dataesr/scientific-paragraphs-categorization huggingface dataset:
Databases used in this study and bioinformatic analysis Nucleotide and amino acid sequences of putative and confirmed PETases were acquired from databases integrated into the NCBI (https://www. ncbi. nlm. nih. gov/), UniProt (http://www. uniprot. org/) and IMG (JGI, http://jgi. doe. gov/) servers  [58] [59] [60]. Human gut sequences were retrieved from the Unified Human Gastrointestinal Protein (UHGP) catalog (PMID:32690973). Sequences were compared to others deposited in the NCBI databases using BLAST alignment tools  61. Amino acid sequence HMM search was carried out using the HMMER (http://hmmer. org) webpage or a local version of the software (v3. 1b2)  62  with downloaded datasets. Structural information on the enzymes was retrieved from the RCSB-PDB  63  database. Sequence data were processed and analyzed using ChromasPro 2.
1. 8 (Technelysium, Brisbaine Australia) or SnapGene (GSL Biotech LLC, San Diego CA, USA). Amino acid alignment was constructed using structural alignments with T-Coffee  64  and was further visualized with Bioedit  65. The model structures of bacteroidetal PETases were modeled with the Robetta server 37 using the IsPETase crystal structure (6QGC) as a backbone. A phylogenetic tree was constructed using the RAxML-NG autoMRE algorithm  39  with the treesapp create command implemented in TreeSAPP  38  with maximum bootstraps set at 1000. RAxML-NG has recently to return the best scoring tree for highest number of datasets when compared against other fast maximum likelihood (ML) methods  39  , allowing a large number of maximum bootstraps to be used to produce as conservative a tree as possible.
Sequences were assigned NCBI lineages according to source organisms listed in TABLE  1  and TABLE  S1 , and colors were assigned to the tree at the phylum level using the treesapp colour command. This reference tree was also built for and used with TreeSAPPbased to analyses of marine metagenomic and metatranscriptomic data sets to gain an understanding of Bacteriodetal PETase potential role in the environment. The UHPLC profiles were plotted and edited using MATLAB version R2020b. Scanning IMG/M was done on 19/November/2020 for PET30 and on 14/January/2021 for PET27. Geo locations were used as provided whenever available. In case no Geo location was available, whenever possible, information about isolation source/location/city/country were used to look up Geo coordinates on GeoHack (https://geohack.
toolforge. org). The map representing the frequency and geographical distribution of PET hydrolases in metagenomes (FIGURE  5 ) was constructed using QGis Desktop 2. 18. 5 (http://www. qgis. org). BARM Redoxcline metagenomic sequence assemblies from samples collected on October 26, 2014 at TF0271 sampling station were downloaded from EMBL-EBI under the study accession number PRJEB34883  42. The corresponding metagenomic reads were obtained from EMBL-EBI under the study accession number PRJEB22997  43. Four of eight samples from depths of 100 m, 120 m, 140 m, and 200 m (samples P2236_102, P2236_104, P2236_106, and P2236_108) were chosen from the BARM Redoxcline dataset as they most closely mirrored the Saanich Inlet data also used for environmental analysis described below. For the transcriptome analyses of the Baltic Sea, water samples from the central Baltic Sea were taken during cruise EMB107 of the German R/V "Elisabeth Mann Borgese" in July 2015 with an automatic flow injection sampler (AFIS) attached to a conductivity-temperature-depth (CTD)-rosette.
After updating the headers for resulting 219 bins with the sample IDs, the medium plus quality (completeness >50%, contamination <10%) bins were concatenated together into one FASTA file to use with TreeSAPP  38. For metagenomic and metatranscriptomic analyses of the BARM and Saanich redoxcline data sets, TreeSAPP (v11. 0)  38 was used to create a PETase reference package (FIGURE  4 ) from 29 PETase protein sequences (TABLE 1, TABLE  S1 ) in FASTA format. The reference package includes a profile HMM, a multiple sequence alignment, a phylogenetic tree (described above) and a taxonomy table. For validation, the original 29 sequences were assigned back to the tree to visualize reference package construction in iTOL  78  with treesapp assign using the -trim_align option. To phylogenetically place PETase homologs recovered from the entire SI072 metagenome and the selected BARM redoxcline metagenome, the concatenated FASTA of contig assemblies from all depths for each data set was mapped to the reference tree using TreeSAPP (v11.
0)  38 using the treesapp assign command with the -trim_align flag. Metagenomic contigs were then assigned separately by depth alongside their corresponding metagenomic and/or transcriptomic reads in FASTQ format using treesapp assign with the -abundance option to calculate corresponding transcript abundance (transcripts per million, TPM) for metagenomic hits at each depth. SI072 medium plus quality MAG sequences were placed in the reference tree using the same methodology as for the contigs described above to better understand and interpret phylogenetic placements from Saanich Inlet. All trees were then visualized in iTOL  78. It is important to note TreeSAPP abundance calculations as visualized in iTOL maps read abundance at the tips of the reference tree. If a sequence maps closer to the root, that abundance gets split evenly among the children in that cluster, and abundances shown represent a combination of reads that mapped to all nodes feeding into that particular leaf.
Heterologous expression of putative PET esterase genes in Escherichia coli BL21 (DE3) The putative PETases were extracted from metagenomic datasets (Table  1 ), therefore the gene sequences were optimized for expression in E. coli and synthesized into pET21a(+) vector at Biomatik (Wilmington, USA). The obtained constructs were sequenced at Eurofins (Ebersberg, Germeny) and checked for correctness by comparing to the original sequences. Chemical competent E. coli T7-Shuffle or E. coli BL21(DE3) were used for heterologous expression of possible PETases. IsPETase gene in pMAL-p4x was provided by Sebastian Weigert (University of Bayreuth, Germany) and purified by maltose-binding Tag. The cultures were grown aerobically in auto-induction medium (ZYM-5052)  79  containing 100 µg/ml ampicillin for pET21a(+) at 37 °C until they reached an OD600 of 1.
9 μm diameter. Isocratic elution was performed using a mobile phase consisting of 20:80 (v/v) acetonitrile and water (acidified with 0. 1% vol trifluoroacetic acid) at a flowrate of 0. 4 ml min -1. UHPLC samples were prepared by mixing 50 µl of incubation supernatant with 200 µl acetonitrile (acidified with 1% vol trifluoroacetic acid), followed by centrifugation at 10,000 × g for 3 minutes and transferring 200 µl of the supernatant into 600 µl water. 15 µl of sample were injected per measurement and detection was performed at 254 nm with a VWD-3400 detector from Thermo Scientific (Waltham, MA, USA). Quantification of peak areas was performed using data analysis software supplied with the Compass HyStar software package from Bruker (Billerica, MA, USA).
ACKNOWLEDGEMENTS This work was in part supported by the  BMBF  within the programs MarBiotech ( 031B0562A ) MetagenLig ( 031B0571B ),  MethanoPEP  ( 031B0851B) LipoBiocat (031B0837B ) and  PlastiSea (031B867B) MetagenLig  ( 031B0571A ) at the  Universities of Hamburg, Kiel and Stuttgart. Additional support was derived from the  University of Bayreuth  funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project Number  391977956 -SFB 1357 , the  US Department of Energy (DOE) Joint Genome Institute , an  Office of Science User Facility , supported by the  Office of Science of the U. S. Department of Energy  under Contract  DE-AC02-05CH11231 , the  Natural Sciences and Engineering Research Council (NSERC) of Canada , the G. Unger Vetlesen  and  Ambrose Monell Foundations , the  Canada Foundation for Innovation (CFI)  and  Compute Canada  through grants awarded to S.
Materials and methods In this single-center retrospective cohort study, we included all consecutive patients with histologically confirmed primary CNS DLBCL who were diagnosed between 2007 and 2015 and categorized according to the 2017 World Health Organization (WHO) criteria 1 by a specialized hematopathologist (MP). Patients with systemic disease prior to or synchronous with the PCNSL (n = 73), with insufficient data for formally excluding systemic disease (n = 46), or with insufficient histologic material (n = 16) were excluded, leading to a final cohort of 25 patients. The clinicopathological characteristics of this cohort have been previously published  7  : the Memorial Sloan Kettering Cancer Center (MSKCC) score was used as a prognostic model  2. According to this model, patients ≤ 50 years of age (group A) have the best prognosis, followed by patients older than 50 years with a KPS of ≥ 70 (group B), and finally, patients older than 50 years with a KPS of < 70 (group C) show the worst prognosis  2.
The intensity of p62 staining was recorded as negative, weak, moderate, or strong  16. The percentage of cells positive for LC3B and M6PR was recorded from 0 to 100% and presented as the H score (percentage of positive cells multiplied by the intensity). Staining for p62 was uniform across all the cells of the same tumor, and both a four-tiered system (negative, weak, moderate, and strong) and a two-tiered system (negative/mild/moderate vs. strong  16  ) were used for statistical analyses. We further combined the immunoexpressions of LC3B and p62 into basal autophagy characterized by low LC3B and low or high p62, activated autophagy showing high LC3B and low p62, and activated but then blocked autophagy, showing high LC3B and high p62 expression  5, 15. Data were analyzed using the StatView software (Abacus Concepts, Berkley, California): we used the x 2 test to explore the relationship between two groups (categorical data), factorial analysis of variance (ANOVA) to explore the relationship between a factor (categorical data) and a continuous parameter, and simple regression analysis between two continuous parameters.
, 2017). It is important to note that in normalized case, the heart rate is between 60-120 beats/min, which is related to the scale value of 35-75. The appropriate measurement range is chosen for the heart rate and the selected scale, where Wavelet coefficients have the largest low energy, as shown in Figure  6. For heart rate, the approximate scale has a value of 42 (continuous pink line in Figure  6 ), which corresponds to a frequency of 1. 801 Hz in Figure  7. The heart rate can be calculated as 1. 801 × 60 108. 08 bpm. In the same way, the range of respiration rate lies between 10-30 bpm, which corresponds to a scale value of 150-450  (Chia et al. , 2005; Wang et al. , 2017). The respiration rate scale can be selected by measuring the maximum spectral energy within these ranges. The generated scale value associated with the loss-free data is 350 as shown in Figure  8.
The 350 scale value corresponds to a frequency of 0. 2161 Hz in Supplementary Figure  S1. Therefore, the heart rate can be calculated as 12. 96 (0. 2161*60) bpm. There are two readings, obtained from uninterrupted signal, which are: 1. Respiration rate 12. 96 bpm. 2. Heart rate 108. 08 bpm. Hence, this information would work as a benchmark for a particular patient. In this work, we have used ECG sensor; which is current gold standard in monitoring heart rate  (Nelson and Allen, 2019) , for comparison of the results. The respiration was monitored manually to find the actual breathing rate of the human. So far, we have considered the normal situation wherein complete data is received from patients under investigation for analysis purposes. In the unique approach used in this article, we have generated simulation results for comparison in this section.
CONCLUSION In this paper, vital signs monitoring has been considered for practically achieved data. Due to its direct link with human beings, the importance of vital signs is very clear. The loss of such information needs to be efficiently handled in order to avoid catastrophic results. Initially, the vital signs are monitored from normal data, where there is no data loss. This provides a benchmark to perform comparison for later stages where measurement data is intermittent in nature after striking the patient. This lossy data has been observed in vital sign monitoring (heart and respiration rates) with degrading outcomes. This data loss is then attempted to be recovered through the AR-based model which gives much better results compared to lossy results. However, the AR-based model entertained only the measurement vector in the recovery process.
Following the consensus-building procedure of a modified Delphi method (figure  1 ), we conducted the following steps: 1. We contacted all corresponding authors of the identified publications and asked them to evaluate and give feedback on the initial APOSTEL recommendations. The participants were asked about their agreement on each item of the recommendations, rating from 1 (full disagreement) to 4 (full approval). Participants were given the opportunity to provide comments. In a blinded fashion, we collected feedback and suggestions using a free online survey via Google Forms (initial questionnaire; raw data of survey results can be obtained from the corresponding author upon qualified request). 2. We then formed a panel of 54 international experts who gathered at congress meetings and during 4 rounds of telephone conferences.
Acknowledgment The authors thank the contributors for taking part in the surveys:  Solmaz Abdolrahimzadeh  ( University of Rome Sapienza, Italy );  Fahmy Aboulenein-Djamshidian  ( Department of Neurology, Sozialmedizinisches Zentrum Ost, Donauspital, Vienna, Austria );  Kaveh Abri Aghdam  ( Eye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran );  Asaf Achiron  ( Wolfson Medical Center Tel Aviv University, Israel );  Lillian Aly  ( Department of Neurology, TU Munich, Germany );  Sofia Androudi  ( Department of Ophthalmology, University of Thessaly, Larissa, Greece );  J. Fernando Arevalo  ( Wilmer Eye Institute at Johns Hopkins University, Baltimore, MD );  Jennifer Arnold  ( Marsden Eye Specialists, Parramatta, Australia );  Nicole Balducci  ( Policlinico Sant'Orsola-Malpighi Bologna , Italy);  Francesco Bandello  ( San Raffaele Scientific Institute, Milan, Italy );  Rajani Battu (Centre for Eye Genetics and Research, Bangalore, India) ;  Raed Behbehani  ( Al-Bahar Ophthalmology Center, Kuwait );  Ivan Bodis-Wollner  ( SUNY Downstate Medical Center, New York, NY );  Enrico Borrelli  ( San Raffaele Scientific Institute, Milan, Italy );  Livia Brandao  ( University of Basel, Switzerland );  Josefine Britze  ( Department of Neurology, Rigshospitalet Glostrup, Copenhagen, Denmark );  Troels Brynskov  ( Department of Ophthalmology, Glostrup Hospital, Copenhagen, Denmark );  Pilar Calvo  ( IIS-Aragon, Miguel Servet University Hospital, Zaragoza, Spain );  Adriana Carrá  ( MS Center Hospital Británico, Buenos Aires, Argentina );  Alfonso Casado  ( Hospital Universitario Marqués de Valdecilla, Santander, Spain );  Irini Chatziralli  (2nd  Department of Ophthalmology, University of Athens, Greece );  Fred K.
Íñigo Gabilondo  reports grants from "Ikerbasque: The  Basque Foundation for Science" and Carlos III Health Institute (Ministry of Science and Innovation of Spain)  and personal fees and nonfinancial support from  Zambon Group  and  Alter Medica. Jennifer S. Graves ,  Ari J. Green , and  Hans-Peter Hartung  report no conflicts of interest. Joachim Havla  reports grants for OCT research from  Friedrich-Baur-Stiftung  and  Merck , personal fees and nonfinancial support from  Celgene ,  Merck ,  Alexion ,  Novartis ,  Roche ,  Santhera ,  Biogen ,  Heidelberg Engineering , and  Sanofi Genzyme , and nonfinancial support from the  Guthy-Jackson Charitable Foundation , all outside the submitted work; and is partially funded by the  German Federal Ministry of Education and Research  (grant numbers  01ZZ1603  [A-D] and  01ZZ1804  [A-H] [DIFUTURE]).
Axel Petzold  is supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust  and  UCL Institute of Ophthalmology  and is part of the steering committee of the  ANGI network , which is sponsored by Zeiss; steering committee of the OCTiMS study, which is sponsored by  Novartis ; and reports speaker fees from  Heidelberg-Engineering. Gorm Pihl-Jensen ,  Jana Lizrova Preiningerova , and  Gema Rebolleda  report no conflicts of interest. Marius Ringelstein  received speaker honoraria from  Novartis ,  Bayer ,  Roche ,  Alexion , and  Ipsen  and travel reimbursement from  Bayer Schering, Biogen Idec ,  Merz ,  Genzyme ,  Teva ,  Grifols ,  Roche , and  Merck , none related to this study. Shiv Saidha  has received consulting fees from  Medical Logix  for the development of CME programs in neurology and has served on scientific advisory boards for  Biogen, Genzyme ,  Genentech Corporation, EMD Serono , and  Celgene ; is the PI of investigator-initiated studies funded by  Genentech Corporation and Biogen Idec ; received support from the  Race  to Erase MS foundation; was the site investigator of a trial sponsored by  MedDay Pharmaceuticals ; and has received equity compensation for consulting from  JuneBrain LLC , a retinal imaging device developer.
Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain,  Finland, Norway, and Brazil)  for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years.
Objectives In this study, we aim to assess whether asthma hospitalizations could be predicted by GT along with data from previous hospital admissions. To do so, we aim (1) to assess and discuss GT for common cold in different countries of the world, (2) to correlate GT for common cold and hospitalization data, and (3) to build models forecasting asthma hospitalizations for a period of 1 year (based on GT and past hospitalization data), correlating observed and predicted asthma hospitalizations.
Queries and Data Sources for GT for Rhinovirus-Related Search Terms GT topics are groups of search terms that concern the same concept, irrespective of their language  [9, [12] [13] [14]. This is particularly relevant, as there are countries with several words and idioms referring to the common cold (a paradigmatic example is Spain, a country with five official and co-official languages) as well as others with ambiguous words referring to the common cold (eg, English-speaking countries, in which cold has a double meaning). Assessing data from several countries, we found that common cold was listed as a pseudo-influenza syndrome topic (this topic was renamed as common cold at the time of manuscript submission). We then retrieved country-level GT on rhinovirus-related search terms from January 1, 2012, to December 17, 2016, in Portugal, Spain, Finland, Norway, and Brazil.
The countries were selected according to the possibility of having nationwide weekly asthma hospitalization data available for comparison. To provide a wider perspective, we plotted GT patterns for the pseudo-influenza syndrome topic, not only in these 5 countries, in which asthma hospitalizations were also assessed, but also in 11 additional countries. The assessed time frame was selected considering both GT and asthma hospitalization data. On the one hand, for periods longer than 5 years, GT data are presented on a monthly level rather than on a weekly level (with monthly intervals being insufficiently sensitive for assessing variations in asthma hospitalizations). On the other hand, a complete period of 5 years was not assessed (with the last 2 weeks of December 2016 not being assessed) on account of available asthma hospitalization data (see the subsection Asthma Hospitalization Data Sources).
In Spain, identical relative search volumes are also observed, whether or not the misspelled word resfrio is included in search term combinations. In Norway, the misspelled variants forkjolelse and forkjoling generate negligible results (ie, all observations with a relative search volume of zero). State-level analyses were also performed in Spain and Brazil. For Spain, we performed separate analyses for the three most populous autonomous communities (Andalusia, Catalonia, and Madrid). For Brazil, we separately analyzed data from the most populous state in three of the five Brazilian geographical regions (as defined by the Brazilian Institute of Geography and Statistics): São Paulo (Southeast), Rio Grande do Sul (South), and Bahia (Northeast). For the two other geographical regions (North and Central-West), the most populous states had low-quality GT data (with many weeks recorded as 0).
Asthma Hospitalization Data Sources In the 5 studied countries, we assessed all hospitalizations with asthma as the main diagnosis (ie, International Classification of Diseases, Ninth Revision, Clinical Modification code 493. x or International Classification of Diseases, Tenth Revision, code J45) occurring in public hospitals from January 1, 2012, to December 17, 2016 (the last 2 weeks of 2016 were not included, as we did not have any information on 2017 Portuguese and Brazilian discharges, and many patients admitted at the end of 2016 were discharged in 2017). Hospitalization data were retrieved from (1) the Hospital Morbidity database (provided by the Portuguese Central Administration of the Healthcare System) for Portugal, (2) the Hospital Morbidity Survey databases (Encuesta de morbilidad hospitalaria, Instituto Nacional de Estadistica) for Spain, (3) the National Hospital Discharge Register (Hoitoilmoitusrekisteri, HILMO) for Finland, (4) the Norwegian Patient Registry (Norsk Pasientregister) for Norway, and (5) DATASUS data from the Single Health System (Sistema Único de Saúde) for Brazil.
Statistical Analysis In brief, we performed two major types of analyses. First, we assessed the correlations between GT data and asthma hospitalizations in each country after applying time series analysis methods. Subsequently, we built models forecasting asthma hospitalizations for a period of 1 year based on GT and hospitalization data from the previous 3 years. To test for the predictive ability of the models, forecasted and observed asthma admissions were compared. Both GT and hospitalization data were presented weekly. The performance of analysis on a weekly basis allowed for the detection of short-term variations without the large random fluctuations that can be observed when data are analyzed on a daily basis. In detail, we calculated Pearson correlation coefficients to assess the correlation between GT data and asthma hospitalizations in each country.