dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and spike sorting. Extracellular signal recorded from the silicon probes was amplified (1000x), bandpass filtered (1 Hz to 5 kHz) and acquired continuously at 32 kHz with a 64-channel DigitalLynx (NeuraLynx) at 16-bit resolution. We preprocessed the raw data using a custom-developed suite of programs  (Csicsvari et al. , 1999). The signals were down-sampled to 1250 Hz for the local field potential (LFP) analysis. Spike sorting was performed automatically, using KLUSTAKWIK (http://klustakwik. sourceforge. net (Harris et al. , 2000) ), followed by manual adjustment of the clusters, with the help of auto-correlogram, cross-correlogram and spike waveform similarity matrix (KLUSTERS software package, http://klusters. source-forge. net (Hazan et al. , 2006) ). After spike sorting, we plotted the spike features of units as a function of time, and we discarded the units with signs of significant drift over the period of recording.
Firing sets, active information storage, and the information sharing networks were all computed using a binarized raster built from the temporal labeling of spike firing (see Data Collection and Spike Sorting). Spiking data was binned using a 50 ms bin; if a neuron fired within a given bin the output is a '1', and if not, a '0'. This, for example would mean that a 2-hour recording would be transformed from a 7200 second × N neuron matrix to a 7200000 × N neuron matrix that is composed solely of 0's and 1's. Firing sets were computed by computing the average firing density for each neuron within a window, and after these averages were compiled into timedependent vectors. This resulting matrix is the Firing Features. Active information storage was computed by measuring the mutual information of a neuron's binarized spike train between a given window and the window previous.
Feature-Based Substate Extraction State extraction for each recording were also computed using the methods of our previous work, namely based around k-means clustering of each feature. The exception here, is we no longer choose a stable number of K clusters in k-means. Rather we cluster our 3 raster-based computed features (firing, storage, sharing) 3 separate times with K ranging from K = 3, 4, … 10. The function 'kmeans' was used from the default MATLAB toolbox. More information can be found on the Mathworks website. These K values were chosen as they represented a clustering range of too gross to too fine based on previous findings. K <= 2 would represent the same, or less, number of states as global states, which was previously established to be too small  (Clawson et al. , 2019). The clustering became too fine when K >= 10, wherein many substates only appeared for brief time periods, and never re-occurred.
For each feature there are 8 different clustering results, done in an unsupervised manner 3 times to ensure that our results do not rely on single instance of clustering. This gave our analysis an opportunity to compute all metrics defined below over a robust range of K, ensuring that we can investigate how our substate stable metrics and results vary with arbitrarily too little or too many substates. To compute the null model for substate extraction the process detailed above was repeated with the time stamps of all firing, storage and sharing jittered. This therefore retains the global mean and variance. Then, k-means was run on this jittered dataset 3 times, to produce 3 different clustering of the randomized dataset. These were not modified after this step and were used in any instances where a null model was needed (i.
It is important to note that all tables have the same spectral clustering, as the 2 substates of SO and THE are extremely robust as discussed above. This entire process is then repeated for each clustering attempt, resulting in 3 sets of our 512 substate tables for each region for each recording. Where applicable, all results are given as a function of total k states per table (i. e. for state table 1, there are 2 global states, 3 firing, 3 storage and 3 sharing for a total ktotal = 11). To produce the ordered tables for the 'ordered' null model, each substate table was sorted such that all substates with label '1' appeared first, label '2' was second, and so on and so forth. This can easily be achieved with the MATLAB function sort. Note that there is only one possible version of this type of ordering, and therefore the sample size for ordered tables is the same as recordings (n = 5 for control, n = 6 for epilepsy).
Hubs & Hub Stability In this work we define a hub neuron in the same way as our previous work. Namely, for a given feature if a neuron's activity within a given substate was higher than the 90 th percentile it was marked as a hub for the feature for that state. We compute hubs for every iteration of state table as defined above, such that we have a graph, or matrix, (see FIG  4A ) for each state table. These matrices are Neuron × ktotal where each entry is either a '0' for non-hub or '1' for hub. To compute how stable each of these matrices are as a function of k, we compute the normalized hamming distance of each matrix using the pdist2 function in MATLAB but modified so that it gives a sense of how stable hubs are across states, where perfect similarity would result in a '1', and no similarity at all would give a '0'.
Ordered & Random Substate Tables To have relevant points of reference in our measures, each substate table was ordered and randomized. For the case of ordering, all substate labels for all features were sorted in ascending order which keeps the total lifetime of any state constant, while removing the temporal organization in an ordered fashion. In the case of randomization, all substate labels for all features were randomized 500 times, which again keeps the total lifetime of any state constant, while removing the temporal organization in a random fashion. To compute the relative minimums and maximums for comparisons between order and random the MATLAB function 'rescale' was used. The minimums were computed using the average (of a and SO power in the mEC, and to a lesser extent on THE power in CA1.
Each point was calculated by computing mean silhouette values from a random selection of the randomized and normal clustering and taking the difference. This was done 500 times to produce error bars, but the error bars were so small that they appear to be squares on the graph. The blue line is representative of control data and the red line represents epilepsy data. There is a very large difference for firing and storage modalities from the null model for all k values in both CA1 and mEC in control and epilepsy conditions. Of special note is the sharing states found within CA1 (bottom right). We find that for both control and epilepsy conditions, our measure crosses 0 at k=5 and k=7, respectively, but fluctuates back above 0 until k=9 states in control and k=10 in epilepsy. This would indicate that the clustering only weakly holds in these intermediate values of k before not separating states better than a null, shuffled model up until the higher k values.
Acknowledgements P. P. Q. and A. G. performed and administered all surgery, implantation, and experimental recordings. P. P. Q, performed spike sorting, spectral analysis, and data pre-processing. W. C. and D. B. performed computational analysis. T. M. assisted with computational analysis and many of the supplementary materials. All authors designed the study and wrote the manuscript. W. C. is funded through the  M-GATE program , who has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  765549. T. M was funded through  Aix-Marseille Universite. P. P. Q. acknowledges support from  FRM ,  FFRE  and  CURE Epilepsy Taking Flight Award. D. B. has.
5°× 0. 5°), and (2) GRACE-M, which is less noisy but requires further downscaling efforts. In this study, we used both solutions for comparison. Monthly GRACE-derived Terrestrial Water Storage (TWS) anomalies (difference between a TWS value and its average value calculated for the period January 2004-December 2009) were retrieved from the JPL data center (http://grace. jpl. nasa. gov). GRACE-SH data correspond to the RL05 solution from the Centre for Space Research (CSR, University of Texas, http://www. csr. utexas. edu/grace/) scaled with the scaling factor of the NCAR-CLM4. 0 model  [Landerer and Swenson, 2012]. GRACE-M data are the RL06M solution from JPL. We retrieved the JPL R06M filtered from the contribution of oceans [CRI filter,  Wiese et al. , 2016] , therefore giving the TWS time series corresponding only to the land part of a Mascon.
Regarding the top-down approach and the assumptions retained for the design vessel calculation, parametrical studies of high pressure bubble expansions are performed with a fast dynamic calculation code (Europlexus,  (Sridi et al. (2016) ) taking into account the fluid structure interactions in order to investigate vessel pressure loadings of increasing severity aiming at identifying mechanical weak points on the primary circuit. The severity of the pressure loading applied to the the vessel is increased by increasing the bubble initial pressure. Considering an initial pressure and an intial volume, the presure evolution in the bubble is calculated according to a polytropic law (1):.
Patients Patients with thoracic involvement were identified from the French case registry of IgG4-RD patients. This registry is the result of a multidisciplinary multicentric collaboration, which collects prospectively clinical, biological, radiological and pathological data from patients with IgG4-RD. When thoracic involvement was declared, physicians were asked to send the DICOM (Digital Imaging and Communications in Medicine) of CT scans performed at diagnosis before treatment (group 1, see Supplemental Figure  S1 ). The case registry is authorised by the French authorities (Commission Nationale de l'Informatique et des Libertés). We also systematically reviewed all available thoracic CT scans from patients followed in a single tertiary centre without referenced thoracic involvement. All patients presenting with thoracic abnormalities were included in the study (group 2, Supplemental Figure  S1 ).
All imaging data were reconstructed using high-resolution and standard algorithms. Concerning patients from the other centres, physicians were asked to send the anonymised DICOM of CT scans performed at diagnosis before treatment. All imported imageries included thin-section CT scans and high-resolution reconstruction filters. Subsequently, the system and the parameters were not available for imported CT. Monitors were used to view both mediastinal (width, 400 HU; level, 20 HU) and lung (width, 1600 HU; level, -600 HU) windows. The pre-established top anatomic border was the lower part of the neck. The pre-established anatomic bottom boundary was the estimated location of the adrenal glands below the costophrenic angle. CT data were sent to an archiving and communicating system (Centricity Universal Viewer, GE Healthcare, WI, USA).
All CT scans were reviewed by two thoracic radiologists blinded from clinical data and discrepancies were reviewed by the two radiologists together to reach a consensus. Abnormalities were described according to the Fleischner glossary  [16]  as following: bronchiolitis (tree in bud or alveolar nodules), bronchiectasis, solid nodules, ground-glass opacities (GGO), areas of consolidation, pleural effusion or thickening, peribronchovascular thickening, mediastinal lymph nodes, mucoid impactions, septal thickening, honeycombing, reticulations, architectural distortion, and retromediastinal fibrosis ( paravertebral soft tissue band). Subsequently, they were classified into four patterns as previously reported: nodular, round-shaped GGO, interstitial disease and peribronchovascular involvement  [14].
HTLV-1 serologic test and proviral load (PVL) determination HTLV-1 serologic diagnosis was made by ELISA (Ortho Diagnostics, USA) and positive samples were confirmed by western blot (HTLV Blot 2. 4 test, DBL, Singapore). All patients whose serum sample was reactive with either test was submitted to a nested-PCR using HTLV-1 generic primers and amplified products were digested with restriction enzymes  [18]. In order to determine HTLV-1 proviral load, peripheral blood mononuclear cells (PBMC) were isolated from an acid-citrate-dextrose solution and separated by Ficoll density gradient centrifugation (Pharmacia, Uppsala, Sweden). DNA was extracted using a commercial kit (GFX Pharmacia, Uppsala, Sweden). The forward and reverse primers used for HTLV-1 DNA quantitation were SK110 (5'-CCCTACAATCCAACCAGCTCAG-3', HTLV-1 nucleotide 4758-4779 (GenBank accession No.
J02029)), and SK111 (5'-GTGGTGAAGCTGCCATCGGGTTTT-3', HTLV-1 nucleotide 4943-4920). The internal HTLV-1 TaqMan probe (5'-CTTTAC TGACAAACCC GACCTACCCATGGA-3') was selected using the Oligo (version 4, National Biosciences, Plymouth, MI, USA) and Primer Express (Perkin-Elmer Applied Biosystems, Boston, MA, USA) software programs. The probe was located between positions 4829 and 4858 of the HTLV-1 genome and carried a 5' reporter dye FAM (6-carboxy fluorescein) and a 3' quencher dye TAMRA (6-carboxy tetramethyl rhodamine). Albumin DNA quantification was used to normalize variations due to differences of DNA extraction or PBMCs counts as described.
Gating strategy All data were analyzed using FlowJo Software Version 10. 5. 3 for Mac OS X. Major lineage subsets were selected from forward and side scatter properties followed by single live cells (S1 Fig). Doublet discrimination was achieved by plotting FSC-H vs. SSC-A. For innate immunity live, single, HLA-DR+ cells were selected. Hierarchical gating allows then the discrimination of the following innate cell subsets: cDC1 (CD11c + , BDCA2/3 + ), cDC2 (CD11c + , BDCA2/3 -, BDCA1 + ), pDC (CD11c -, BDCA2/3 + ), monocytes (CD11c+, BDCA2/3 -, BDCA1 -). CD16 and CD14 expression further defined the following subsets of monocytes: classical (CD14 + CD16 -), intermediate (CD14 + CD16 + ) and non-classical monocytes (CD14 -CD16 + ). NK populations were defined as HLA-DR -, Lin -, and subdivided into CD56 dim CD16 + and CD56 high CD16 -NK cell subsets.
Acknowledgments BR would like to acknowledge  Dr. Yamila Rocca  ( CIRI, T Walzer team ). Authors specifically thank  Sebastien Dessurgey  and  Thibault Andrieu  (SFR biosciences, cytometry platform) for their technical advices and  Dr. ChloeJourno  for her critical reading, helpful discussions and her help in the statistics. Authors are also grateful to  Dr. David Karlin  for his advices in writing and to  Dr Patrick Lecine  for critical reading. Author Renaud Mahieux passed away during the course of the publication process. Authors would like to dedicate this work to his memory.
Treatments increasing viral clearance rate, may lower risk of onward transmission but this requires empirical demonstration. Ivermectin is a well-established anti-parasitic drug used worldwide for a broad number of parasites and also for topical use against rosacea. Antiviral activity of ivermectin has been demonstrated for SARS-CoV-2 in Vero/hSLAM cells [IB6]. However, concentrations required to inhibit viral replication in vitro (EC50=2. 8M; EC90=4. 4M) are not achieved systemically after oral administration of the drug to humans  [6, 7]. The drug is estimated to accumulate in lung tissues (2. 67 times that of plasma)  [8] , but this is also unlikely to be sufficient to maintain target concentrations for pulmonary antiviral activity  [7, 9]. Current data suggest that the dosages of ivermectin used in human trials are unlikely to provide systemic or pulmonary concentrations necessary to exert meaningful direct antiviral activity.
Methods The systematic review and meta-analysis was conducted according to PRISMA guidelines. A systematic search of PUBMED and EMBASE was conducted to identify randomized control trials (RCT) evaluating treatment with ivermectin for SARS-CoV-2 infected patients. Clinical trials with no control arm, or those evaluating prevention of infection were excluded alongside non-randomized trials and case-control studies. Key data extracted included baseline characteristics (age, sex, weight, oxygen saturation, stage of infection), changes in inflammatory markers, viral suppression after treatment, clinical recovery, hospitalization and survival. Data were extracted and cross-checked by two independent reviewers (HW and LE).
Search strategy and selection criteria RCTs were eligible for inclusion if they compared an ivermectin-based regimen with a comparator or standard of care (SOC) for the treatment of COVID-19. Clinicaltrials. gov [18]  was searched on 14th December 2020 using key words COVID, SARS-CoV-2 and ivermectin to identify studies. The WHO International Clinical Trials Registry Platform (ICTRP) was accessed via the COVID-NMA Initiative's mapping tool, updated to 9th December 2020,  [19]  and Stamford University's Coronavirus Antiviral Research Database (CoV-RDB), updated to 15th December 2020,  [20]  to identify additional trials listed on other national, and international registries. Additionally, literature searches via PubMed, and the preprint server MedRxiv were conducted to identify published studies not prospectively or retrospectively registered in a trial registry.
Duplicate registrations, non-controlled studies and prevention studies were excluded following discussion between the authors. In a third stage of data collection, the research teams conducting unpublished clinical trials were contacted and requested to join regular international team meetings in December 2020 and January 2021. All results available from unpublished studies were also included in this systematic review. All of the clinical trials included in this meta-analysis were approved by local ethics committees and all patients signed informed consent. The primary outcome was all-cause mortality from randomization to the end of follow-up. Changes in inflammatory markers, viral suppression, clinical recovery and hospitalization were measured in different ways between trials and were summarized for individual clinical trials where endpoints could not be combined.
Data analysis Statistical analyses for all-cause mortality were conducted with summary published data, on the intention-to-treat population, including all randomized patients. Clinical trials with at least two deaths reported were included in this analysis. Treatment effects were expressed as risk ratios (RR) for binary outcomes. For each outcome we pooled the individual trial statistics using the random-effects inverse-variance model; a continuity correction of 0. 5 was applied to treatment arms with no deaths. Heterogeneity was evaluated by I 2. The significance threshold was set at 5% (twosided) and all analyses were conducted using Revman 5. 3. All studies included in this analysis were assessed for risk of bias using the Cochrane Collaboration risk of bias standardized assessment tool  [21]  and the outcome of this assessment is given in supplementary table  1.
Methods Participants. Seventy-eight children between the age of 9 and 15 participated in our study from local schools. Three participant had missing data on the ASRT task due to technical difficulties; three children's caregiver reported psychiatric condition; one child did not have corrected-to-normal vision during one session of the assessments; and one children showed extremely low average accuracy according to  Tukey 39  criterion (more than 3 times the interquartile range from the quartiles) consistently throughout the ASRT task. These eight participants were excluded from the analyses. The final sample consisted of 70 participants (M age = 11. 99 years, SD age = 1. 61 years; 37 boys, 33 girls). Participants performed in the normal range on standard neuropsychological tests (Wisconsin Card Sorting Task  40, 41  [WCST, percentage of perseverative errors]: M = 14.
12%, SD = 5. 90%; Counting Span task  42, 43  : M = 3. 17, SD = 0. 78). Due to technical problems, data of three participants on the WCST and data of one participant on the WCST and Counting Span task is missing. Handedness was measured by the Edinburgh Handedness Inventory  44  (EHI). Due to a technical error, EHI of one participant is missing. The Laterality Quotient (LQ) of the sample varied between -100 and 100 (where -100 means compete left-handedness and 100 means complete right-handedness) with a mean of 76. 21 (SD = 36. 50). Furthermore, caregivers of participants completed a parental questionnaire regarding socioeconomic status (SES) and health-related questions (i. e. , whether the child has any neurological, psychiatric, or neurodevelopmental disorder). Caregivers of one participant did not provide information about their socioeconomic status (SES), therefore, data of one participant is missing.
Statistical analyses. Statistical analyses were carried out by SPSS version 25. 0 software (SPSS, IBM) and by JASP 0. 11. 1. 0. software  48. Based on previous studies using the ASRT task  17, 21, 22  , we firstly collapsed the blocks of the task into epochs, with each epoch consisting of five blocks. This way, the Learning Phase contained four epochs, the Testing Phase contained two epochs, while the Retesting Phase consisted of four epochs. Epochs are labeled consecutively (from 1 to 10, Fig. 1d ). From the analysis, two types of low-probability triplets were excluded: repetitions (e. g. , 111, 222) and trills (e. g. , 121, 242), as participants often show pre-existing tendencies to them  13, 49. As described above in the task description, each trial was determined as the last trial of a pattern, random high, or random low triplet.
Mean accuracy (ratio of correct responses) and median RT (for correct responses) were calculated for each participant and each epoch, separately for the three types of trials (i. e. , pattern, random high and random low trials). Based on the three trial types, statistical and sequence learning can be assessed by the cued ASRT task  21  (for further details, see the task's description above). Analyses and results concerning accuracy are presented in the Supplementary Material; here, we focus on RT data. Prior developmental studies showed that age has a large effect on average RTs, with younger children showing slower RTs [e. g. ,  29, 30, 50  ]. To test this, we first calculated average RTs over the 10 epochs (i. e. , RT data were calculated on all correct trials, irrespective of trial types).
In this case, H 0 states the lack of difference between the mean of the memory scores before and after the offline period, while H 1 states that the mean of the memory scores differ. Here, we report BF 01 values, which were calculated using the JASP software [version 0. 11. 1. 0. 48 ]. According to Wagenmakers, et al. 53  BF 01 values between 1 and 3 indicate anecdotal evidence, values between 3 and 10 suggest substantial evidence and values larger than 10 indicate strong evidence for H 0. Values between 1 and 1/3 suggest anecdotal evidence, values between 1/3 and 1/10 indicate substantial evidence, and values below 1/10 indicate strong evidence for H 1. Values around 1 do not support either hypothesis.
Does age affect the one-year retention of statistical and serial-order regularities? To check the possible association between age and retention, we conducted Pearson's correlation between the offline change scores and age. Regarding statistical knowledge, offline change scores did not show correlation with age (r(68) = 0. 06, p = 0. 62, BF 01 = 5. 92). Concerning serial-order knowledge, one participant had to be excluded from the analysis on RT data as they showed extremely low offline change score according to Tukey 39 criterion (more than 3 times the interquartile range from the quartiles). The correlation between the offline change score of serial-order knowledge represented by RT values and age was also not significant (r(67) = -0. 06, p = 0. 62, BF 01 = 5. 91).