dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with Existing Tools We first compared strain characterization by our new SNP-IT tool with those of KvarQ (https://github. com/kvarq/ kvarq), TB-Profiler (http://tbdr. lshtm. ac. uk), and Mykrobe predictor (http://www. mykrobe. com/products/predictor) on default settings and then after integrating our updated SNP library. To enable our new data to be integrated with published SNP libraries  (23)  and for practical reasons when modifying existing tools, we created a minimal SNP dataset. We filtered our larger SNP catalog for synonymous SNPs that occurred in coding regions (as annotated by SnpEff version 4. 3  [24] ) and selected 1 representative SNP for each subspecies, lineage, and sublineage at random. We then modified the existing software packages to include reference SNPs (or kmers for Mykrobe predictor) for the subspecies, lineages, or sublineages that they initially failed to identify.
Comparison with Existing Software Compared with SNP-IT for the clinical set of isolates, Mykrobe predictor reported M. orygis as M. tuberculosis West African lineage and M. pinnipedii as M. microti. KvarQ identified all animal-associated subspecies only as "animal lineage. " TB-Profiler was unable to delineate among animal subspecies, which were all reported as M. bovis/M. tuberculosis West African lineage. After we modified the KvarQ, TB-Profiler, and Mykrobe predictor databases with our minimal SNP catalog, all systems agreed on the identity of all the MTBC isolates in the clinical set. SNP-IT was unable to identify 10 samples because <10% of type-specific SNPs were present in these strains. This result was because our pipeline made no call at the lineage informative sites because of the presence of a minor allele, most likely the result of contamination or a mixture of 2 strains in the sample.
However, TB-Profiler and Mykrobe predictor were both able to identify 2 of these isolates as mixed Beijing lineage/M. orygis using our new minimal SNP dataset. by conventional laboratory methods. The SNP databases of Stucki, Coll, and Comas are currently used as the knowledge base for KvarQ, TB-Profiler, and Mykrobe predictor  (18, 23, 26). None of these databases, however, provides adequate resolution for the animal subspecies. In contrast, SNP-IT was able to assign subspecies, lineages, and sublineages to all samples in the validation set with 100% accuracy compared with maximum-likelihood phylogeny. Implementing this fine-resolution algorithm into a routine diagnostic workflow would be a major step toward understanding the epidemiology and pathogenicity of the less common members of MTBC.
Discussion By applying SNP-IT to a clinical dataset, we discovered an unexpectedly high number of animal subspecies among MTBC isolates, particularly M. orygis, from humans in the United Kingdom. This recently described member of the complex has a host range that includes waterbucks, gazelles, rhesus monkeys, cows, and rhinoceri  (2, 28, 29). Several human cases have been described in patients in the Netherlands of South/Southeast Asian origin (2), but no cases have been described in the United Kingdom. Human-to-animal transmission has been described in 1 case in New Zealand  (30). Given that zoonotic TB is associated with higher rates of extrapulmonary disease and may be less likely to grow in culture  (31, 32) , retrospective interrogation of WGS libraries, as in this study, is likely to underestimate the true burden of disease.
Acknowledgments We thank  Phillip Fowler  his work improving the SNP-IT Python package and  Pretin Davda  for his assistance in linking isolates to patient level. This research was supported by the  National Institute for Health Research  ( NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford  in partnership with Public Health England (PHE) and by  Oxford NIHR Biomedical Research Centre. T. P. is a NIHR senior investigator. The report presents independent research funded by  NIHR. The views expressed in this publication are those of the authors and not necessarily those the  National Health Service, NIHR , the  Department of Health , or PHE. Z. I. is a  Sir Henry Dale Fellow jointly  funded by the  Wellcome Trust  and the  Royal Society  (grant no.
Statistical analysis Linear regression analyses were performed with the JMP 13. 2. 0 software. Each petiole was analyzed separately. We did not analyze the fluorescence quantification data set as a whole (pooling cells from distinct petioles) because the virus-associated fluorescence intensity is not directly comparable. The total viral load varies in between distinct petioles because of a 'natural' variance across infected plants and because some were harvested at different dpi. The auto-fluorescence of the infected tissues also varied across petioles because of the time of infection, the total viral load and presumably other unknown factors. The proportion of cells containing both S and R segments, both S segment and M-Rep protein, or both R segment and M-Rep protein (Figure  3C , Supplementary file 1: Table  S1 ) was analyzed with Generalized linear models (GLM) with 'treatment' (S segment -R segment/S segment -M-Rep/ R segment -M-Rep) as a categorical explanatory factor, and a quasi-binomial error type.
Acknowledgments We are grateful to  Sophie Leblaye  for greenhouse work, plant production and virus inoculation. We also thank  T Timchenko ,  B Gronenborn  and  J Vetten  for kindly providing the antibody directed against the viral protein M-Rep. This work was supported by  INRA dpt. SPE  and by the grant ' Nano '  N ˚ANR-14-CE02-0014  from the  French national funding agency (ANR). MP, CU and SG acknowledge support from  CIRAD. YM acknowledges support from  IRD  and  CNRS.
Data availability All data are available in the manuscript and in Supplementary files. Raw data of all quantified green and red fluorescence within individual cells of infected plants are provided as a separate Excel supplementary file: Table  S4. To allow repeat/reproduce all correlation tests, the 508 raw/unprocessed images (. lsm format) used for preparing all figures and for fluorescence quantification in individual cells have been deposited in the public repository figshare. They can be accessed at the DOI: 10. 6084/m9. figshare. 5981968 The following dataset was generated:.
Yet, while the array-based genome-wide DNA methylation studies of BC are already quite numerous, NGS-based studies are scarce (see Discussion for details). Taking into account some advantages of the sequencing data compared with the array datasets, we have developed a rapid and affordable genome-wide bisulfite DNA sequencing protocol, XmaI-reduced representation bisulfite sequencing (XmaI-RRBS)  [19, 20] , and applied it to a significant collection of BC tissues and cells with the aim to provide a pilot BC methylotype classification based on the genome-wide CpG islands bisulfite sequencing data.
Histologically, normal tissues from cancer patients were found to cluster with normal cancer-free tissues, independent of cancer status of the individual. An analysis of matched sets of lung cancers and adjacent normal lung tissue taken at the time of resection has demonstrated that normal cadaveric lung samples studied by Byun et al. cluster with the normal 'live' lung tissue data from patients with lung cancer  [25]. In our study, we have selected six autopsy samples of normal breast tissues taken from the women without reported chronic disease, including cancers, who died of acute reasons. Morphological examination of the autopsy samples has ruled out the signs of BC. More importantly, this sampling of six autopsy normal breast tissues has previously been evaluated by us for the concordance of the genome-wide DNA methylation profiles with the normal breast samplings used in RRBS experiments within the ENCODE project  [26] , GEO Series GSE27584 and by Abba et al.
Sequencing & alignment XmaI-RRBS libraries were sequenced on the Ion Personal Genome Machine (PGM) using the Ion PGM 200 Sequencing Kit and Ion 318 Chip (Thermo Fisher Scientific). It is of utmost importance to perform all procedures with ISPs from enrichment and till the chip installation into the PGM slot in a box preheated to 37 • C in order to prevent mutual annealing of the ISPs. The sequence data processing, quality control and alignment of the XmaI-RRBS reads were performed exactly as described previously  [19].
Statistical analyses & bioinformatics procedures Cluster analysis of the epigenome-wide data was performed using unsupervised hierarchical clustering. To perform the cluster analysis, we first selected 105,469 CpGs that in total possessed no more than 20% missing data in the whole collection of 116 biological samples included in this study. Of these, we then selected high Shannon entropy CpGs using R package 'entropy' with 3. 5 threshold. We have chosen Shannon entropy metrics in order to identify the top CpGs varying in the methylation status across the whole collection of samples under analysis, because it provides a more uniform distribution of CpGs β-values variability in the sample collection than selection of differentially methylated CpGs by β-values standard deviation. The resulting 41,474 high entropy CpGs entered unsupervised hierarchical clustering with normalized pairwise Manhattan distance and ward.
D2 agglomeration method  [30]. No preliminary selection of a limited subset of the top CpGs found differentially methylated between breast tumors and normal breast samples has been made; normal samples were included in the complete dataset undergoing unsupervised hierarchical clustering. We then used the pvclust package in R to define statistically significant tumor clusters indicating patterns of potential biological relevance. Differentially methylated CpGs discriminating BC epigenomic subtypes were selected with Mann-Whitney U-test. Differentially methylated genes (DMGs) were defined as those containing differentially methylated CpGs in a window of ±1000 base pairs around the transcription start site of the gene. Enrichment of the DMGs sets with specific biological processes and molecular functions was assessed with the Gene Ontology (GO) Consortium Enrichment analysis tool  [31, 32].
Principal epigenomic subtypes (superclusters) of breast tissues & cells defined by genome-wide CpG islands methylation patterns We have obtained the XmaI-RRBS results for 105 BC samples, 5 BC cell lines and 6 normal breast samples (116 samples in total). The samples were processed as described in the 'Materials & methods' section, and the average of 118 Mb sequences with 500,000-2,000,000 reads per sample was obtained. Using the XmaI-RRBS ingenious workflow we achieved the highest currently possible efficiency of bisulfite-converted DNA sequences mapping against the reference genome, reaching 68%. Raw datasets have been submitted to The National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database, accession number GSE122799. Unsupervised cluster analysis of the XmaI-RRBS results distinguishes two principal epigenomic subtypes of BC, highly and moderately methylated over the CpG islands, respectively (Figure  1  & Table  2 ).
We have identified novel molecular subtypes of BC differing in the patterns of DNA methylation at the 1421 promoter CpG islands selected for evaluation from our XmaI-RRBS-derived dataset on the basis of containing high entropy CpG dinucleotides. To our knowledge, to date seven studies identifying molecular genetic subtypes of BC based on the results of more or less genome wide DNA methylation assays have been published. The first was carried out with the use of the Illumina GoldenGate (Methylation Cancer Panel 1) on 28 BC samples. The authors have evaluated methylation of CpG dinucleotides belonging to 806 cancer related genes; for each of these genes methylation was studied for two CpG dinucleotides on average  [3]. Three clusters of samples have been identified, corresponding to TNBC, HER2+ and luminal A tumors, respectively.
futuremedicine. com were discarded as well. Finally, the effective number of CpG dinucleotides that entered cluster analysis shrinked to N = 574  [46]. Cluster analysis of the methylation data obtained for thus selected 574 CpG dinucleotides in a large collection of 466 BC samples has revealed 5 groups of BC samples. One of the groups has incorporated near to all TNBC samples; the samples of other expression subtypes are exclusively rare in this cluster. The other four groups do not demonstrate any tendency to accumulate samples of a particular expression subtype. The number of HER2+ samples was insufficient to trace their tropism to one of the methylotypes, if any  [46]. In 2014, Stefansson et al. published a genome wide methylation study performed on 40 BC samples and 17 normal breast tissue samples with the Infinium 450 K arrays.
Selected sized fragments were removed from Pippin elution wells and used directly in qPCR library quantification (Kapa Biosystems) and Illumina Sequencing on a NextSEq. 500 to obtain single-ended, dual-indexed reads of 75 bp. Libraries were sequenced in the National Center for Natural Products Research, University of Mississippi. Raw reads were downloaded from Basespace using basespacerundownloader (Illumina) and demultiplexed using BCL2FASTQ (Illumina) allowing up to 2-bp errors in barcodes as our barcodes had 3-bp degeneracy built in. After demultiplexing, each sample was trimmed from the beginning of each read to remove remaining internal (adapter) barcode and restriction site overhang. All samples were then trimmed to 58 bp in length to prevent downstream difficulties in assignment of homology.
Loci were identified within individuals and aligned among individuals using pyRAD  (62). Population Genetic Analysis. We used the SNP data with the Bayesian coalescent program G-PhoCS (Generalized Phylogenetic Coalescent Sampler)  (26)  to estimate rates of migration between the 2 populations. Six independent, simultaneous runs were conducted, each with a 500,000 Markov chain Monte Carlo iterations, which were then compiled for a total of 3,000,000 iterations. We used a 10% burn-in independently for each run, which resulted in 2,700,000 iterations from which summary statistics were calculated. We defined a constant mutation rate in order to conduct these analyses as a constant mutation rate would be a null assumption when lacking data, suggesting fluctuating mutation rates over evolutionary history  (26).
Bidirectional migration rates between the 2 populations were then calculated in G-PhoCS. We examined population structure between the 2 populations using snmf function in the LEA R package  (27). We tested from 1 up to 10 populations and ran 1,000,000 iterations. Avoidance of Novel Phenotypes. Plasticine clay model experiments are commonly used to assess predator responses to novel aposematic signals (e. g. , refs. 29 and 63-65). These allow observation of predation attempts and provide insight into selection pressures on various phenotypes. We constructed 45-mm-long (snout to vent) replica frogs using Van Aken polymer modeling clay  (29, 66) , which does not harden and retains evidence of predation attempts. We poured melted clay into silicone molds made from plastic model replicas  (67).
S3  for a schematic on how the learning and generalization experiments were conducted. Learning Experiment Data Analysis. We analyzed the data from the learning experiments using a GLMM in 2 ways. First, we used a survival analysis (Cox regression) to examine the effect of color and chloroquine content (5% vs. 10%), and the interaction between the 2, on the latency to attack (i. e. , time to event) each mealworm. Chick ID was included in the model as a random factor to account for repeated presentations to the same individual. Then, for each chick, we counted the number of trials in which the mealworm was attacked, to a maximum of 10, and whether or not the chicks learned to avoid the signal they were presented (as defined by 3 consecutive refusals). In the first case, we used a Poisson error distribution, whereas in the second we used a binomial distribution.
Each oat (1 at a time) was presented on a hatch that had a visual barrier, which allowed us to detect the exact moment at which the oat was seen, which set the actual beginning of the trials. We measured the number of times the bird wiped its beak, which is a known aversive behavior  (89) , and the percentage of the oat eaten. Birds were watched for a 2-min period after they finished eating the oats, or for a maximum of 5 min in those instances in which the oat was not fully eaten, to make sure that any delayed response to the oat taste would not be missed. Data were analyzed using GLMM using the package lme4 (84). We entered frog population as the predicting variable, while number of times the beak was wiped and percentage of oat eaten were entered as the response variables. Because the response of each bird was measured twice, we included bird ID as a random factor.
Given that the duration was not the same for all trials, we used the function "offset" in R to account only for the effect of population on our response variable once the effect of duration was controlled for. These and all other statistical analyses were done in R (83) using the RStudio interface  (90) , unless stated otherwise. Ethics Statement. Chick and blue tit experiments were conducted under University of Mississippi Institutional Animal Care and Use Committee protocols 14-025 and 14-026 and the Central Finland Centre for Economic Development, Transport, and Environment and license from the National Animal Experiment Board (ESAVI/9114/04. 10. 07/2014), and the Central Finland Regional Environment Centre (VARELY/294/2015), respectively. Data Availability. Data supporting the findings reported in this study are available from the data repository of the University of Jyväskylä (DOI: 10.
ACKNOWLEDGMENTS. We thank  Tim Muhich ,  John Constan ,  Antoine Baglan , and  Mathilde Segers  for their invaluable help in creating and placing model frogs in French Guiana;  Ombeline Vrignaud  for providing valuable information and assistance and securing permission for work within the Grand Matoury Natural Reserve;  John Endler ,  Emily Burdfield-Steel ,  Janne Valkonen , and  Carita Lindstedt  for providing helpful feedback on an earlier version;  Helinä Nisu  at the  Konnevesi Research Station  for help with the blue tit experiment; and  Sara Calhim  and  J. Valkonen  for invaluable statistical advice. This work was supported in part by  Investissement d'Avenir Grant   CEBA ANR-10-LABX-25-01  of the  Agence Nationale de la Recherche ; the  American Society of Ichthyologists and Herpetologists' Gaige Award  (to J.
P. L. ); and a  Society for the Study of Amphibians and Reptiles' grant in Herpetology  (to J. P. L. ). B. R. and J. M. were funded by the  Finnish Centre of Excellence in Biological Interactions  (Project  28466 , to J. M. ). B. R. also acknowledges funding from the  Academy of Finland  ( Academy Research Fellowship , project No. 21000042021 ).
In the real atmosphere stratospheric temperatures increase towards the pole  (Flasar et al. , 2004, Fig. 1a ), so zonal velocity goes down with height. But this is likely due to physical processes that are not included in our model (e. g. gravity wave breaking or joule heating). Here stratospheric temperatures decrease towards the pole as only solar heating is included as a physical parametrisation in the stratosphere, and so we expect zonal velocity to increase with height according to the thermal wind equation (and not necessarily so near the equator, where that balance breaks down). While  Flasar et al. (2004)  do not see a retrograde equatorial jet in the lower stratosphere,  Read et al. (2006)  do using the same dataset (although they used a simpler version of the thermal wind equation and applied this balance closer to the equator).
Acknowledgments Support for RMBY and PLR provided by  UK STFC  Grants  ST/F003145/1 ,  ST/I001948/1 , and  ST/K00106X/1. Support for PLR provided by  UK STFC  Grant  ST/N00082X/1. Part of this work was completed during a visit to the  Kavli Institute for Theoretical Physics at UC Santa Barbara. This work was supported in part by the  US National Science Foundation  under Grant No. NSF   PHY-1125915. The authors would like to thank  Terry Davies ,  Nathan Mayne ,  William Ingram ,  Hiro Yamazaki , and  Lena Zuchowski  for assistance with an earlier version of the model,  Pat Irwin  and  João Mendonça  for help with the new radiation scheme,  Liam Brannigan ,  Yuan Lian ,  David Munday  and  Inna Polichtchouk  for assistance running the MITgcm,  Jeremy Yates  for his patience while we ran the simulations,  Michael McIntyre ,  Aymeric Spiga , and  Stephen Thomson  for useful discussions, and two anonymous reviewers whose extensive comments improved the paper significantly.
Methods The amber specimen hosting the association (AMNH DR-NJIT001) originates from La Cumbre, Dominican Republic and is deposited at the Division of Invertebrate Zoology at the American Museum of Natural History. The amber has been trimmed and polished with a water-fed trim saw and Buehler EcoMet™ 30 grinder to improve imaging and reveal additional detail. Lower magnification (1-2x) extendedfocus photomicrographs were taken with a Nikon SMZ-18S through NIS Element D software. Higher magnification pictures were captured with a Nikon Eclipse TS2 compound microscope after immersion in glycerol. Reflectance images of springtails were captured with a Leica SP8 confocal scope (NJIT) and processed using the LAS X software. For systematics, we follow Derhaveng  [93]  for more inclusive ranks and Bretfeld  [47]  for subordinal classification.
Introduction: as the life expectancy and weight of patients are increasing, more old and obese patients are undergoing total knee arthroplasty (TKA). TKA may lead to several perioperative complications. These include anesthesia-related risks, exacerbation of comorbid medical issues and complications of surgical procedure. We have no studies reporting medical complications following TKA among our population. This study aimed to evaluate perioperative complications of TKA and to identify the related risk factors. Methods: it was a monocentric retrospective including 410 observations in the local TKA registry. Data of patients operated for primary unilateral TKA during the period from January 2014 to December 2017 were reviewed. All patients had standardized protocols of anesthesia and post operative care for three days following surgery.
Methods Type of study: it was a monocentric descriptive, analytic and retrospective study conducted in a referral center of orthopedic surgery (Department of critical care and anaesthesiology of Kassab Orthopaedic Institute, Tunisia) during the period from January 2014 to December 2017. We had enrolled all the patients of this period since we had standardized protocols of anesthesia and post operative care for at least three days following surgery. TKA register is the first database in Tunisia which is recording all the medical details of total knee replacement done in Kassab Orthopaedic Institute. Ethical approval was granted from local ethics committee, number CE 001/2014. Patients: we included all the patients undergoing TKA for osteoarthritis (idiopathic or posttraumatic), inflammatory arthritis (rheumatoid, psoriatic, etc), or congenital deformities.