dataesr/scientific-paragraphs-categorization huggingface dataset:
WLD, PLD and MLD recognition results In this section, we presented the experimental results of the WLD, PLD and MLD recognition methods. For the MLD recognition method, the morphological decomposition was performed using the toolkit introduced in  [44]. The decoding stage is based on a beam search in a FST, with a token passing algorithm. The EESEN speech recognition toolkit introduced in  [43]  was used for this purpose. This method is a variation of the technique explained in  [29]. In these experiments, we restricted the search space to a sub-set of hypotheses using a defined beam. Different beam values were tested  (10, 20 and 30). A beam value equal to 10 was chosen in such a way that keeps a low graph density and the best recognition performance as explained in section 2. 2. The WSLM, the PSML and the MSLM statistical language models were the standard n-gram trained using the SRILM toolkit  [45].
‫. "ﻣﻜﻨﻮﻧﻪ"‬ In order to compare the achieved results with the most recent works presented in the literature, we report in Table  9  some recent results that were obtained on the test set of the KHATT database. We notice that our proposed AHTR achieves state of the art results with a WER of 20. 83%. An additional set of experiments was carried out using the AHTID/MW database in order to validate our proposed OOV detection and recovery method. In this work, the PAW, morpheme and word statistical language models were estimated on the training ground-truth texts of the AHTID/MW database. The OOV recovrey results performed on the AHTID/MW database are summarized in Table  10 , which shows the word error rate and recovery rate using the WLD, WWSLD and the DWLD recognition methods. The expermiments are performed using the CNN-MDLSTM recognition engine.
Discussion We propose in this paper a two-step approach for the detection and recovery of OOVs to improve the dictionary coverage in Arabic handwriting recognition systems. In the literature, OOV recognition issue can be handled using different approaches. One way to deal with OOVs is to build open vocabulary systems such as sub-word-based models  [1]  [2]  [3]. Sub-word modeling methods can reach a coverage rate of nearly 100% of words belonging to the test datasets. Their performance will however depend on the language model design and most importantly on the properties of the training corpus compared to those of the test corpus. In addition, they can produce some words that do not belong to the language and consequently their recognition performance drastically drops. An alternative way to deal with OOVs is to use large static dictionaries  [19] ,  [24]  that consists in increasing the size of the working lexicon.
[81]text line images are automatically extracted from the proposed paragraph images. In this work, the experiments were conducted on all the line images scanned at 300 dpi resolution. This database consists of three subsets: training, validation and test. Table2reports some statistics on the KHATT database. The default word lexicon is composed of 18933 words corresponding to 7885 distinct PAWs and 13422 distinct morphemes (17 prefixes, 37 suffixes, and 13368 stems). Table2reports also the number of OOV words in the test and validation subset. Additional experiments were performed using the AHTID/MW handwritten text line database[81]. It is an offline handwritten database that includes 3710 line images written by 43 individuals. The handwritten texts were scanned in grayscale with the resolution of 300 dpi.
2. 2. Fig 8. 28 Fig 8. The three proposed OOV words detection methods experimental results performed on the Test set of the KHATT database: a) Detection using CNN-MDLSTM recognition engine (OOV = 11. 46%), b) Detection using CNN-MDLSTM recognition engine (OOV = 40%), c) Detection using HOG-BLSTM recognition engine (OOV = 11. 46%), d) Detection using HOG-BLSTM RECOGNITION ENGINE (OOV = 40%).
We used 887 line images for the test and 2823 for the training. It should be noted that the test set presents an OOV word rate of 12. 49%. The statistics describing AHTID/MW database are presented in Table3. in terms of number for the KHATT dataset TRAIN TEST Validation Pages 690 141 148 Lines 9475 2007 1902 Words 129 826 26 449 26 142 Characters 605 537 122 757 121 433 PAWs 246 762 49 781 49 285 Morphemes 190 757 38 617 38 493 OOV words -- 3067 2998 OOV PAWs -- 975 1 017 OOV Morphemes -- 2 957 2 426 The KHATT database is an offline handwritten text image database that includes 4000 paragraphs written by 1000 distinct authors. The text images are scanned at multiple resolutions.
Search strategy and selection criteria This study was conducted according to the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) statement. 11 We searched for studies reporting AF detection rates by ICM in patients with history of IS or transient ischemic attack (TIA). A literature search in MEDLINE, SCOPUS, and the Cochrane Central Register of Controlled Trials (CENTRAL) was performed, using the following terms in combination: "cardiac monitoring," "implantable loop recorder," "insertable loop recorder," "implantable cardiac monitor," "cryptogenic stroke," "embolic stroke of undetermined source," "ischemic stroke," "cerebral ischemia," "atrial fibrillation," and "atrial flutter. " The complete algorithm used in the MEDLINE database search is available in the online Supplementary Methods.
However, we retained publications providing data for distinct IS groups, including CS and embolic stroke of undetermined source (ESUS), despite the possibility of overlapping group data (ESUS overlapping with CS, and CS/ESUS overlapping with unselected IS/TIA). Reference lists of all articles that met the inclusion criteria, and of relevant review articles, were examined to identify studies that may have been missed by the initial database search. All retrieved studies were scanned independently by two reviewers (G. T. and A. H. K. ). In case of disagreement regarding the literature search results between the two coauthors, the remaining coauthors were consulted, and disagreement was ultimately resolved with consensus. We used the Newcastle-Ottawa Scale to assess the quality of included studies that were published at the time of the literature search, and to identify potential sources of bias amongst eligible studies.
Finally, for all the aforementioned meta-regression and subgroup analyses, we performed additional sensitivity analyses after excluding studies that were presented in conferences and had only abstracts publicly available at the time of the literature search. Data extraction was performed by two independent authors (A. H. K. and L. P. ), and in cases of disagreement, a senior author (G. T. ) was consulted. For all proportion analyses we used the variance-stabilizing double arcsine transformation. 14 Pooled estimates in both the overall and subgroup analyses were calculated using the Hartung-Knapp-Sidik-Jonkman method. 15 Meta-regression analyses were performed under the random-effects model (method of moments). Variables with a threshold of P<0. 1 in the initial univariate meta-regression analyses were used as covariates for multivariate meta-regression models.
All extraction negative controls were free of amplification products. Amplification products from putatively positive individuals were not sequenced, as the presence of Y. pestis was subsequently assessed through whole-genome capture and highthroughput Illumina sequencing. In addition, shotgun Next Generation Sequencing (NGS) data from individuals unearthed at the New Museums site (Augustinian Friary) in Cambridge (n = 26) were screened for Y. pestis with the MEGAN alignment tool (MALT)  18  (see Methods). The output was post-processed within the pathogen screening pipeline HOPS  19. The assessment of shotgun NGS reads produced from non-uracil-DNA-glycosylase (non-UDG) libraries revealed the potential presence of Y. pestis DNA in four individuals (Supplementary Table  2 , Supplementary Fig.
Of those, 24 isolates showed at least 50% of the reference genome covered at 5-fold (Table  1 ), which allowed for their confident inclusion in phylogenetic analysis. In addition, we nearly tripled the genomic coverage of the published "549_O" isolate from Ellwangen, Germany (now reaching 14. 1-fold), which was previously processed by arraybased capture using a different probe design  9  (Supplementary Table  3 ). Y. pestis phylogenetic reconstruction. To infer genetic relationships between the new and previously published Y. pestis isolates, we constructed phylogenies using the maximum likelihood (ML) method, allowing for up to 3% missing data (97% partial deletion) to accommodate lower coverage genomes. As a reference dataset, we used 233 modern isolates  [23] [24] [25] [26] [27]  (as listed in ref.
13 ). This phenomenon likely arises from enrichment of non-target DNA stemming from closely related organisms, an issue frequently encountered in ancient metagenomic datasets  18, 29, 33. Moreover, these genomes had notably longer branch lengths in comparison to other contemporaneous isolates from the same archaeological contexts (Supplementary Fig. 14 ). Their assessment using the recently developed SNPEvaluation tool  28  (see Methods) classified their private SNP calls as false-positive, suggesting that the observed branch lengths are erroneous (Supplementary Data 2). Similarly, the previously published SLC1006 and BSS31 genomes  8  were also excluded from further analyses as they also showed high heterozygosity (Supplementary Fig. 15 ) and exceedingly longer branch lengths compared to other 14th-century Y.
Notably, these two clades show dissimilar rates of substitution accumulation. For example, the mean SNP distance between the Ellwangen genome  22), despite an assumption of them being contemporaneous (early 17th century AD) based on archaeological dating (Fig. 2 ; Supplementary Table  1 ; Supplementary Note 1). Analysis of substitution rate variation in Y. pestis. We used the Bayesian framework BEAST v1. 8 in order to make an assessment of substitution rate variations across the genealogy of Branch 1 (n = 80), retaining high-quality second pandemic Y. pestis genomes and using available calibration points in our modern and ancient datasets (Supplementary Data 6). Previous studies have demonstrated that overdispersion among Y. pestis branch lengths is unlikely a result of natural selection, and have rather suggested a link between rate acceleration and geographic expansion of certain lineages during epidemic spread  16, 23.
Our analysis based on the coalescent skyline model (Fig. 3 , Supplementary Fig. 19 ) suggests an over 40-fold difference between the fastest and slowest substitution rates identified on Branch 1 (Fig. 3 ). In particular, we observe the fastest rates in three internal branches (Fig. 3 ). The first spans the genetic distance between the strains from Ellwangen (549_O) and London (BED), and supports the conflicting branch lengths of BED and BRA strains described Fig. 3  Substitution rate variation across the Y. pestis Branch 1. The figure presents a maximum clade credibility (MCC) phylogenetic tree generated using BEAST v1. 8  85  (rooted with 2. MED KIM10-outgroup not shown). The tree was viewed in FigTree v1. 4 (http://tree. bio. ed. ac. uk/software/figtree/), and modified so that branch colours represent mean substitution rates (substitutions per site per year).
The tree depicts the substitution rate variation across Branch 1 of the Y. pestis phylogeny, which ranges from 2. 09E-7 (highest-red) to 4. 95E-9 (lowest-blue) substitutions per site per year (see rate key). The isolates used for this analysis overlap with the ones used for the SNP and maximum likelihood phylogenetic analysis (see Supplementary Fig. 12 ), with the exception of the TRP002 and OSL1 genomes since their private SNP calls are likely affected by environmental contamination and other representative genomes exist in our dataset from the BD time period (1346-1353 AD). Labels of genomes associated with the second and third plague pandemics appear in bold. The mean substitution rate across the tree (including 2. MED KIM10) was calculated to 2. 85E-8 substitutions per site per year. Lengths of branches are scaled to represent sample ages, and the depicted Branch 1 sequences are estimated to represent 731 years (95% HPD: 672-823) of Y.
pestis chromosome. Published genomes from the Bronze Age period  31, 32  (RISE509 and RT5), from the first pandemic 29 (6th-century Altenerding 2148), from modern-day isolates (0. PE2, 0. PE4 and 1. ORI)  23  , as well as Y. pseudotuberculosis IP32953  60  , are also shown for comparative purposes. The colour scale ranges from 0 (not covered-yellow) to 1 (entirely coveredblue) according to the relative proportion of gene/locus covered. The heatmap was plotted in R version 3. 4. 1  82  using the ggplot2 package  89. Boxes marked with "X" indicate genomic loci that were not part of the Y. pestis probe design when the respective isolates were captured  9, 29. Refer to Supplementary Fig. 20  for presence/absence of virulence-associated genes across the pMT1, pPCP1 and pCD1 plasmids. b Chromosomal coverage plots made with the Circos 90 software.
Specifically for Y. pestis, mgtB disruption has been associated with a decreased ability for macrophage invasion resulting in its attenuated virulence in mice  40. Both mgtB and mgtC are present in all 233 modern Y. pestis genomes used in our comparative dataset. We explored these gene deletions in greater detail using BWA-MEM and identified them as part of a 49-kb missing region within the BED and OBS genomes (1,879,467-1,928,869 on CO92) (Fig. 4b , Supplementary Fig. 21 ) flanked by an IS100 element immediately following its downstream end, which is consistent with previously characterised disruptions or losses of Y. pestis genomic regions via insertion elements  41. Apart from mgtB and mgtC, this region encompasses a set of 34 additional genes that code for both characterised and hypothetical proteins, most of which seem to be associated with phenotypic characteristics that appear inactivated in Y.
Discussion A series of studies have sufficiently demonstrated the preservation of Y. pestis in ancient human remains from a wide temporal transect  [8] [9] [10] 14, 22, 29, 31, 32, 43. This study presen s an extensive sampling of multiple European epidemic burials from the period between the 14th and 17th centuries in order to gain a more complete picture of Y. pestis' genetic history during the second plague pandemic. Here, we nearly triple the amount of genomic data available from that time period (Fig. 1 , Table  1  and Supplementary Table  3 ) and integration with existing datasets reveals key aspects regarding the initiation and progression of the second plague pandemic in Europe. Based on historical sources alone, it has been difficult to determine the time at which Y. pestis first reached different parts of western Russia  7.
Nevertheless, the phylogenetic positioning of the BED and OBS genomes within the "post-BD lineage" and in relation to other second pandemic isolates suggests they arose within Europe or its vicinity. We identified a 49-kb deletion within both BED and OBS genomes (Fig. 4b ), which caused the loss of two virulenceassociated genes, mgtB and mgtC (Fig. 4a ). This deletion could not be identified in other second pandemic or modern strains in our dataset (Supplementary Fig. 21 ). The inferred virulence potential of mgtB and mgtC genes is associated with intracellular survival of Y. pestis within macrophages  40, 54. Their co-expression has been shown to affect the virulence exerted by other pathogenic enterobacteria under laboratory conditions  55, 56  and both genes have been proposed as potential drug targets  40, 57.
Indexing PCR reactions were set up as follows: 50 µl of DNA library, 1× PCR buffer, 2. 5 mM MgCl 2 , 1 mg/ml BSA, 0. 2 µM in PE 1. 0, 0. 2 mM dNTP each, 0. 1 U/µl HGS Taq Diamond and 0. 2 µM indexing primer, with the following cycling conditions: 5 min at 94 °C, followed by 18 cycles of 30 s each at 94 °C, 60  °C and 68 °C, with a final extension of 7 min at 72 °C. Amplified products were purified using the MinElute kit (Qiagen) and DNA was eluted in 35 μl EB. The indexed libraries were then quantified using the Quant-iT™ PicoGreen® dsDNA kit (P7589, Invitrogen™ Life Technologies) on the Synergy™ HT Multi-Mode Microplate Reader with Gen5™ software. Subsequent shotgun sequencing of these libraries was carried out on an Illumina NextSeq500 platform (using the High-Output kit 1 × 75 cycle chemistry) at the University of Cambridge Biochemistry DNA Sequencing Facility.
The program MALT (version 0. 4. 0)  18 , integrated in the pathogen screening pipeline HOPS  19  , was used to assess the presence of Y. pestis DNA in the NMS specimens. A custom NCBI RefSeq (November 2017) database was used for running MALT, including all bacterial and viral assemblies marked as complete, a selection of eukaryotic pathogen genomes, as well as the human reference sequence (GRCh38). Genomes with keywords such as "unknown" were removed. A total of 15,361 genomes were retained in the database. Pre-processed shotgun NGS reads (. fastq) were used as input and the parameters were set as follows: 85 for the minimum percentage identity (-minPercentIdentity), 1 for the minimum support (-minSupport), using a top percentage value of 1 (-topPercent), a semi-global alignment mode, and with all remaining parameters set to default.
1, -l 32) for genome reconstruction and mean coverage calculation and more lenient parameters (-n 0. 01, -l 32) for inspection of regions surrounding potential variants. Reads with mapping quality lower than 37 (-q) were removed using SAMtools (http://samtools. sourceforge. net/), and PCR duplicates were removed using the MarkDuplicates tool (http://broadinstitute. github. io/ picard/). Prior to SNP identification, raw pre-processed reads from partially-UDGtreated libraries were trimmed for 2-bp at both ends to remove sites that could be affected by aDNA damage and, subsequently, were re-filtered for length and remapped using stringent parameters. SNP calling and phylogenetic analysis. SNP calling was performed using the UnifiedGenotyper of the Genome Analysis Toolkit (GATK v3. 5)  75. Our newly reconstructed genomes were analysed alongside previously published Y.
pestis genomes, which included a modern-day dataset of 233 genomes  [23] [24] [25] [26] [27] 48  (as listed in ref. 28  ), three Bronze Age strains  31  , a 2nd-to 3rd-century AD isolate from the Tian Shan mountains in Kyrgyzstan  30  , one Justinianic strain (Altenerding 2148)  29  , 15 previously published historical genomes from the second plague pandemic  [8] [9] [10] 14  and a Y. pseudotuberculosis strain (IP32953)  60  that was used as outgroup for rooting the phylogeny. A vcf file was produced for every genome using the "EMIT_ALL_SITES" option, which generated a call for every position present in the reference genome. Fu thermore, we used the custom java tool MultiVCFAnalyzer v0. 85 (ref. 33  ) (https://github. com/alexherbig/MultiVCFAnalyzer) to produce a SNP table of variant positions across all genomes analysed, using the following parameters: for homozygous alleles, a SNP would be called when covered at least 3-fold with a minimum genotyping quality of 30, and for heterozygous alleles, a variant would be called when 90% of reads would support it.
In cases where none of the parameters would be met, an "N" would be inserted in the respective genomic position. In addition, we omitted previously defined noncore regions, as well as annotated repetitive elements, homoplasies, tRNAs, rRNAs and tmRNAs from our SNP analysis  16, 23. In the present dataset, a total of 7,510 variant positions were identified. Su sequently, the annotation as well as the effect of each SNP was determined through the program SnpEff v3. 1i (ref. 76  ). We used a SNP alignment produced by MultiVCFAnalyzer v0. 85 to construct phylogenetic trees using the ML and maximum parsimony (MP) methods. Up to 3% missing data were included in the analysis (97% partial deletion), resulting in a total number of 6,058 SNPs used for phylogenetic reconstruction. The MP phylogeny was produced in MEGA7 (ref.
77 ) in order to make a first assessment of genome topologies. The ML phylogenies were constructed with the program RAxML (version 8. 2. 9) 78 using the Generalised Time Reversible (GTR)  79  model with four gamma rate categories and 1000 bootstrap replicates to assess tree topology support. Reanalysis of recently published non-UDG Y. pestis genomes. A recent study described the phylogenetic positioning and SNP analysis of five 14th century Y. pestis genomes 8. As these genomes were non-UDG treated, they were reanalysed here using different criteria compared to other second pandemic and modern genomes in our dataset. Read pre-processing and merging was done as described in the above section "Sequencing and read processing". In addition, read mapping against the CO92 reference genome (NC_003143.
1) was performed using more lenient parameters in BWA  80  (-n 0. 01, -l 16) than the ones previously reported  8  , to account for ancient DNA deamination within mapping reads. In our view, the usage of strict BWA mapping parameters for non-UDG data (i. e. -n 0. 1) could potentially introduce a reference bias to the analysis, which could in turn affect SNP discovery and phylogenetic inferences. PCR duplicates were removed from all five datasets using MarkDuplicates (http://broadinstitute. github. io/picard/) and reads were filtered for mapping quality (q 37) using SAMtools (http://samtools. sourceforge. net/). The obtained mean coverage for each of the five re-analysed genomes was: 3. 4-fold for BSS31 (27. 8% covered 5-fold), 6. 7-fold for SLC1006 (59. 1% covered 5-fold), 30. 5-fold for OSL-1 (91.
7% covered 5-fold), 38. 1-fold for Ber37 (95. 2% covered 5-fold) and 46. 1-fold for Ber45 (94. 1% covered 5-fold). In addition, the obtained average fragment lengths for the five re-analysed genomes were as follows: 52. 2 bp for BSS31, 71. 5 bp for SLC1006, 108 bp for OSL-1, 61. 9 bp for Ber37 and 69. 7 bp for Ber45. Before SNP calling, MapDamage2. 0 (ref. 81 ) was used to rescale base qualities, primarily on the extremities of mapped reads, to account for sites that could potentially be affected by aDNA deamination. Subsequently, SNPs were called using GATK and the resulting vcf files were comparatively assessed in MultiVCFAnalyzer v0. 85 (ref. 33 ) to compile a SNP table including all genomes in the dataset as described in the above section "SNP calling and phylogenetic analysis". Qualitative SNP assessment in UDG-treated data using SNPEvaluation.
A frequent challenge faced when using ancient metagenomic datasets to reconstruct bacterial genomes is a strong environmental signal that can interfere with SNP assignments, especially in low-coverage data  29. Such an effect can interfere with phylogenetic analyses by creating artificial branch lengths, which can in turn affect evolutionary inferences. As such, in order to avoid erroneous SNP assignments, we qualitatively evaluated all private SNP calls for the newly reconstructed genomes that were used for phylogenetic analysis in this study (minimum 50% of the genome covered 5-fold (Table  1 )). We used the recently developed SNPEvaluation tool (https://github. com/andreasKroepelin/ SNP_Evaluation) to compare the SNP profiles that arise for each dataset under both stringent (BWA parameters -n 0.