dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, to detect apnoea, we focus on each individual positive spike (to discard body movements or other cardiac arousals) that is preceded by one or more negative spikes appearing within the time window [t T -50, t T -5], where t T denotes the time instant of the (single) positive spike that exceeds a threshold introduced below. The histogram of apnoea durations established from our internal data (Figure  4 ), which is presented in the following section, helped us define this time window. We need to select a threshold for each night, computed from the observed heart rate itself, to account for the high inter-individual variability of heart rate, which is known to be strongly influenced by factors such as age, gender, health condition, and other unmeasurable factors. We would like to remind the reader that the device to be used (Somno-Art) and the associated software were initially developed for automatic sleep staging  [6, 7]  using the heart rate obtained via photoplethysmography and the movements measured by a threedimensional accelerometer.
Internal Data To evaluate the model and its performances in apnoea detection, we relied on 34 recordings (one for each of the 34 subjects), which were divided into learning, validation, and test datasets; the dataset composition is given in terms of apnoea severity in Table  1 , and in terms of age, sex, and body mass index (BMI) in Table  2. The recordings were established during the time allocated to sleep (between lights off-when the subject attempted to sleep, and lights on-the moment the subject woke up for the day) and consist of synchronous recordings of polysomnography (PSG), an accelerometer (ActiSleep+, ActigraphTM LLC, 49 East Chase Street, Pensacola, FL 32502, USA), and a Somno-Art device (see https://www. somno-art. com/, accessed on 2 November 2022); the latter two were placed on the non-dominant forearm.
The PhysioNet Apnea ECG Database As will be detailed in Section 3. 2, we also tested our algorithm on a publicly available database relative to apnoea detection, based on the cardiac signal and the PhysioNet Apnea ECG database  [46, 47] ; both the full ECG and a one-minute-scored apnoea signal are given. The PhysioNet challenge dataset comprises three categories A, B, and C: • Class A contains at least 1 h with an apnoea index of 10 or more, and at least 100 min with apnoea during the recording.
• Class C contains fewer than 5 min of apnoea during the recording (which is clearly not equivalent to the AHI). A full description of the data is publicly available online (see https://www. physionet. org/content/apnea-ecg/1. 0. 0/additional-information. txt, accessed on 10 November 2022). This database also provides the AHI, which allows for evaluating our algorithm on these data. The PhysioNet Apnea Challenge database is composed of 35 training nights (20 "A" apneic subjects, 5 "B" borderline subjects, 10 "C" non-apneic subjects) and 35 "X" test nights (for which the AHI was not initially disclosed). The hypnogram is not provided. We apply the sparse decomposition algorithm on the HR series extracted from these 70 nights using the "QRS" files, which are unaudited and contain measurement errors; these generally appear as spikes in the RR time series (see Figure  6 ).
Evaluation of Internal Data In Figure  7 , a comparison between ground-truth and estimated RDI is presented for the training nights (one night per recording). The results are promising, and although they would not allow for accurate categorisation of apnoea severity (i. e. , on a four-class, standard assessment of severity), they are still a good prediction of the apneic status of a given person or recording. Moving forward on estimating performances on the validation and testing datasets (see Figure  8 ), several misestimation examples can be observed in under-and over-predictions. To understand the reasons for misclassification, we compare the model's behaviour in Figures  9 10 11. Figure  9  shows a situation where the link between heart rate and apnoea is behaving as observed in training, while Figure  11  illustrates a situation of over-estimation, which will be described below (highlighted by the red square in Figure  8 ), and a situation of under-estimation (highlighted by the blue circle in Figure  8 ).
Evaluation of the PhysioNet Apnea ECG Database In this paper, we focus on high RDI (or AHI) indexes as defined in Equation (  5 ) by the AASM  [45]  using only the heart rate. Both standardised indexes do not take into account the duration of apnoea, which is used in the labelling of the PhysioNet data, as indicated below. However, comparing our results with those from the results of the PhysioNet Challenge competitors  [49]  (https://physionet. org/content/challenge-2000/1. 0. 0/, accessed on 10 November 2022) is not easy because of the differences between the performance criteria used. Moreover, some competitors employed their own QRS detection algorithms and carried out significant signal processing to eliminate outliers and filter the signals, while others utilized an ECG-derived respiratory (EDR) signal, which is easier to obtain from the complete ECG (or PPG)  [4, 5]  than from the 1Hz sampled HR (as in our case, as explained in the introduction) or specific waves of the QRS complex.
For these reasons, it is difficult to provide an accurate comparison of performances. However, the usage of this dataset enabled us to evaluate our algorithm on a public database. From the de-noised RR signal, we compute the instantaneous heart rate, which we interpolate to resample at 1 Hz. We then apply the proposed sparse decomposition algorithm using a Gaussian window (σ = 4) as the reference signal and a regularisation parameter of λ = 300. Finally, we count only the number N p of positive coefficients and estimate the AHI as  The correlation between our estimate and the actual AHI equals 0. 75. We also consider the detection of the apneic characteristic of a recording. Our objective is to restrain our method to the detection of severe apnoea. To achieve this, we utilise a threshold of 30 on the ground-truth AHI, represented by a vertical dashed line in Figure  12.
Recordings with an AHI value above 30 are considered severely apneic, while those below 30 are moderate-to-non-apneic. We use the same value of the threshold on the estimated AHI (represented by a horizontal dashed line) to evaluate the detection performance. We are now able to count the occurrences of correct and false detections. In this database, our results show a sensitivity of 0. 87 (27 out of 31 severely apneic recordings are correctly detected) and a specificity of 0. 90 (only 4 out of 39 moderate-to-non-apneic recordings are incorrectly classified as severely apneic). Here again, a few nights are located away from the diagonal. Using the aforementioned thresholds, we highlight cases of over-estimation (red squares) and under-estimation (blue circles). In the following, we focus on recording "A-18", a case of under-estimation for which the presence of apnoea does not induce visible bradycardia-tachycardia events in the heart rate (see Figure  13 ), and recording "X-10", where high heart rate variability generates false positives while making the bradycardia-tachycardia events hardly visible (see Figure  14 ).
Discussion The results of our study are promising, but not sufficient for accurately evaluating the Apnea-Hypopnea Index (AHI) for standard four-class classification of apnoea severity. Nevertheless, we find these results encouraging, particularly on our internal database, where we have access to accurate apnoea scoring from respiratory signals and a hypnogram. This enables us to select an adaptive detection threshold computed from the observed heart rate on each night/subject to address the high inter-individual variability. Additionally, we identified detection problems induced by cardiac events that resemble apnoea but result from other pathologies. In the PhysioNet database, we only used the RR extracted from the available full ECG and processed only the RR time series to remove the simplest artefacts.
Conclusions In this paper, we considered the application of Bernoulli-Gaussian sparse decomposition on the heart rate signal, which provides an elegant graphical visualisation of the succession of bradycardia and tachycardia events that appear in apneic subjects. We focused on the detection of apnoea (and related respiratory events) and the estimation of associated indexes (AHI and/or RDI) as an obvious candidate example. Our results on both our internal database and the PhysioNet database are encouraging, although a few recordings are poorly assessed. Firstly, there is a specificity problem associated with other causes for cardiac events (e. g. , limb movements) that cannot be discriminated against based solely on the heart rate. Secondly, the performance of the estimator is strongly impacted by data quality.
Participants Participants were recruited from the fifth data freeze of the GENFI study between 20 January 2012 and 30 May 2019, including sites in the UK, Canada, France, Belgium, Germany, Italy, Netherlands, Portugal, Spain and Sweden. All aspects of the study were approved by local ethics committees and written informed consent obtained from all participants. Participants underwent a standardised clinical assessment including a clinical history and neurological examination, neuropsychometric assessment, the Mini-Mental State Examination (MMSE) and the CDR® plus NACC FTLD. 21 The CDR® plus NACC FTLD was used to classify mutation carriers as asymptomatic (global score of 0), prodromal (score 0. 5) or symptomatic (score ≥1). Initially, we reviewed all symptomatic mutation carriers recruited to the study and excluded those severely affected (CDR® plus NACC FTLD score of 3, i.
Volumes for subcortical regions were also calculated including the amygdala, hippocampus, nucleus accumbens, caudate, globus pallidus, putamen, thalamus, together with the cerebellum. All measures were expressed as a percentage of total intracranial volume (TIV) computed with SPM12 v6470 ( 29 Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, London, UK) running under Matlab R2014b (Math Works, Natick, MA, USA). 30.
Limitations Firstly, despite the large numbers overall compared with previous studies, when stratified, the numbers in the individual groups are small. Nonetheless, this work provides important evidence about the phenotype of GRN-related PPA which will need to be substantiated in future prospective cohort studies. Secondly, linguistic symptom data was limited to the 10 items of the PASS, and whilst this encompasses the majority of symptoms relevant to the canonical PPA syndromes, it does not separate out single word and sentence repetition (with just a single 'speech repetition' item), which can be helpful in differentiating nfvPPA and lvPPA. Furthermore, we only had data from two linguistic neuropsychometric tests (BNT and mCCT). This is because the GENFI neuropsychology battery was designed to include mainly visual tasks due to the involvement of countries speaking eight different languages and the difficulty of translating and validating verbal tasks across those languages.
In future studies it would be helpful to consider developing a multilanguage verbal linguistic battery to study cognitive deficits further in this cohort. Thirdly, we only had data on episodic memory and social cognition in the genetic groups which limited the comparison with the canonical groups. Lastly, we focused on cross-sectional differences, but longitudinal analysis would be helpful in future studies to understand the progression of disease with additional symptoms and also the presence of very early deficits, particularly in the prodromal genetic cohort. In conclusion, we examined the presence of PPA syndromes in the GENFI cohort and found that the language network was strikingly more vulnerable to GRN mutations than either C9orf72 or MAPT mutations. Two distinct PPA syndromes were found in association with GRN mutations -those with nfvPPA, similar to sporadic nfvPPA, and those with a PPA-NOS, who had mixed features overlapping with the canonical sporadic disorders but, distinctively, impaired grammar/syntax in the presence of relatively intact articulation.
Acknowledgements We thank the research participants and their families for their contribution to the study. Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases -Project  ID  No  739510. National Institute for Deaf People Dunhill Medical Trust Pauline Ashley Fellowship  ( PA23_Hardy ) and  Wellcome Institutional Strategic Support Fund  ( 204841/Z/16/Z ). J. C. V. S. was supported by the  Dioraphte Foundation  grant  09-02-03-00 ,  Association for Frontotemporal Dementias Research Grant 2009, Netherlands Organization for Scientific Research grant   HCMI 056-13-018 ,  ZonMw Memorabel  ( Deltaplan Dementie , project number  733 051 042 ),  Alzheimer Nederland  and the  Bluefield  project. F. M. received funding from the  Tau Consortium  and the  Center for Networked Biomedical Research on Neurodegenerative Disease.
ACKNOWLEDGMENTS The author is grateful to  Piero Rettegno ,  Donato Bini , and  Thibault Damour  for useful discussions and suggestions during the preparation of this work. The author also thanks the  Institut des Hautes Etudes Scientifiques  for the hospitality and the stimulating environment. The author is jointly funded by the  University of Cambridge Trust, Department of Applied Mathematics and Theoretical Physics (DAMTP) , and  Centre for Doctoral Training, University of Cambridge. The present research was also partly supported by the " 2021 Balzan Prize for Gravitation: Physical and Astrophysical Aspects ," awarded to  Thibault Damour.
Acknowledgments We thank  Raef Minwer Barakat  for allowing us to use his quantitative data for Guadix-Baza Basin rodents and  Tanja Kouwenhoven  and  Alexandrina Tzanova  for their data from Mediterranean Sea records. Frits Hilgen  and  Gert-Jan Reichart  are thanked for discussion and the reviewers for their comments. This publication is part of project I+D+i  PID2020-117289GBI00  funded by  MCIN/AEI/10. 13039/5011000110 33. Our research has also been supported by  CERCA Programme/Generalitat de Catalunya ,  AEI-FEDER EU  (to M. F. ),  AGAUR  (Consolidated Research Group,  2017 SGR 960  to M. F. and  2017 SGR 596  to M. G. ), and the  Department of Science, University and Society of Knowledge of the Government of Aragon  (Reference Research Group  E04_20R FOCON-TUR  funding to Fundación Dinópolis -L.
Bioinfomatic Analysis and Reproducibility of µFlu Reassortment Datasets. We developed a bioinformatic pipeline to accurately identify the viral progeny captured in the scRNA-seq data resulting from the µFlu experimental workflow (Fig. 2  A-C and SI Appendix, Fig. S2 ). Our results were robust to a variety of parameter choices (SI Appendix, Figs. S3  and S4 ), and we present herein those based on one such set (SI Appendix, Supplementary Methods and Supplementary Results). After removal of data from BCs with <125 unique molecular identifier (UMI) counts, we analyzed 54,918 BCs (corresponding to cell-containing droplets) across all experiments (SI Appendix, Tables  S3  and S4  and Dataset S1). As we were interested in analyzing reassortant patterns, we wanted to be sure to exclude any droplets containing more than one infected cell (i.
e. , "doublets"). As such, we conservatively PNAS 2023 Vol. 120 No. 6 e2211098120 https://doi. org/10. 1073/pnas. 2211098120 3 of 12 excluded any BCs harboring ≥1 vRNA from both H1N1p and H3N2 strains from further analysis (SI Appendix, Table  S4  and Dataset S2). Doublet rates were high among experiments where coencapsulation of cells infected with differing viral strains was possible (combined six replicates of reassortment experiments: 37. 70%; Premix1 + Premix2: 23. 12%), while doublet rates were low in experiments where cells infected with the different strains were never encapsulated together (No-MixCtrl 2. 39%, Library-MixCtrl 0. 66%) (SI Appendix, Figs. S1E  and S5A ). This suggests that artifactual doublets associated with molecular biology steps (e. g. , stray BCs and template switching) are relatively infrequent in this setting and that the high doublet rates in Premix and coinfection experiments are more likely attributable to coencapsulation of cell aggregates that form after FACS and/ or changes in cell density due to sedimentation during injection.
Across all replicates, following the exclusion of potential doublets, data from 37,492 single-cell-containing droplets remained. Focusing on 4,952 single cells across all replicates for which we had evidence for all eight viral segments (SI Appendix, Table  S4  and Dataset S2), we found that the average rate of reassortment across coinfection experiments was 51. 08% ± 8. 42% SD (Fig. 2D , yellow bars), while the false-positive reassortment rate was <1%, based on the control experiments (Fig. 2D  and SI Appendix, Fig. S5B ). Thus, less than 2% of observed reassortants are artifacts. The false-positive reassortants can be attributed to events happening after breaking the emulsion (e. g. , stray BCs and template switching) as the false-positive reassortment rate was similar in Premix1 + Premix2 (where coencapsulation of cells infected by H1N1p and cells infected by H3N2 viruses in drops is possible) and in No-MixCtrl and Library-MixCtrl (where there is no coencapsulation of cells infected by H1N1p and cells infected by H3N2 viruses) (SI Appendix, Fig.
S5B ). Barnard's test of independence  (56)  was performed by constructing a 2 × 2 table with the frequency counts of reassortant genomes and parental genomes between the Premix control experiments and individual replicates of NGS1 and NGS2 as categorical values. In all cases, the categorical values are "not independent" (P < 10 -5 ), indicating that the reassortment rates in all six replicate experiments are different from the false-positive reassortment rates in the Premix control experiments (SI Appendix, Table  S5 ). We observed similar patterns of reassortment across all replicates (SI Appendix, Fig. S6 A-C  and E-G and Dataset S2), as demonstrated by the high Pearson correlation  (57)  for reassortant genotype frequencies (r = 0. 77 to 0. 88, P < 10 -26 ), single segment frequencies (r = 0.
64 to 0. 92, P < 10 -3 ) and pairwise segment combinations frequencies (r = 0. 72-0. 94, P < 10 -18 ) between pairs of replicates (SI Appendix, Table  S6 ). This justifies that data from R1. 1, R1. 2, and R1. 3 can be merged (SI Appendix, Fig. S6D ), as can data from R2. 1, R2. 2, and R2. 3 (SI Appendix, Fig. S6H ). Furthermore, the high Pearson correlation of reassortant genotype frequencies (r = 0. 919, P < 10 -64 ), single-segment frequencies (r = 0. 957, P < 10 -8 ), and pairwise segment frequencies (r = 0. 955, P < 10 -59 ) between these two merged data sets (SI Appendix, Table  S6 ) justifies the creation of a "merged dataset" with data from R1. 1, R1. 2, R1. 3, R2. 1, R2. 2, and R2. 3, which contains 2,592 reassortant eight-segment genomes, and which is used from hereon (Dataset S2).
Reassortment between Two Circulating Seasonal H1N1 and H3N2 Strains Generates >60% of All Possible Gene Combinations, with a Limited Number of High-Frequency Gene Combinations. The frequencies of genotypes, single segments, and pairs of cosegregating segments, were analyzed among the 2,592 reassortant eight-segment genomes (Fig. 3 ). Barnard's test of independence  (56)  was performed by constructing a 2 × 2 table with the mean intrastrain and interstrain pair-wise segment frequency counts between the experimental dataset and theoretical values of random reassortment. For random reassortment, intrastrain and interstrain pair-wise segment frequency would each be equal to 0. 5. For the merged dataset (and all replicates), the categorical values are "not independent," i. e. , P < 10 -5 (SI Appendix, Table  S7 ), clearly indicating that reassortment is nonrandom.
The cumulative counts of each segment found in a nonhomologous H1N1p or H3N2 genetic background (i. e. , in genomes containing ≥5 segments from the opposite strain) are shown in Fig. 3A. In reassortant genomes where ≥ 5 segments were derived from H1N1p (top), the most frequent H3N2 segments were PB2, PB1, HA, and NA, while PA, NP, M, and NS segments were rarely present. Noticeably, this distribution was nonsymmetrical, as in genomes where ≥5 segments derived from H3N2 (bottom), the H1N1p segments PB1, NP, M, and NS were the most frequently incorporated, while the H1N1p segments PB2 and PA were rarely detected. A total of 159 different reassortant genotypes were observed out of the 254 theoretical possibilities (Dataset S2) but at very different frequencies, approximating to a negative exponential distribution (Fig.
3B ): The 21 most frequent genotypes (Fig. 3C ) represent only 13% of detected genotypes but make up 66% of all reassortant genomes (Dataset S2). Among reassortants with an H1N1p genetic background (red bars in Fig. 3C ), the most frequent genotypes were 7:1 reassortants containing H3N2-NA or -PB1 or 6:2 reassortants containing H3N2-NA and -PB1, in agreement with previously published studies  (13, 36). Conversely, the incorporation of single H1N1p-M, -NS, -NP, and -PB1 segments dominated in the H3N2 genetic background. Genomes wherein the number of segments from H1N1p and H3N2 are more imbalanced are more frequent, indicating a more frequent cosegregation of segments from the same strain (SI Appendix, Fig. S7 ). To investigate this, the frequencies of the pairwise combinations of segments within reassortant genomes were analyzed.
Using data from the 9,438 reassortant genomes containing 2 to 8 segments (2,592 from the eight-segment dataset plus 6,846 from 2-to 7-segment dataset) slightly biased the data, although frequencies of cosegregated pairs of segments remained similar to data from eight-segment genomes (SI Appendix, Fig. S9  A-C compared to SI Appendix, Figs. S9 D-F  and S10 and S11 compared to SI Appendix, Figs. S13  and S14 ). To correct for this bias, the data were normalized using the pairwise frequencies of the eight-segment reassortant genomes to take into account the different dropout frequencies for the different segments (SI Appendix, Supplementary Methods and Figs. S8  and S15 ). The normalized data from the 9,324 reassortant genomes (2,592 from the eight-segment dataset plus 6,732 from 4-to 7-segment dataset) is shown in Fig.
3  G and H (intra-plus interstrain or interstrain combinations only) and closely match those obtained analyzing the eight-segment data (Fig. 3 D  and E ). For pairwise segment frequencies, the best fit by linear regression of the 2-to 8-segment nonnormalized reassortant dataset to data from eight-segment reassortants is y = 0. 621x + 1. 794 (r = 0. 8996, P < 10 -40 ) (Fig. 3F ), whereas for the normalized 4-to 8-segment dataset, the best fit is y = 0. 862x + 3. 445 (r = 0. 95728, P < 10 -60 ) (Fig. 3I ), indicating that normalization corrects for underestimation of pairwise reassortments and increases the correlation with the eight-segment data. Importantly, although we observed differences in the overall mean frequencies of the H1N1p and H3N2 single segments and of the H1N1p-H1N1p and H3N2-H3N2 pairwise segments between experiments, both in the eight-segment and dropout normalized datasets (SI Appendix, Fig.