dataesr/scientific-paragraphs-categorization huggingface dataset:
1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GFSLG 60. 8% Lipase_3 domain-containing protein (C. a variabilis) E1Z814 GHLX01004575. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 55. 3% Lipase_3 domain-containing protein (C. variabilis) E1Z559 GHLX01004232. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 52. 4% Alpha beta-hydrolase (C. sorokiniana) A0A2P6TJS1 GHLX01005999 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 62. 2% sn1-specific diacylglycerol lipase alpha (Auxenochlorella protothecoides) A0A087SMB1 GHLX01005800 IPR029058, AB_hydrolaseIPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 44. 5% sn1-specific diacylglycerol lipase alpha (M. conductrix) A0A2P6V840.
In line with  Neggers et al. (2012) , we advocate that, although not a new approach, the power of SCM/LES comparisons is largely underestimated and under-exploited. Applying history matching to this comparison is a way to fully take advantage of the SCM/LES on a large multi-case ensemble and explore whether there exists a sub-space of the parameter space for which the SCM is able to reproduce a series of LES simulations within a given uncertainty. Note that the metrics can be different from one case to the other. This tool offers the possibility to revisit the different intercomparison exercises documented in the literature and to benefit from this rich database still underused. Eventually, a point that becomes crucial when using LES for parameterization evaluation and tuning is the assessment of LES reliability and its uncertainties.
Data Availability Statement All the programs, scripts, and reference LES are publicly available via a Subversion through "svn checkout http://svn. lmd. jussieu. fr/HighTune"; a fixed version of this code is provided under http://doi. org/10. 14768/70efa07b-afe3-43a4-8334-050354f9deac. Note, however, that this tool is a new research tool, and, as such, is still evolving. The code, the SCM runs and the LES used to produce Experiment 1 is available at http://doi. org/10. 14768/29fbfe70-a8e8-41db-914c-b14be9a6f90b.
Infant electroencephalography (EEG) presents several challenges compared with adult data. Recordings are typically short. Motion artifacts heavily contaminate the data. The EEG neural signal and the artifacts change throughout development. Traditional data preprocessing pipelines have been developed mainly for event-related potentials analyses, and they required manual steps, or use fixed thresholds for rejecting epochs. However, larger datasets make the use of manual steps infeasible, and new analytical approaches may have different preprocessing requirements. Here we propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. Artifacts are detected using multiple algorithms and adaptive thresholds, making it suitable to different age groups and testing procedures without redefining parameters.
Artifacts detection and correction of transient artifacts is performed on continuous data, allowing for better data recovery and providing flexibility (i. e. , the same preprocessing is usable for different analyses). Here we describe APICE and validate it using two infant datasets of different ages tested in different experimental paradigms. We also tested the combination of APICE with common data cleaning methods such as Independent Component Analysis and Denoising Source Separation. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github. com/neurokidslab/eeg_preprocessing, together with example scripts.
Relatively to previous work, the key innovations we propose with APICE are: (1) an iterative artifact detection procedure based on multiple algorithms applied on continuous data, (2) the use of automatically adapted thresholds, and (3) the correction of transient artifacts on continuous data. The use of multiple algorithms and adaptive thresholds makes APICE applicable without modifications across different ages and protocols. It also makes it easily adaptable to adult EEG datasets. The early detection of artifacts in the continuous data enables the experimenter to decide how to deal with artifacts before applying further preprocessing steps, preventing leaks on noncontaminated parts of the data. Additionally, the detection and correction of artifacts on continuous data increase data recovery by avoiding rejecting data segments containing transient voltage jumps.
Finally, APICE is highly flexible, allowing using the same preprocessed data for many kinds of analysis. Note that we build this pipeline to suit our own needs, that is to say, to use highdensity nets to explore infants' cognition. Processing clinical data with a few electrodes has neither the same goal of high robustness at the individual level nor the same spatial redundancy to interpolate data. Nevertheless, we believe that most of the solutions we propose in APICE are usable in many experimental and clinical situations keeping in mind the signal features and the purpose of the EEG recording. It is why all parameters are adjustable even if we provide default values based on our experience. In this paper, we first describe the general logic behind the pipeline and its different steps. We then provide validation of APICE by comparing it with a standard preprocessing pipeline on two datasets with very different properties.
We also compare APICE with a reduced version of it. In the latter version, we kept the automatic detection of artifacts in continuous data but removed the correction of transient artifacts to evaluate the effect of both processes in terms of data quality and data recovery. Finally, we evaluate whether incorporating additional data cleaning methods described in the literature provides any improvement. Specifically, we tested ICA and Denoise Source Separation (DSS)  (De Cheveigné & Parra, 2014; de Cheveigné & Simon, 2008). APICE is implemented in MATLAB, and it uses the EEGLAB toolbox and custom functions compatible with the EEGLAB structure. It is freely available at https://github. com/neurokidslab/eeg_preprocessing, together with example scripts. APICE is modular, allowing functions to be easily recombined to meet different requirements.
For most EEG analysis, it is imperative to reduce this contamination. However, extensive filtering (above 0. 1 Hz) can introduce critical distortions in the data  (de Cheveigné & Nelken, 2019). While alternatives to high-pass filtering exist, such as local detrending methods (de Cheveigné & Arzounian, 2018), we have not observed improved results. Therefore, at this early stage of preprocessing, we apply a high pass filter at 0. 1 Hz, which should not introduce critical distortions in the data but remove the main drifts. We use the pop_eegfiltnew EEGLAB function, which uses the FIRfilt plugin, to perform a non-causal Finite Impulse Response (FIR) filter. We first apply a low-pass filter at 40 Hz with a transition band of 10 Hz. We then apply a high-pass filter at 0. 1 Hz with a transition band of 0.
All algorithms compute a measure for each sample or in a sliding time window. Then, the algorithms reject the data when the measure is above or/and below a threshold. In all cases, it is possible to use absolute thresholds but our advice is to use relative thresholds. Relative thresholds are determined based on the distribution of the measure throughout the entire recording. More precisely, a threshold is computed as, ℎℎ =  3 +  × ( 3 - 1 ), and/or ℎℎ =  1 - × ( 3 - 1 ), where  1 and  3 are the first and third quartiles of the distribution, and  should be provided as an input (by default 3). Because the measures used by the algorithms usually have a normal distribution, this threshold definition successfully identifies extreme values. A single threshold can be defined for all electrodes, or individual thresholds can be established per electrode.
To account for these scenarios and recover as much data as possible, we reconstruct the channels identified as bad during the whole recording and during restricted periods, by spatial interpolation. It is worth noticing that while the reconstruction of non-functional channels is a common practice that can simplify the between-subjects analysis, the signal is estimated as a weighted sum of the goodelectrodes. Thus, it does not add any new information, and the dataset becomes rank deficient. In APICE we apply the artifacts correction as follows. First, we correct transient artifacts using target PCA. Second, we spatially interpolate channels not working during a period. We restrict this interpolation to good times, that is to samples where no more than 30% of the channels are excluded. The underlying assumption is that bad samples contain too strong artifacts affecting too many channels; hence, the spatial interpolation is not effective.
Preprocessing report Within the EEGLAB structure, we register a report containing the size of the data, the amount of rejected data, and the amount of interpolated data after each data processing step. More specifically, we retain the number of channels, samples, epochs, the number of rejected and interpolated points, and the number of bad times, bad channels, and bad epochs. We also provide a function that prints a table in the command window and a text file to summarize these measures: The information is collected for all subjects at critical points during the pipeline. Specifically, it summarizes the rejection percentages before data epoching, before the rejection of bad epochs, and at the final stage. This summary information should help the experimenter to evaluate the pipeline's performance and to detect possible problems.
A description of these functions can be found in Appendix E. As an example, the report for the analysis performed using the APICE pipeline on dataset 1 is shown in Table  1. Table  1. Example of a preprocessing report. The summary table corresponds to Dataset 1 after the rejection of bad epochs. For each subject it shows the total number of channels (Ch), the total number of samples per epoch (Smpl), the total epochs (Ep), the percentage of bad data (BCT), the percentage of interpolated data (CCT), the percentage of bad times (BT), and the percentage of bad channels (BC). Notice that an analog table is also generated before rejecting the bad epochs.
To have a more robust validation, we analyzed two very distinct datasets. Significant changes occurred in the EEG features during development  (Eisermann et al. , 2013; Marshall et al. , 2002; Nelson & Monk, 2001) , and the level of contamination by motion artifacts can vary considerably according to infants' age and the type of task. Therefore, we decided to use two datasets differing in the age of the infants, in the modality of the stimulation, and in the type of task. The first dataset corresponds to an auditory experiment in neonates. Neonates were tested while sleeping, and the task consisted of passive listening, meaning that the data were minimally contaminated by motion artifacts. The second experiment corresponded to a visual task in which 5-month-old infants looked to a sequence of images on the screen.
Dataset 1: Neonates dataset The neonate' dataset corresponds to an auditory experiment. During each trial, infants heard 4 or 5 syllables lasting 250 ms presented every 600 ms. 216 trials were presented to each infant. Scalp electrophysiological activity was recorded using a 128-electrode net (Electrical Geodesics, Inc. ) referred to the vertex with a sampling frequency of 250 Hz. Neonates were tested in a soundproof booth while sleeping or during quiet rest. Participants were 24 (11 males) healthyfull-term neonates, with normal pregnancy and birth (gestational age > 38 weeks of gestation, Apgar score ≥ 7 in the first minute and ≥ 8 in the fifth minute, and cranial perimeter ≥ 33. 0cm). All participants were tested at the Port Royal Maternity (AP-HP), in Paris, France. Parents provided informed consent.
Dataset 2: 5-month-old dataset The 5-month-old infants' dataset was a study investigating their capacity to associate two sets of images. During each trial, an attractor appeared on the center of the screen for 0. 6 s, followed by a first image lasting 1 s, a second image lasting 1. 2 s, and the attractor again during other 1. 0-1. 2 s. The experiment lasted until the infants were fussy (80-140 trials per participant). Scalp electrophysiological activity was recorded using a 128-electrode net (Electrical Geodesics, Inc. ) referred to the vertex with a sampling frequency of 500 Hz. Infants were tested in a soundproof shielded booth while sitting in their parents' lap. Participants were 26 (12 males), 22. 98-weeks-old infants (SD 1. 41, min 20. 86, max 27). All participants were tested at NeuroSpin, in Gif/Yvette, France.
Pipelines evaluation We report different metrics to compare pipelines performance. The metrics include: (1) the standardized measurement error (SME) to quantify the noise  (Luck et al. , 2020)  applied to the auditory ERP for Dataset 1 and to the visual ERP for Dataset 2; (2) the proportion of retained epochs; and (3) the amount of interpolated data in the retained epochs. The SME was computed for the average response over a certain region of interest and time window corresponding to the auditory or visual ERPs using bootstrapping. We randomly sampled with replacement N responses for each subject, where N is the number of trials retained. Then, we computed the mean in time and space. We repeated the process 1000 times, and the standard deviation of the measure across all iterations corresponded to the SME for each subject ERP  (Luck et al.
, 2020). Higher SMEs denote nosier data and smaller SMEs cleaner data. The SME for Dataset 1 was computed over central electrodes in the time window 250-350 ms (Figure  4 ), which corresponds to the auditory response  (Dehaene-Lambertz & Pena, 2001). The SME for Dataset 2 was computed over occipital electrodes in the time window 550-650 ms (Figure  4 ), which corresponds to the P400 visual ERP (de Haan  & Nelson, 1999).
Results The ERPs obtained with the different methods on the two datasets are shown in Figure  4. As can be observed, the shapes of the ERPs do not seem to be affected by the applied preprocessing pipeline. We then investigated whether the interpolation of localized artifacts brings any improvement to the APICE pipeline (Figure  6 ), and compared the three measures after APICE, our full pipeline, and APICEa, a reduced version of APICE in which only the artifacts detection of APICE was implemented. In Dataset 1, we observed no difference in the SME (t(23) = 0. 057268; p = 0. 9548); but more data were retained after APICE (t(23) = 6. 038; p = 3. 7x10 -6 ; mean of the difference 3. 84%), and more data were interpolated in epochs after APICE (t(23) = 3. 2739; p = 0. 0033; mean of the difference 1.
22%). In dataset 2, with APICE we observed a slightly lower SME (t(25) = 2. 4295; p = 0. 02264; mean of the difference 0. 23); more data retained with APICE (t(25) = 6. 1227; p = 2. 119x10 -6 ; mean of the difference 4. 84%); and no difference in the amount of interpolated data (t(25) = 1. 0135; p = 0. 3205). Finally, we considered the two supplementary cleaning methods: ICA and DSS. The ICA step applied on Dataset 1 removed on average 11. 9% of the components (SD 8. 37%, min 0%, max 30%) and 0. 57% of the total variance (SD 0. 46%, min 0%, max 2. 09%). On Dataset 2, it removed on average 48. 3% of the components (SD 17. 18%, min 12%, max 75%) and 2. 89% of the total variance (SD 1. 71%, min 0. 07%, max 7. 30%). The DSS filter on Dataset 1 removed 13. 18% of the total variance (SD 4. 13%, min 6.
51%, max 23. 11%). On Dataset 2, it removed 33. 46% of the total variance (SD 5. 31%, min 19. 48%, max 44. 43%). Neither of the data cleaning methods, ICA or DSS, made a critical change (Figure  7 ). In Dataset 1, ICA marginally improved the SME (t(23) = 2. 0848; p = 0. 04838; mean of the difference 0. 00957), slightly decreased the amount of retained epochs (t(23) = 2. 5841; p = 0. 0166, mean of the difference 0. 17%), and slightly decrease the percentage of interpolated data (t(23) = 3. 5399; p = 0. 001749; mean of the difference 0. 095%). Dataset 2, ICA slightly decrease the SME (t(25) = 2. 3089; p = 0. 0295; mean of the difference 0. 1468), did not affect the amount of retained epochs (t(25) = 1. 3872; p = 0. 1776), and slightly decreased the amount of interpolated data (t(25) = 7. 4111; p = 9.
2x10 -8 ; mean of the difference 0. 47%). Meanwhile, the DSS slightly decreased the SME (t(23) = 3. 3037; p = 0. 003102; mean of the difference 0. 0176) when applied to dataset 1,and did not affect the SME when applied to dataset 2 (t(25) = 0. 96803; p = 0. 3423).
Discussion Using two widely different infant datasets, we compared our pipeline with a standard preprocessing pipeline. In traditional pipelines, data is usually segmented after filtering, epochs containing artifacts are identified based on absolute thresholds (e. g. , the absolute amplitude or a running average of the signal larger than a threshold), and bad channels are eventually interpolated. In the APICE pipeline, different algorithms based on relative thresholds are used to identify artifacts, and the detection and correction steps are performed on the continuous data. We evaluated the pipelines on the stability and reproducibility of the obtained ERPs through the SME procedure. This procedure consists of examining the distribution of the ERP values through multiple random draws of the trials used to compute the subject ERP.
This value should be low if the averaging process correctly cancels the remaining and unwanted "noise"; that is to say if no unexpected large-amplitude not-evoked activity remains in the preprocessed data. We also examined the number of kept trials that can be important for complex paradigms in which pertinent conditions comprise only a few trials. We additionally report the proportion of interpolated data that corresponds to a loss of data, although this loss is relative given the high redundancy between spatial and temporal samples of EEG data. Results show that the APICE pipeline outperforms the standard pipeline for both datasets. SMEs were smaller in both cases with all the tested relative thresholds levels. For Dataset 1 (an auditory experiment in sleeping neonates), which was only mildly contaminated by motion, the standard pipeline retained more trials than our pipeline.
While more kept trials are, in principle, an advantage to recover the ERP through the average process, it is not anymore when it implies a diminished data quality. The neonates were tested asleep, which means that high amplitude artifacts exceeding the absolute thresholds of the standard pipeline were rare. However, implementing various algorithms based on different signal features makes our detection procedure more sensitive to outlier signals. In Dataset 2 (a visual experiment in awake 5-month-old infants), where the contamination by motion artifacts was substantial, APICE shows a smaller SME and a smaller amount of rejected data. Thus, relative thresholds determined by subject and electrode make the artifacts' detection in APICE more accurate, resulting in improved data quality and better recovery of good quality data.
For example, we can exclude motion artifacts before performing ICA or use the amount of data with artifacts in an epoch as a criterion to reject it. Finally, we also tested if ICA and DSS, two data cleaning methods extensively used in adult studies, improve data quality. For ICA, we implemented a procedure that automatically removes components identified as non-neural artifacts using iMARA  (Marriot Haresign et al. , 2021)  from non-high passed filter continuous data (section 2. 5). Results show a modest improvement (decrease in the SME) restricted to Dataset 2. Similarly, for DSS applied on the retained epochs, we observed a slight improvement in the data quality, particularly in Dataset 1. In brief, our results show no substantial improvement in data quality, neither for ICA nor for DSS, even though we tried the latest proposals (combination of ICA with wavelet-thresholding to achieve a better ICA decomposition  (Rong-Yi & Zhong, 2005) , as it was also carried out in HAPPE, another pipeline for developmental studies  (Gabard-Durnam et al.
APICE also includes functions for renaming events, correcting their timings, organizing trials by condition, and computing the average ERP. APICE is freely available at https://github. com/neurokidslab/eeg_preprocessing, with example scripts illustrating its application. APICE is currently limited to MATLAB, but we want to make it available in an open-source programming language in the future, especially to integrate it with MNE python. and electrodes. By default, the algorithm z-scores the data per channel, uses relative thresholds, and rejects data when the power in the frequency band [1, 10] Hz is below the threshold or the power in the frequency band  [20, 40]  Hz is above the threshold. Notice that for this algorithm, the same threshold is used for all electrodes. The following algorithms are more specifically sensitive to motion artifacts, but they will also partially identify non-working channels and other types of artifacts.
When preprocessing a dataset, a series of processes are sequentially applied to many files. We have created a function that sequentially applies a series of specified functions to a set of files. The function operates on all the specified files in an input folder and saves the final files in an output folder.  eega_RunAll: This function considers all the files adhering to a provided file name in a specified folder and saves the resulting EEGLAB structure in a specified output folder. The functions to apply and their parameters are provided to the function as a string, corresponding to the function name, followed by a cell array containing its inputs (excluding EEGLAB structure). It can run the functions for all the files found in the input folder or limit the output files that do not exist.
Acknowledgments This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 695710 ). We thank the families that participated in the studies. We would also like to thank  Marie Palu  and  Chanel Valera  for their help in recruiting and testing the infants and the entire  Neurokids group at Neurospin  for their feedbacks.
There are at least 27 IS families  [3, 4]  defined by the chemistry used by their Tnp, the sequence relatedness of the Tnp as defined  [5]  by TRIBE-MCL which relies on the Markov cluster (MCL) algorithm  [6]  and the sequence of their ends. These families are listed in the ISfinder database (ISfinder, https://www-is. biotoul. fr/) and described in detail in TnPedia (https://tnpedia. fcav. unesp. br/index. php/Main_Page), a source of information on prokaryotic TE which is integrated into the transp o s o n d a t a b a s e , T n C e n t r a l ( h t t p s : / / t n c e n t r a l. proteininformationresource. org/). Here we present an overview of one of these IS families, IS6, whose importance in generating clusters of clinically important antibiotic resistance genes is becoming increasingly clear  [7]  and whose members may use an unusual transposition pathway.