interface (BCI) system that is optimized to operate effectively in intensive care unit environment. Their system consists of a wearable EEG cap together with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, in order to overcome the challenges that BCI systems face nowadays in real-world scenarios, they propose a novel subject-specific Gaussian Mixture Model (GMM) based training and adaptation algorithm.
S. Blum et al. present the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. T. Kondo et al. evaluate the difference between the therapeutic effect of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and that of continuous theta burst stimulation (cTBS), when each is combined with intensive occupational therapy (OT), in poststroke patients with upper limb hemiparesis. They recommend the use of 2400 pulses of LF-rTMS/OT for 2 weeks as treatment for hemiparetic patients.