dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Dr. Qian-jie Fu  and the  Emily Shannon Fu Foundation  for providing the experimental software. We also thank three anonymous reviewers for helpful comments. This study was supported by  VIDI  Grant No. 016. 096. 397  and  VICI Grant  No. 918. 17. 603  from the  Netherlands Organization for Scientific Research (NWO)  and the  Netherlands Organization for Health Research and Development (ZonMw) , funds from  Heinsius Houbolt Foundation , and the  Rosalind Franklin Fellowship  from  University Medical Center Groningen. E. G. 's contribution was conducted in the framework of the  LabEx CeLyA ("Centre Lyonnais d'Acoustique ,"  ANR-10-LABX-0060/ANR-11-IDEX-0007 ) operated by the  French National Research Agency. This research is part of the  Healthy Aging and Communication research program  of the  Otorhinolaryngology Department of University Medical Center Groningen.
2 Â 10 7 cells were washed in PEMS (PEM +1. 2 M Sorbitol) and digested with Zymolyase 100T (0. 4 mg/ml in PEMS) for 30 min at 37 ˚C. Images were processed and quantified using ImageJ with automated background subtraction. Aliquots of budding yeast cells grown in YEP-RG media were transferred to Concavalin A-coated Cellview slides (Advanced TC, Greiner Bio-One). After 20 min, cells were washed twice and covered with SC media containing 2% raffinose and 2% galactose medium for live cell imaging. Images were acquired on an LSM780 with a 63Â, 1. 4 NA, Oil Plan Apochromat objective (Zeiss) controlled by ZEN 2012 software. The microscope was equipped with an incubation chamber (EMBL) and heated to 30 ˚C. The pinhole was entirely opened and 7 z-slices with a distance of 1 mm were recorded. Dis3-mNeonGreen fluorescence measurements were performed in Fiji.
05), an unpaired t-test with Welch's correction was chosen to test for statistical significance. FACScan 2 Â 10 6 fission yeast cells were fixed with ethanol 70% (v/v), washed in sodium citrate (50 mM pH 7) and digested with RNase A (100 mg/ml) (Merck). Cells were briefly sonicated and stained with 1 mM Sytox Green (ThermoFischer Scientific). DNA content was quantified on a FACSCALIBUR cytometer using CellQuest Pro software (BD Biosciences). Raw data were analyzed with FlowJo software (BD biosciences). FACScan analysis of budding yeast cells was performed as previously described after staining DNA with either propidium iodide (Figure  4 ;  Cuylen et al. , 2011).
RNA-seq and analysis RNA-seq was performed on biological triplicates. Total RNA was extracted from 2 Â 10 8 yeast cells by standard hot-phenol method. RNA quality was determined using TapeStation (Agilent) with RINe score >9. Ribosomal RNA was removed by treating 2 mg of total RNA with the Ribo-Zero Gold rRNA Removal Kit (Yeast) (MRZY1324, Illumina, Paris, France). RNA-seq libraries were prepared using Tru-Seq Stranded kit. Sequencing was performed on Illumina Hiseq 4000, with single-end reads of 50 nt in length. Total number of reads per sample ranged from 59 million to 93 million. For in-silico analyses, we used the version ASM294v2. 30 of the S. pombe genome in fasta format and the corresponding gff3 annotations downloaded from the ebi website (2017/03/22). Scripts are available in the following git repository: https://github.
com/LBMC/readthroughpombe (Modolo, 2018 ; copy archived at https://github. com/elifesciences-publications/readthroughpombe). The RNA-seq reads were processed with cutadapt  (Martin, 2011)  to remove adaptors and further trimmed with UrQt (-t 20)  (Modolo and Lerat, 2015)  based on their quality. After quality control with fastqc  (Andrews, 2010) , we built the reverse complement of the reads in the fastq files using seqkit, indexed the genome (-build) and mapped the trimmed fastq files (-very-sensitive) using Bowtie2  (Langmead and Salzberg, 2012). Mapping output files were saved in bam format with samtools (view -Sb) (1000 Genome Project Data Processing  Subgroup et al. , 2009). To detect read-through events, we searched for sections of reads located immediately downstream of the 3' ends of annotated transcription units, on the same DNA strand, and within genefree intergenic regions.
First, broad peaks of RNA-seq reads were identified using the peak caller Music v6613c53  (Harmanci et al. , 2014). Bam files were sorted and filtered as forward or reverse with samtools (view -hb -F 0 Â 10 or view -hb -f 0 Â 10). The gff annotation file was converted to bed with convert2bed from bedtools, the genome indexed with samtools (faidx) and the annotation file split into forward and reverse with bedtools (complement). Next, reads corresponding to annotated transcripts were removed using samtools view (-hb bams -L annotation). Subtracted bam files were sorted with samtools sort, and RNA-seq peaks located outside annotated transcripts were detected with Music (-get_per_win_p_vals_vs_FC -begin_l 50 -end_l 500 -step 1. 1 l_mapp 50 l_frag 363 -q_val 1 l_p 0). The resulting annotation was further filtered by removing peaks whose starting positions were located more than two read-length away from the nearest 3'end of a transcript.
Quantifications in mutant strains compared to wt were performed using the package DESeq2  (Love et al. , 2014) with R (v3. 4. 4). For differential expression analyses, we tested for log2 fold change superior to 0. 5 or inferior to À0. 5. For read-through events, we tested for a log2 fold change superior to 0 compared to the wild type to declare the read-through present in the mutant background. For all analyses, we selected p-values with an FDR 0. 05. The package ggplot2 (v2. 2. 1) was used for graphics. The orientation of genes was analysed with R scripts.
Acknowledgements We thank  Vincent Vanoosthuyse  and AndreVerdel for fruitful discussions, and  V Vanoosthuyse  for critical reading of the manuscript. We are grateful to  Francois Bachand ,  Jean-Paul Javerzat ,  Kim Nasmyth ,  Chris Norbury ,  Masayuki Yamamoto  and the  Yeast Genetic Resource Center of the National BioResource Project-Japan  for strains, and to  Keith Gull  for the TAT1 antibody. We thank the  Pole Scientifique de Modelisation Numerique (PSMN)  of  ENS-Lyon  and the Biocomputing Pole of the LBMC for in-silico analyses. We acknowledge the help of Helene Polveche and  Lorraine Soudade  during the initial phase of bioinformatics analyses. We are grateful to support from the  EMBL Flow Cytometry Core Facility. Clemence Hocquet is supported by a PhD studentship from the  Ministere de l'Enseignement Superieur et de la Recherche  and from the  Fondation pour la Recherche Medicale  (grant  FDT20170437039 ).
Xavier Robellet is supported by a post-doctoral fellowship from the  ANR. Research in the Bernard lab  is supported by the  CNRS , the  ANR  (grant  ANR-15-CE12-0002-01 ), the  Fondation ARC pour la Recherche sur le Cancer  (grant  PJA 20151203343 ) and the  Comite du Rhone de la Ligue Nationale contre le Cancer. This research in the Marguerat lab was supported by the  UK Medical Research Council. Research in the Haering and  Steimetz  labs is supported by  EMBL.
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 instead of 30 second scoring-epochs. A T1N marker based on unusual sleep-stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets.
Inter-scorer reliability cohort Supplementary Table  1  reports on the description of the various cohorts included in this study, and how they were utilized (see datasets section in Methods). These originate from seven different countries. We assessed inter-scorer reliability using the Inter-scorer Reliability Cohort (IS-RC)  31  , a cohort of 70 PSGs scored by 6 scorers across three locations in the Unites States  31. Table  1  displays individual scorer performance as well as the averaged performance across scorers, with top and bottom of table showing accuracies and Cohen's kappa's, respectively. The results are shown for each individual scorer when compared to the consensus of all scorers (biased) and compared to the consensus of the remaining scorers (unbiased). In the event of no majority vote for an epoch, the epoch was counted equally in all classes in which there was disagreement.
Optimizing machine learning performance for sleep staging We next explored how various machine learning algorithms (see Methods) performed depending on cohort, memory (i. e. feed forward (FF) versus long short-term memory networks (LSTM)), signal segment length (short segments of 5 s (SS) versus long segments of 15 s (LS)), complexity (i. e. low (SH) vs. high (LH)), encoding (i. e. octave versus cross correlation (CC) encoding, and realization type (repeated training sessions). The performance of these machine learning algorithms was compared with the six-scorer consensus in the IS-RC and with single scorer data in 3 other cohorts, the Stanford Sleep Cohort (SSC)  10, 32  , the Wisconsin Sleep Cohort (WSC)  32, 33  and the Korean Hypersomnia Cohort (KHC)  10, 34  (see datasets section in Methods for description of each cohort).
Model accuracy varies across datasets, reflecting the fact scorer performance may be different across sites, and because unusual subjects such as those with specific pathologies can be more difficult to score-a problem affecting both human and machine scoring. In this study, the worst performance was seen in the KHC and SSC with narcolepsy, and the best performance was achieved on IS-RC data (Supplementary Figure  1  a, Table  2 , Supplementary Table  7 ). The SSC+KHC cohorts mainly contain patients with more fragmented sleeping patterns, which would explain a reduced performance. The IS-RC has the most accurate label, minimizing the effects of erroneous scoring, which therefore leads to an increased performance. Incorporating large ensembles of different models increased mean performance slightly (Table  2 ).
As expected, the relative model variance (standardized to the average variance in a correct wakefulness prediction) is generally lower in correct predictions (Supplementary Table  3 ) and this can be used to inform users about uncertain/incorrect estimates. To demonstrate the effectiveness of this final implementation, the average of the models is shown alongside the distribution of 5234±14 scorers on 150 epochs, a dataset provided by the AASM (AASM inter-scorer reliability (ISR) dataset, (see datasets section in Methods). On these epochs, the AASM ISR achieved a 90% agreement between scorers. In comparison, the model estimates reached a 95% accuracy compared to the AASM consensus (Figure  2 b ). Using the model ensemble and reporting on sleep stage probabilities and inter-model variance for quality purpose constitute the core of our sleep scoring algorithm.
Construction and evaluation of a narcolepsy biomarker The neural networks produce outputs that depend on evidence in the input data for or against a certain sleep stage based on features learned through training. We hypothesized that narcolepsy, a condition characterized by sleep/wake stage mixing/dissociation  [37] [38] [39] [40] [41]  , would result in a greater than normal overlap between stages, an observation that was obvious when sleep stage probability were plotted in such subjects (see example in Figure  3 ). Based on this result, we hypothesized that such sleep stage model outputs could be used as a biomarker for the diagnosis of narcolepsy using a standard nocturnal PSG rather than the more time consuming MSLT. To quantify narcolepsy-like behavior for a single recording, we generated features quantifying sleep stage mixing/dissociation.
Features are described in Supplementary Table  5  with detailed description of the 8 most important features reported in Table  4. Final predictions were achieved by creating a separate Gaussian Predictor (GP) narcolepsy classifier from each of the sleep scoring models used in the final implementation. This was tested in seven independent datasets: a training dataset constituted of PSG from WSC  32, 33  , SSC  10, 32  , KHC  10, 34  , AHC  35  , Jazz Clinical Trial Sample (JCTS)  43  , Italian Hypersomnia Cohort (IHC)  41  , and DHC; with verification in test data mostly constituted of PSG from the same cohorts and independent replication in the French Hypersomnia Cohort (FHC), and the Chinese Narcolepsy Cohort (CNC)  12  , that had never been seen by the algorithm (see Supplementary Table  1 ).
Another contributor was the presence of sleep pathologies in the dataset that could influence machine learning. Of the pathologies tested, only narcolepsy had a very significant effect on the correspondence between manual and machine learning methods (p=1. 77x10 -1  vs p= 7. 53x10 -4 for sleep apnea for example) (Supplementary Table  4  and 7 ). This was not surprising as the pathology is characterized by unusual sleep stage transitions, for example transitions from wake to REM sleep, which may make human or machine learning staging more difficult. This result suggests that reporting inter-model variations in accuracy for each specific patient has value in flagging unusual sleep pathologies, so this metric is also reported by our detector. Unlike previous attempts using automatic detector validations, we were able to include 70 subjects scored by six technicians in different laboratories (the IS-RC cohort)  31  to independently validate our best automatic scoring consensus algorithm.
It is also able to score at higher resolution, i. e. 5 seconds, making it unnecessary to score sleep stages by 30 second epochs, an outdated rule dating from the time sleep was scored on paper. Although the data sample used for multi-scorer validation contained only female subjects, the scoring accuracy of our model was not seen to be affected by gender (Supplementary Table  3 ) in another analysis. Using our models, and considering how typical T1N behaved in our sleep stage machine learning routines, we extracted features that could be useful to diagnose this condition. T1N is characterized by the a loss of hypocretin-producing cells in the hypothalamus  3  and can be best diagnosed by measuring hypocretin levels in the CSF  11  , a procedure that requires a lumbar puncture, a rarely performed procedure in the US.
The most successful model, consisting of an ensemble of different models, achieved an accuracy of 87% on this dataset, and was statistically better performing than any individual scorer. It was also able to score sleep stages with high accuracy at lower time resolution (5 seconds), rendering the need for scoring per 30 seconds epoch obsolete. When predictions were weighted by the scorer agreement, performance rose to 95%, indicating a high consensus between the model and human scorers in areas of high scorer agreement. A final implementation was made using an ensemble with small variations of the best single model. This allowed for better predictions, while also providing a measure of uncertainty in an estimate. When the staging data was presented as hypnodensity distributions, the model conveyed more information about the subject than through a hypnogram alone.
Datasets The success of machine learning depends on the size and quality of the data on which the model is trained and evaluated  51, 52. We used a large dataset comprised of several thousand sleep studies to train, validate, and test/replicate our models. To ensure significant heterogeneity, data came from 10 different cohorts recorded at 12 sleep centers across 3 continents: SSC  10, 32  , WSC  32, 33  , IS-RC  31  , JCTS  43  , KHC  10, 34  , AHC  35  , IHC  41  , DHC  53  , FHC, and CNC  12. Institutional Review Boards approved the study and informed consent was obtained from all participants. Technicians trained in sleep scoring manually labeled all sleep studies. Figure  5   A detailed description of the sample can be found in Young  33  and Moore et al. 32. The sample does not contain any T1N patients, and the three subjects with possible T1N were removed  54.
617 randomly selected subjects were used for training the neural networks while 277 randomly selected PSGs were kept for validation testing of the sleep stage scoring algorithm. These 277 subjects were also used for training the narcolepsy biomarker algorithm. The sample contains PSGs of 25 independent untreated subjects with T1N (12 with low CSF hypocretin-1, the others with clear cataplexy). 26 subjects were removed from the study-4 due to poor data quality, and the rest because of medication use.
Patient-based Korean Hypersomnia Cohort The Korean Hypersomnia Cohort is a high pretest probability sample for narcolepsy. It includes 160 patients with a primary complaint of excessive daytime sleepiness (see Supplementary Table  1  for age at study). These PSGs were used for testing the sleep scoring algorithm and for training the narcolepsy biomarker algorithm. No data was used for training the sleep-scoring algorithm. Detailed description of the sample can be found in Hong et al. 34  and Andlauer et al. 10  The sample contains PSGs of 66 independent untreated subjects with T1N and clear cataplexy. Two subjects were removed from the narcolepsy biomarker study because of poor data quality.
Patient-based Austrian Hypersomnia Cohort Patients in this cohort were examined at the Innsbruck Medical University in Austria as described in Frauscher et al. 35   1  for details). This allowed for a much more precise gold standard, and the inter-scorer reliability could be quantified for a dataset, which could also be examined by automatic scoring algorithms. Detailed description of the sample can be found in Kuna et al. 31  and Malhotra  6. The sample does not contain any T1N patients.
The Jazz Clinical Trial Sample This sample includes seven baseline sleep PSGs from five sites taken from a clinical trial study of sodium oxybate in narcolepsy (SXB15 with 45 sites in Canada, USA, and Switzerland) conducted by Orphan Medical, now named Jazz Pharmaceuticals. The few patients included are those with clear and frequent cataplexy (a requirement of the trial) that had no stimulant or antidepressant treatment at baseline  43   12. Together with the FHC, the CNC was used as data for the replication study of the narcolepsy biomarker algorithm.
Sleep scoring technicians score sleep in 30 second epochs, based on what stage they assess is represented in the majority of the epoch-a relic of when recordings were done on paper. This means that when multiple sleep stages are represented, more than half of the epoch may not match the assigned label. This is evident in the fact that the label accuracy decreases near transition epochs  20. One solution to this problem is to remove transitional regions to purify each class. However, this has the disadvantage of under-sampling transitional stages, such as N1, and removes the context of quickly changing stages, as is found in a sudden arousal. It has been demonstrated that the negative effects of imperfect "noisy" labels may be mitigated if a large enough training dataset is incorporated and the model is robust to overfitting  41.
This also assumes that the noise is randomly distributed with an accurate mean-a bias cannot be cancelled out, regardless of the amount of training data. For these reasons, all data including those containing sleep transitions were included. Biases were evaluated by incorporating data from several different scoring experts cohorts and types of subjects. To ensure quick convergence, while also allowing for long-term dependencies in memory-based models, the data were broken up in 5 minute blocks and shuffled to minimize the shift in covariates during training caused by differences between subjects. To quantify the importance of segment sizes, both 5 second and 15 second windows were also tested.
Training of CNN models Training the models involves optimizing parameters to minimize a loss function evaluated across a training dataset. The loss function was defined as the cross-entropy with L2 regularization: 𝐿𝐿(𝛚𝛚) = 1 𝑁𝑁 ∑ 𝐻𝐻(𝐲𝐲 𝑖𝑖 , 𝐲𝐲 � 𝑖𝑖 ) 𝑁𝑁 𝑖𝑖=1 + 𝐿𝐿2 = 1 𝑁𝑁 ∑ 𝐲𝐲 𝑖𝑖 log 𝐲𝐲 � 𝒊𝒊 + (1 -𝐲𝐲 𝑖𝑖 )log (1 -𝐲𝐲 � 𝒊𝒊 ) 𝑁𝑁 𝑖𝑖=1 + 𝜆𝜆 ‖𝛚𝛚‖ 2 2 ( 8 ) where   is the true class label of the  th window,  �  is the estimated probability of the  th window,  is the parameter to be updated, and  is the weight decay parameter set at 0. 00001. The model parameters were initialized with (0,0. 01), and trained until convergence using stochastic gradient decent with momentum  64. Weight updates were done as: 𝛚𝛚 𝑡𝑡+1 = 𝛚𝛚 𝑡𝑡 + 𝜂𝜂𝐯𝐯 𝑡𝑡+1 with 𝐯𝐯 𝑡𝑡+1 = 𝛼𝛼𝐯𝐯 𝑡𝑡 - 𝛿𝛿𝐄𝐄 𝛿𝛿𝛚𝛚 𝑡𝑡 where  is the momentum set at 0.
High pretest probability sample MSLT are typically performed in patients with daytime sleepiness that cannot be explained by OSA, insufficient/disturbed sleep or circadian disturbances. These patients have a higher pretest probability of having T1N than random clinical patients. Patients are then diagnosed with type 1 or type 2 narcolepsy, idiopathic hypersomnia or subjective sleepiness based on MSLT results, cataplexy symptoms, and HLA results (if available). To test whether our detector differentiates T1N from these other cases with unexplained sleepiness, we conducted a post hoc analysis of the detector performance in these subjects extracted from both the test and replication datasets. FIGURE LEGENDS Figure 1 Accuracy per scorer and by time resolution a The effect on scoring accuracy as golden standard is improved.
a, b, and c summarize the overall design of the study for sleep stage scoring and narcolepsy biomarker development. Supplementary Table1provides a summary of the size of each cohort and how it was used. In the narcolepsy biomarker aspect of the study, PSGs from T1N and other patients were split across most datasets to ensure heterogeneity in both the training and testing dataset. For this analysis, a few recordings with poor quality sleep studies, i. e. missing critical channels, with additional sensors or with a too short sleep duration (≤ 2 hours) were excluded. A "never seen" subset cohort that included French and Chinese subjects (FHC and CNC) was also tested. Below is a brief description of each dataset.
Acknowledgments This research was mostly supported by a grant from  Jazz Pharmaceuticals to E. Mignot. Additional funding came from:  NIH  grant  R01HL62252  ( P. E. Peppard );  Ministry of Science and Technology   2015CB856405  and  National Foundation of Science of China 81420108002 , 81670087  ( F. Han ); H. Lundbeck A/S, Lundbeck Foundation ,  Technical University of Denmark , and  Center for Healthy Aging, University of Copenhagen  ( P. Jennum ,  H. B. D. Sørensen ). Additional support was provided by the  Klarman Family ,  Otto Mønsted ,  Stibo ,  Vera & Carl Johan Michaelsens ,  Knud Højgaards ,  Reinholdt W. Jorck  and  Hustrus , and  Augustinus Foundations  ( A. N. OIesen ). We thank  Briana Gomez  for helping on performing the last corrections on this manuscript.