dataesr/scientific-paragraphs-categorization huggingface dataset:
The Gallus gallus wild-type COMTD1 N protein consists of 260 amino acids with a C-terminal Methyltransf_3 O-methyltransferase domain (based on the NCBI Conserved Domains Database; E-value: 1. 7x10 -79 ; pfam:01596). The protein encoded by the COMTD1 IG1 transcript deviates from COMTD1 N at codon 163 and contains a premature stop at codon 201. Thus, the protein encoded by COMTD1 IG1 lacks the C-terminal 98 amino acids present in the predicted wildtype protein, and these are replaced by 38 amino acids due the frameshift. The protein sequence derived from the COMTD1 IG2 transcript aligned well to wild-type protein sequence except for the 4 out-of-frame amino acids derived from exon 5 and the 44 missing amino acids encoded by exon 6. As indicated in Fig 4C, the mutant isoforms lack considerable portions of the O-methyltransferase domain and are therefore predicted to be non-functional.
Acknowledgements The authors would like to thank  K. Wimmer  for providing us the input for FRESCO calculations and  A. Moro  for giving access to an unpublished version of the FRESCO code. This work was supported by the  U. S. Department of Energy (DOE), Office of Science, Office of Nuclear Physics , under Grant No. DE-SC0020451 , the  U. S. National Science Foundation (NSF)  under Grant No. PHY1565546  ( Operation of the NSCL ) and  PHY2110365 , and by the  DOE National Nuclear Security Administration  through the  Nuclear Science and Security Consortium , under Award No. DE-NA0003180. GRETINA was funded by the  DOE, Office of Science. Operation of the array at NSCL was supported by  DOE  under Grants No. DE-SC0019034  (NSCL) and No. DE-AC02-05CH11231  (LBNL). A. P. acknowledges the Grant  CEX2020-001007 -S funded by  MCIN/AEI/10.
Methods: We collected , posts shared between July and July on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the di erent symptoms and categories of symptoms and to study the co-occurrences and correlation between them. Results: More than % of the posts mentioned at least one Long COVID symptom. Fatigue (. %), pain ( %), clouded consciousness (. %), anxiety (. %) and headaches (. %) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e. g. , fever, sinonasal congestion). Di erent categories of symptoms were found: general (. %), neurological/ocular (. %), mental health/psychological/behavioral (. %), body pain/mobility (. %) and cardiorespiratory (. %). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.
This willingness is not limited to those with chronic illnesses as shown by Park and Conway's lexicon-based and supervised machine learning approaches, which confirmed the prevalence of communicative diseases discussions and public health concerns on Reddit  (15). Long COVID was explored by Sarker and Ge through the Reddit community "covidlonghaulers"  (16). Outside of social media, Long COVID research on health records was conducted. Wang et al. employed language processing tools to build a symptoms lexicon for Long COVID from clinical records (17) while Zhang et al. studied the characteristics of the condition and identified potential subphenotypes using machine learning  (18). Within this framework, this research work focused on analyzing self-reported Long COVID experiences on Reddit to identify potential profiles of the condition, not only in terms of individual symptoms but also the categories of symptoms and the eventual associations between them.
Data analysis Most of the posts were originally published in the "covidlonghaulers" subreddit, being the most popular Long COVID community. The raw dataset encompasses 27,216 posts from 8,434 distinct users with an average number of 3 posts per user (SD = 6, Q1 and Q2 = 1, Q3 = 3). The weekly evolution of the general number of posts over the span of 2 years can be seen in Figure  2. We inspected the length of the posts and found that the average number of words per post rounds up to 137 words (SD = 191, Q1 = 38, Q2 = 84, Q3 = 165). The longest post contained 5469 words while the shortest was only one word (which was then excluded during the preprocessing phase). In the subsequent stages of this study, the statistical examination of symptoms, clusters and their associations involves the utilization of quantifiable measures such as percentages, frequencies and co-occurrence analysis.
Personal/non-personal Long COVID text classifier It is common that text discussing Long COVID can also include information about the acute infection with COVID-19 as well as news, scientific articles, or anything not descriptive of the personal experience itself with the long-term condition. As our goal was to investigate the experiences and perspectives of people with PASC, we needed to remove portions of the text that were not related to these aspects. For this particular purpose, we built a classifier that identifies sentences with explicit mentions of personally having or suspecting to have Long COVID or being close to someone who does, e. g. , a family member. Two authors (HA, CB) manually labeled a dataset containing a total of 1,395 text samples. This dataset consisted of random sentences from posts.
Data preprocessing Multiple preprocessing steps were applied to the collected 27,216 English posts. First, we computed the pairwise cosine similarity between the Term Frequency-Inverse Document Frequency (TF-IDF) vectors of the Reddit posts to detect contentbased duplicates. Second, the Personal/Non-personal Long COVID text classifier filtered out irrelevant posts and sentences. In fact, for each post, the sentences including an experience with Long COVID were kept. We ended up identifying 20,377 relevant posts. Third, we moved on to a standard preprocessing pipeline of replacing context-specific words and contractions, tokenization, removing punctuation, preprocessing emojis and emoticons, removal of non-ASCII characters, lowercasing and lemmatization. Stop words are commonly used words such as the words "the" or "and" in English.
K-means clustering K-means is an unsupervised clustering algorithm. A known challenge with K-means is the choice of K, the initial value of the number of clusters. We experimented with values ranging from 2 to 200 to then closely focus on a smaller interval and evaluate the results for each value. Several criteria were taken into consideration to validate the final clustering result. First, the silhouette score was computed based on the average distances between each data sample, the samples of the same cluster and those of the nearest neighboring cluster. This score is commonly used as an evaluation metric to assess how well the text sample fits in its current cluster compared to the other clusters  (23). Second, the distribution of the posts across the clusters was also considered. For instance, obtaining multiple clusters containing a very low number of posts or obtaining a few enormous clusters both indicate that more values of K should be explored.
Finally, the number of extracted symptom groups and their epidemiological interpretation also played an important role in finding the best number of clusters. The Elbow method was used to approach the value of the number of clusters K. To label the clusters, we read at least the 20 closest text posts to each cluster center and looked at the most frequently used words and word pairs in the clusters. We used the Python Scikit-learn library's implementation of Mini-Batch K-Means clustering which accelerates the learning for large-scale data. It does incremental updates of the cluster centers' positions using mini-batches of the samples instead of the whole set  (24). In addition, we used the t-distributed stochastic neighbor embedding (t-SNE) method for the projection of the highdimensional TF-IDF input data vectors in a two-dimensional space  (25).
2%), general ache/pain (48. 4%), brain fog/confusion (32. 8%), and dyspnea (28. 9%)  (16). While establishing a Long COVID lexicon from health records, Wang et al. highlighted the most common symptoms in data from clinical records  (17) , which can differ from Reddit data, especially in terms of demographics and language vocabulary. The top symptoms they identified were pain (43. 1%), anxiety (25. 8%), depression (24. 0%), fatigue (23. 4%), and joint pain (21. 0%). On the other hand, we worked on several Long COVID-related subreddits, and using a lexicon-based keyword extraction, the most common symptoms were fatigue (29. 4%), pain (22%), clouded consciousness (19. 1%), anxiety (17. 7%) and headaches (15. 6%). The top categories of symptoms were General (45. 5%), Neurological/Ocular (42. 9%), Mental Health/Psychological/Behavioral (35.
It could This study has also several limitations. First, text in social media discourse does not necessarily stick to language rules such as grammar and can be informal with extended use of abbreviations (e. g. , using acronyms and internet slang)  (28). It is possible that the applied text normalization steps were not exhaustive and did not take into consideration all of those exceptions. In the same context, the usage of a lexicon extracted from clinical records on our input text cannot guarantee the inclusion of all of the symptom vocabulary used in more than 20,000 Reddit posts. Rarely mentioned symptoms that were not part of the lexicon and that did not appear in the most frequent words, could have been overlooked. Furthermore, the classification of the lexicon symptoms in categories was manually done, which could have led to potential misclassifications.
1a ). In study B, the mutations L94F, V91A and T108I were observed in ≥99% of the viral population at week 50 (Extended Data Fig. 1b ). None of these mutations was present in the viral populations passaged in parallel in the absence of drug pressure. The selected mutations were  further assessed for their resistance to JNJ-1802 by inserting them into a subgenomic DENV-2/16681 reporter replicon. V91A and L94F conferred the highest level of resistance and reduced the activity of JNJ-1802 180-fold and 520-fold, respectively. T108I decreased the susceptibility to DENV-2 by 12-fold. Moreover, when the three mutations V91A, L94F and T108I were introduced together into the subgenomic DENV-2 reporter replicon, a 4,200-fold reduction in the activity of JNJ-1802 compared with wild-type (WT) DENV-2 was observed (Extended Data Fig.
Moreover, (4) the effect of JNJ-1802 was assessed in a therapeutic setting during a non-lethal (10 2 PFU) DENV-2 infection. First, the effect of JNJ-1802 on peak viral load (on day 3 after infection) was studied in mice after challenge with a high viral inoculum of DENV-2 (10 6 PFU; setting 1) (Fig. 2a ). Twice-daily (b. i. d. ) dosing with JNJ-1802 for 3 days resulted in a dose-dependent reduction in mean viral RNA (ranging from 3. 8 log 10 to 1. 0 log 10 copies per ml for the dose groups ranging from 60 to 0. 2 mg per kg per day), compared with the vehicle. Undetectable viral RNA was observed in four out of seven mice dosed b. i. d. with 60 mg per kg per day and in 13 out of 14 mice dosed b. i. d. with 20 mg per kg per day (Fig. 2b ). Once-daily (q. d. ) dosing for 3 days (Extended Data Fig.
c,d, ELISA data are presented as end-point titres by study day, which is defined as the reciprocal of the highest dilution of the serum that gives a positive signal. Samples were assayed in duplicate. Data are mean. For a, the schematic was adapted from ref. 3  , Springer Nature Limited. The dagger symbol indicates the day of euthanasia. NS1 as described above. Only the lowest dose of 0. 01 mg per kg per day resulted in exposure levels below the 3× pbaEC 90 value, which may explain why this dose lacked efficacy.
Similarly, we consider that JNJ-1802 has a high genetic barrier to resistance. Importantly, the individual drug-resistance mutations are not, or only at a very low percentage (≤0. 52%), naturally occurring in the Virus Pathogen Resource (www. viprbrc. org) database, while the combination of three JNJ-1802 resistance mutations together into one viral genome did not exist in the database. This suggests that such highly resistant mutants most likely do not occur naturally in the population. Considering that viraemia typically lasts for about 1 week, resulting in a short treatment period  39, 40  , it can be assumed that such a short time frame may not allow for the selection of drug-resistant variants. To further examine the potential of JNJ-1802, its prophylactic effect was studied in NHP models of DENV infection.
Viruses A clonal stock of the strain DENV-2/16681 was produced by transfection of in vitro transcribed RNA of plasmid pFK-DVs into Huh-7 cells 3. DENV-2/16681/eGFP, carrying enhanced green fluorescent protein (eGFP) at the amino terminus of the capsid protein, was produced through the transfection of in vitro transcribed RNA of plasmid pFK-DV-G2A into Huh-7 cells  3, 53. A total of 20 DENV strains, representing the available genotypes within the four DENV serotypes, were used in the in vitro genotype panel, from which 19 viruses were described previously in ref. 3  and one additional virus, DENV-1/45AZ5 (ref. 54 ), was added. For antiviral assays using Vero cells, in vitro resistance-selection experiments and in vivo efficacy studies in mice, the DENV-2 Rega Laboratory strain (referred to as DENV-2 RL) was used (GenBank: MW741553; provided by V.
Deubel). For in vivo studies in mice, high-titre stocks were generated by propagating the virus in C6/36 mosquito cells and subsequently concentrating, either by ultracentrifugation or tangential flow filtration using tangential flow filtration capsules (Minimate TFF; Pall Life Sciences), according to the manufacturer's protocol. Infectious virus titres (PFU per ml) were determined by performing plaque assays on baby hamster kidney cells (BHK-21, ATCC (CCL-10)). Four non-dengue flaviviruses were used: ZIKV (H/PF/2013, French Polynesia, GenBank: KJ776791), JEV (CNS769-Laos 2009, Laos, Gen-Bank: KC196115), WNV (R94224, CDC human brain 29 September 2008, Wisconsin, GenBank: MF004388) and yellow fever virus (YFV; 88-99, Bolivia, GenBank: MF004382). All other viruses, not belonging to the Flavivirus genus, are detailed in the Supplementary Methods and Supplementary Table  2.
Antiviral activity of JNJ-1802 against the DENV genotype panel (Supplementary Table  1 ) was assessed by measuring viral RNA using RT-qPCR as described previously  3, 23. RT-qPCR data were analysed using the QuantStudio 12K Flex software (v. 1. 2. 3) or SDS v. 1. 2 Applied Biosystems software. Inhibition values for antiviral molecules were plotted using KaleidaGraph plotting software (v. 4. 03, Synergy Software). The antiviral activity of JNJ-1802 against DENV-1/45AZ5 was tested in Vero cells by measuring viral RNA using RT-qPCR as described previously  56. Antiviral activity/toxicity assays of JNJ-1802 for all other viruses, not belonging to the flaviviruses, were performed using either reporter systems, cell viability, RT-qPCR or plaque reduction assays as readout, as further specified in the Supplementary Information and Supplementary Table  2.
For the viral kinetics studies and the delayed-treatment studies, a batch approach was applied to calculate the viral load AUC using the PΚ R package  65. This package estimates a mean AUC value for settings in which the animals are measured at varying timepoints within a treatment group. Within each experiment, the mean AUC value and 95% CIs were determined for each group. The AUC was calculated using the LOD (2. 6 log 10 copies per ml) as the lowest limit. In case the CI of a compound-treated group did not overlap with that of another group (vehicle or treatment), the groups were considered to be statistically different. In the delayed-treatment studies, the total viral load AUC for each of the compound-treated groups was calculated and compared with that of the vehicle-treated group. Fisher's exact tests were used to determine whether the survival rate in DENV-2-infected mice on day 25 for each compound treatment group differed significantly from that of the vehicle group.
One passage represents a one-week time span. The dotted line represents the cut off (15%) for the detection of variants compared with wild-type in the virus population. The increasing EC 50 values, as determined by microscopic evaluation of virus-induced CPE, are depicted below the graphs. c, Level of compound resistance conferred by NS4B mutations in DENV-2/16681 induced by JNJ-1802. Fold change (mean ± standard deviation) in compound resistance is calculated as the EC 50 of the compound against the mutant sub-genomic DENV-2/16681 reporter replicon divided by the EC 50 of the compound against wild-type sub-genomic DENV-2/16681 reporter replicon. Data are from at least three independent experiments. d, Natural occurrence of the NS4B mutations in clinical isolates. *The natural occurrence of the mutations was retrieved from the Virus Pathogen Resource database (www.
Acknowledgements We thank  A. T. Henze  for her scientific writing support at  Janssen Pharmaceutica NV ;  K. Vercauteren ,  T. De Marez ,  G. Herrera-Taracena ,  N. Voge  and  R. Draghia Akli  for reviewing the manuscript;  E. Peeters ,  S. De Bruyn ,  N. Verheyen ,  C. Van Hove ,  E. Coesemans ,  B. De Boeck ,  A. Beckers ,  P. Gysemberg ,  T. Loomans  and  K. Allaerts  at  Janssen Pharmaceutica NV ,  E. Maas ,  R. Pholien ,  C. De Keyzer ,  C. Vanderheydt ,  L. Bervoets  and the staff at the  Rega animal facility at KU Leuven , and  J. Fortin ,  F. Doublet  and  P. Muller  at  Janssen-Cilag  for technical assistance;  G. Kiemenyi Kayere  at  BPRC  for technical assistance; and the staff of the  BPRC Animal Science Department. This work was supported by a  Seeding Drug Discovery Award  from the  Wellcome Trust  (grant numbers  089328/Z/09  and  106327/Z/14 ) and received funding from the  Flanders Agency Innovation & Entrepreneurship (VLAIO O&O  grants  IWT 150863  and  HBC.
Data availability The genome sequence of DENV-2 RL has been deposited at GenBank (MW741553). The synthesis and chemical characterization of all compounds described in this paper is provided in the Supplementary Methods. The uncropped images of the western blots shown in Extended Data Fig. 2a ,b,i are presented in Supplementary Figs. 1 2 3. All data supporting the findings of this study are available within the Article and its Supplementary Information. Source data are provided with this paper.
Code availability A custom script  66  was used to derive the amino acid composition of each sample for all coding sequences per DENV genotype that was not specifically developed for this research but for all similar analyses. The code for the custom script is deposited as part of the pipeline VirVarSeq but is individually accessible on the Open Source software platform SourceForge (https://sourceforge. net/projects/virtools/?source=direc tory). The code for this specific variant detection script is 'codon_table. pl'. The natural occurrence of the mutations was retrieved from the Virus Pathogen Resource database (www. viprbrc. org; accessed in May 2020). Graphs and figures were generated using Microsoft PowerPoint (v. 2208, build 16. 0. 15601. 20280), GraphPad Prism (v. 9. 0. 0 and v. 7. 04) or Adobe Illustrator (2020 24.
Statistical Analysis The data were analyzed using SPSS, version 20. 0. The median lethal time (LT50) and the lethal concentration (LC50, LC90) values were calculated by probit regression. The survival curves were calculated using the Kaplan-Meier method. The differences in the survival curves were assessed using the logrank (Mantel-Cox) test and expressed as p values. Values of p < 0. 05 were considered significant.
Statistical Analysis The data were analyzed using SPSS, version 20. 0. The median lethal time (LT 50 ) and the lethal concentration (LC 50 , LC 90 ) values were calculated by probit regression. The survival curves were calculated using the Kaplan-Meier method. The differences in the survival curves were assessed using the logrank (Mantel-Cox) test and expressed as p values. Values of p < 0. 05 were considered significant.
The subjects were 20 to 58 years old, with an average age of 25. 5. They all provided informed consent to take part in this experiment. In the first part of the experiment, the subject was in a supine position as can be seen in Fig. 4  left (setup #1). The radar sensor was positioned horizontally above the chest, in a fixed position, at a distance of about 50 cm. Subjects were asked not to move or talk, and to breathe slowly. We then collected data from the two sensors for one minute (first configuration). Then we asked them to hold their breath with their lungs full and we collected data for 30 s (second configuration). And finally subjects were told to hold their breath with their lungs empty for another 30 s data collection (third configuration). Then, subjects lay in a right lateral decubitus position as can be seen in Fig.
C. Configuration details 1) ECG: For the experiment we used a 6-Lead ECG, with the 4 electrodes placed on the wrists and ankles. We collected one electrical signal for each lead. The collected data from the ECG had a sampling frequency of 2 kHz. We had three bipolar derivations: I, II, III and three augmented derivations: AVR, AVL, AVF. Each derivation can be seen as a way to look at the heart, with a certain angle. The signal obtained for each bipolar derivation corresponds to the difference in potential between two electrodes. The Lead I signal is the difference between the potentials of the right arm and left arm; Lead II is the difference between right arm and left foot and Lead III is the difference between the left arm and left foot. The augmented derivations are computed from the bipolar derivations: AV L = Lead I -Lead III 2 -AV R = Lead I + Lead II 2 AV F = Lead II + Lead III 2 (1) The obtained derivations and their corresponding angle of view can be seen in Fig.
This has an impact on the summation operation described in section V-C, adding some noise. The phase difference between the signals received by two Rx antennas spaced by λ 2 can be seen in Fig. 6. On the other hand, if we increase the distance between the radar and the subject, we become more sensitive to perturbations induced by the environment. As the radar field of view is 180 • , anything that is at the same range as the target will interfere with the heart signal we want to extract. At the output of the radar sensor, we collect the IQ data directly, as an output of the Analog to Digital Converter (ADC). To get access to that raw information, we use the radar paired with a DCA1000 board. The DCA1000 board receives raw IQ data from the radar via an LVDS interface and transmits them to the operator's computer via an Ethernet interface.
B. P, Q, R, S, and T wave detection In the processed ECG signal, we wanted to detect the P, Q, R, S and T waves. The Q and S waves are the bases of the R peak. Thus, we focused on detecting only the P, R and T waves. The wave detection occurred in two parts: the detection of all peaks in the signal and the subsequent classification of those peaks. First, for the detection part, we used a find peaks algorithm, provided by the scipy  [28]  package in Python. We considered a peak if its prominence was greater than a fifth of the standard deviation of the signal and if it lasted less than 200 ms. These parameters were found to detect all peaks of interest while minimizing the number of false detections. Then, we needed to classify these peaks into 3 classes: P-wave, R-wave and T-wave. One of the main challenges of classifying these waves is that their properties, such as peak prominence or width, can vary greatly from one individual to another.