dataesr/scientific-paragraphs-categorization huggingface dataset:
9765, for a specificity 0. 99; interolog: minimum score of 500 out of 1000). Next, the dataset was reduced to a canonical representation, only retaining the human protein isoform appearing in highest-scoring interactions and removing nonreviewed human proteins. For the predicted interactions, the human counterpart was programmatically mapped to KEGG pathways in order to identify the associated functional modules. Data analysis and graphic representations were obtained using Python libraries Pandas, Seaborn, Matplotlib, Matplotlib_venn, and Cytoscape software  (Shannon, 2003).
We employed a machine learning-based method (intersppi), which relies on protein sequences and network properties, to identify patterns of classification into groups of interacting or non-interacting protein pairs  (Lian et al. , 2019). We also used a homologybased prediction of interacting pairs (interolog) that relies on the mapping of similarities to interaction databases and interaction transfers among homologs  (Folador et al. , 2014). Our results revealed a considerable difference between the methods in the number and nature of the interactions predicted. This difference was mainly due to the fact that the interolog methodology depends on data availability in interaction databases, such as STRING and INTACT, from which homology mapping is performed  (Wang et al. , 2012). The data may therefore have been biased by experimental work focused on specific aspects and organisms, as well as the conservation of proteins, which may affect homology identification.
ACKNOWLEDGMENTS This work benefited from Q15 the facilities and expertise of the MRic-TEM platform (https://microscopie. univ-rennes1. fr). The authors are grateful to  Agnes Burel  ( University of Rennes ,  BIOSIT -UMS 3480 ,  US_S 018 ,  Rennes, France)  and  Chantal Cauty  for sessions with the microscope. The authors would like to thank  Ludovica Marinelli  ( INRAE, AgroParisTech, Paris-Saclay University, Micalis Institute, Jouy-en-Josas, France ) for her technical advice on the NF-κB reporter cell line. The authors would also like to thank  Victoria Hawken  for English-language editing of the manuscript. The authors thank  CNIEL  ( Centre National Interprofessionnel de l'Economie Laitière ) for providing the ITG P20 strain.
FUNDING This work has received a financial support from the  INRAE (Rennes, France)  and  Institut Agro  (Rennes, France). VR and BL were supported by the  International Cooperation Program CAPES/COFECUB  at the  Federal University of Minas Gerais  funded by  CAPES -the Brazilian Federal Agency for the Support and Evaluation of Graduate Education of the Brazilian Ministry of Education  (number  99999. 000058/2017-03and 88887. 179897/2018-03 ,respectively).
Data processing Among the 74 and 16 genetically identified F. gigantica and Fasciola intermediates, respectively, we selected the less damaged specimens that constituted the best samples for the morphometric studies: 17 F. gigantica and 10 Fasciola intermediate form specimens (from this study). In addition, nine F. hepatica (from Liverpool School of Tropical Medicine, UK) were included in the material. Four types of morphometric data were separately analyzed: (i) log-transformed linear measurements (LN), (ii) log-shape ratios (LSR), (iii) outlines data (OD), and (iv) landmarks data (LD). For the artificial neural network-based classification (ANN, see below), we also tested the combination of LN with OD.
Software ImageJ image analysis software was used to measure linear dimensions. Landmarks and pseudolandmarks (outlines) were digitized using the CLIC package (https://www. xyom-clic. eu). Subsequent analyses and graphical outputs were performed using both the CLIC package and the recently available online XYOM software (https://xyom. io/) (Dujardin & Dujardin, 2019). The latter allowed some specific analyses to be performed, including the maximum likelihood (MxL) and the ANN methods of classification (see below).
Landmarks data (LD) The following anatomical landmarks (LM) were digitized: LM1 = left-side of the cephalic cone, LM2 = left-side of the oral sucker, LM3 = right-side of the oral sucker, LM4 = rightside of the cephalic cone, and LM5 = end of the testis (see Fig. 1B ). The five landmarks were subjected to a generalized Procrustes analysis, and the resulting aligned individuals were orthogonally projected onto the Euclidean space tangent to the consensus of forms Full-size DOI: 10. 7717/peerj. 8597/fig-1 (Rohlf & Slice, 1990). The principal components (PC) of these data (called ''Procrustes residuals'', ''tangent space variables'', or ''orthogonal projections'') were referred to here as ''landmarks data'' (LD) and used as the input for validated classification. The influence of size on interspecific discrimination by LD was estimated by regressing the discriminant factors on the centroid size.
We used the multilayer perceptron program written in JavaScript, which is available at https://www. npmjs. com/package/mlp. Following a process of trial-and-error, a single (hidden) layer composed of three neurons provided the best results, irrespective of the type of input data. We used as the input the total number of variables instead of a subset of their PCs. Inputs comprised the morphometric variables (LN, LSR, OD, or LD, see above), while the final outputs were the probabilities of inclusion in each group (in a Bayesian sense). Forms were then classified to the species for which their probability of inclusion was the greatest  (Soda, Slice & Naylor, 2017). The ''weights'', i. e. , the coefficients allowing the outputs to be computed, were applied to the individuals being tested to compute categories and suggest a classification.
Overall response rates (defined as achieving a complete or partial response) at the final assessment were 50. 0% (15/30) for ISAV cases and 50. 7% (35/69) for controls. All-cause mortality was 43. 3% (13/30) in ISAV cases as compared to 46. 4% (32/69) in controls  (Figure). Table. Demographic and clinical characteristics of 30 isavuconazole cases and 69 control cases Methods. Patients from 2016-2019 with either 1) at least one positive BC for Candida species or 2) positive T2C assay and negative or no paired BC were identified and retrospectively reviewed. Patients with additional positive BC or T2C within 60 days were excluded from the analysis. Data collected included risk factors for candidemia, causative Candida species, and whether DFE was performed after diagnosis. Patients with evidence of OC by exam were compared by type of ocular involvement (chorioretinitis vs.
STRIVE (NCT02734862) is a global, randomized, double-blind, placebo-controlled, Phase 2 trial evaluating safety and efficacy of IV rezafungin once weekly (QWk) for treatment of candidemia and/or invasive candidiasis compared with standard-of-care (IV caspofungin once daily with optional oral stepdown). Here we report pharmacokinetic (PK) data from the completed STRIVE trial analyzed by patient demographics at baseline.
Interpretation of the Results The MICs alone and in combination were determined as the lowest concentrations that caused a complete inhibition as measured by a 90% of inhibition compared to the control according to spectrophotometric reading. For the calculation of the MIC 50 , the MIC 90 , and the geometric mean of isavuconazole, the MICs of all three sets were pooled together. For the calculation of the fractional inhibition concentration index (FICI), high off-scale MICs were converted to the next log2 dilutions. The FICI data were interpreted in the following way: FICI ≤ 0. 5 = synergy, FICI > 0. 5-4 = no interaction, and FICI > 4. 0 = antagonism.
5281/zenodo. 4046235). Genotyping also identified relationships between primary facial and internal metastatic tumours within individual hosts (S4 Table ). Overall, these studies trace the progression of DFT1 over two decades, revealing geographical routes of expansion through Tasmania, patterns of coinfection and metastasis, and the dynamics and persistence of parallel DFT1 strains. )). Each tumour is represented by a coloured symbol. Mapping coordinates have been randomly offset in heavily sampled labelled locations in order to aid visualisation. Sample density reflects trapping effort, not tumour prevalence. Tumour clade data and geographical coordinates are available in S1 Table. Map outline was obtained from https://library. unimelb. edu. au/collections/map_collection/map_collection_outline_maps.
Furthermore, these changes were specific to DFT1, as they were not observed in cultured devil fibroblasts or in DFT2 cell lines (S2 Data in https://doi. org/10. 5281/zenodo. 4046235). To determine whether the cell line-specific genomic changes were compatible with growth within an animal host, we examined nine tumours derived from sequenced cell lines that had been experimentally introduced into devils  [26]. In all of these cases, the cell line genomic configuration was retained in transplanted tumours  (Fig 3E). We annotated the genes present within genomic regions recurrently gained and lost in DFT1 cell lines to identify potential underlying drivers. Surprisingly, ERBB2, whose activity is necessary for DFT1 growth  [18, 27] , lies on the arm of chromosome 4 whose third copy is repeatedly lost in cell lines (Fig 3D ), indicating that the selective advantage of undergoing this change exceeds the cost of losing a copy of ERBB2.
Chromosome 5p, which is repeatedly gained in cell lines, carries the ribosomal DNA (rDNA) locus  [28] , raising the possibility that increased ribosome abundance may offer an advantage to DFT1 cells in vitro  (Fig 3D). SNV allele assignment revealed that the chromosomal copies implicated in the events on chromosomes 4 and 5 varied among tumours, indicating that the selective advantage is not allele specific (S10 Fig and  S11  Table ). Intriguingly, we identified two DFT1 biopsies, one a   ). Copy number plots for all tumours are available in S2 Data in https://doi. org/10. 5281/zenodo. 4046235. CN, copy number; CNV, copy number variant; DFT1, devil facial tumour 1; M5, marker 5; logR, ratio of tumour to normal reads, log base 2; rDNA, ribosomal DNA; SNV, single nucleotide variant. https://doi.
Sample collection and DNA extraction Biopsies were collected from wild or captive Tasmanian devils or from animals whose carcasses were found as roadkill. Tumour and host (ear, liver, spleen, blood, gonad, or submandibular lymph node) tissue biopsies were collected into either RNAlater (Thermo Fisher Scientific, Waltham, United States of America) or ethanol. Fine needle aspirates were collected into a 1:1 solution of Buffer AL (Qiagen, Hilden, Germany): PBS were sampled from tumours that were too small to biopsy. Cell lines were established and cultured as previously described  [13, 17, 18]. Experiments involving inoculation of devils with DFT1 cell lines have been described  [26]. Genomic DNA was extracted using the Qiagen DNeasy Blood and Tissue Kit (Qiagen). Samples included in the study, as well as metadata for identifying samples used in various analyses, are listed in S1 Table.
Sequencing Illumina whole genome sequencing libraries were generated with an average insert size of 450 base pairs (bp) using standard methods according to manufacturer's instructions, and were tagged with Sanger index tags for multiplexing. We pooled and sequenced approximately 17 samples per lane on an Illumina HiSeq instrument with version 4 chemistry (Illumina, San Diego, USA) in high output mode with paired-ends (2 × 125 bp) to achieve a median coverage of 1× per sample. Reads were aligned to the Devil7. 1+MT Tasmanian devil reference genome (http://www. ensembl. org/Sarcophilus_harrisii/Info/Index) [9]  using BWA-MEM (http://biobwa. sourceforge. net/bwa. shtml) version 0. 5. 9-r16+rugo with options "-l 32 -t 6. " PCR duplicates were flagged using Picard (http://broadinstitute. github.
We demultiplexed and trimmed adaptors using bcl2fastq2 v2. 20 (https://support. illumina. com/downloads/bcl2fastqconversion-software-v2-20. html), allowing for no mismatches ("-barcode-mismatches 0"). We then aligned the reads using BWA-MEM (http://bio-bwa. sourceforge. net/bwa. shtml) to Devil7. 1+MT. Using a customised script based on Allelecount (https://github. com/cancerit/ alleleCount) with min-base-qual = 30, we identified the total number of reads covering the region of the SNV, the number of reads supporting each allele, and the number of reads carrying a 5-bp unique molecular identifier (UMI). We calculated the on-target ratios of each probe by calculating the number of unique reads that mapped to the correct genomic region as a proportion of the total number of reads that carried either or both the extension and ligation sequences of each probe.
We repooled the remaining MIP probes and genotyped across the samples (S2 Table ). Several tumours were sequenced in duplicate, and in these cases, data were merged from the two sequencing runs. We assessed the number of reads supporting the reference and variant alleles at each target site as detailed above (S2 Table ). We then genotyped variant presence or absence as follows: Calculate the expected number of reads for presence: The expected number of reads supporting a variant for a diploid site carrying a heterozygous somatic variant was calculated as follows: Expected reads ¼ Total reads � purity � 0:5 Purity was defined as the median VAF � 2 for variants that pass a minimum threshold of variant read support. The accuracy of this approach was validated by visualising VAF histograms for each sample.
In these cases, breakpoints were consolidated to the median position of observed breakpoints. Finally, segments <500 kb were discarded. Each tumour's segments were visually inspected and validated (S2 Data in https://doi. org/10. 5281/zenodo. 4046235). Breakpoints were screened for absence in the set of normal devil genomes (S3 Data in https://doi. org/10. 5281/zenodo. 4046235). Copy number assignment. The copy number associated with each segment was estimated using two models: a Bayesian hierarchical model implemented in JAGS (http://www. stats. ox. ac. uk/~nicholls/MScMCMC15/jags_user_manual. pdf) and a Poisson logit-linked general linear model implemented in R. Bayesian hierarchical copy number assignment. The read depth of a segment (r s ) can be estimated by the sample's ploidy (ψ) and purity (ρ), defined as the proportion of tumour cells in a sample, as well as the segment's copy number state (Eq 1)  [53].
We developed a Bayesian hierarchical model with a Markov chain Monte Carlo-based Gibbs sampler implemented in JAGS (http://www. stats. ox. ac. uk/~nicholls/MScMCMC15/jags_user_manual. pdf) to fit the read depths of segments in each tumour and to obtain estimates of a tumour's purity and ploidy and calculate copy number states. For DFT1 tumours, we estimated purity through DFT1-specific regions of deletions in the genome and MIPs data (see above). We used the average of these tumour purity measurements to initialise the tumour purity parameter of the model. Rearranging the formula in Eq 1, we then used the optimised tumour purity and ploidy estimates and the read depths of the segments to assign copy number to the segments (Eq 2). r s ¼ log 2 2ð1 À rÞ þ rðCNÞ c � � ð1Þ CN ¼ ðc2 r s À 2ð1 À rÞÞ r ð2Þ Poisson model for copy number assignment.
Using the Poisson mixture model, we then assigned each segment to the copy number state that had the highest probability (Eq 4). r n ¼ kCN2 nr 2 ð3Þ PðnÞ ¼ e ðk logðr n ÞÀ r n À lgammaðkþ1ÞÞ ð4Þ Discrepancies between copy number assignments using the two methods due to low sequencing quality and subclonality were manually validated based on raw read coverage. To further remove false positive breakpoints, adjacent segments that were assigned the same copy number state were merged. Copy number states in all tumours were visually inspected and validated (S2 Data in https://doi. org/10. 5281/zenodo. 4046235) and mtDNA and MIP phylogenetic trees were used to identify and split recurrent CNVs and to infer back mutations. Whole genome duplication was inferred using karyotype information (S1 Table ), together with observations of CNV copy number states.
Phylogenetic tree The phylogenetic tree of DFT1 tumours was inferred jointly from three sources of sequence data: SNVs of the nuclear genome derived from MIP sequencing, mtDNA SNVs and binary presence-absence information corresponding to the CNVs observed in each sample. CNVs associated with M5 and high copy number amplicons were excluded from tree inference. The data were analysed using IQ-TREE 1. 6. 12 [54] , under an edge-linked proportional partition model (-spp option)  [55] , in which each of the three data sources formed a subset of the partition, to which was assigned its own substitution model. The substitution model for each subset was chosen using IQ-TREE's model selection procedure (-m MF option)  [56] , with the following results: MIP SNVs were analysed using K2P+ASC+R2, mtDNA SNVs were analysed using TIM2e+ASC, and CNVs were analysed using GTR2+FO+ASC+R3.
CNV rate and genome size variation rate The accumulation of CNVs with time was plotted using the R package ggplot2. Data were analysed using a linear model; concretely, the lm() function and prediction intervals were inferred by the predict() function implemented in the R programming language. Models were inferred for the relationship between the number of CNVs and the sampling date and for the number of CNVs and the interaction between the sampling date and the DFT1 clade. The significance of the interaction with clade was assessed using analysis of variance (ANOVA), through the Anova() function in the R package "car," and contrasts between pairs of clades were assessed using the emtrends() function in the R package estimated marginal means ("emmeans"). Sets of CNVs which cooccur with the same copy number in the same samples are counted once (see Total_CNVs_linked.
8-μM dNTP mix (New England Biolabs), 0. 8-μM forward primer, 0. 8-μM reverse primer, and 2. 5-U Phusion HF DNA Polymerase (New England Biolabs). Thermal cycling was performed as follows: 3:30 minutes at 94˚C, then 30 cycles of 30 seconds at 94˚C, 30 seconds at 60˚C, 30 seconds at 72˚C, followed by 5 minutes at 72˚C using an Eppendorf thermal cycler (Eppendorf, Hamburg, Germany). Genotypes were called manually by viewing electropherograms using Geneious R9 software (Biomatters, Auckland, New Zealand).
Telomere repeat abundance analysis Telomere repeat abundance was estimated from binary sequencing alignment map (BAM) files using a modified version of telseq version 0. 0. 1  [60] , using merged read groups and a read length of 125, appropriate for the sequencing technology used (-m -r 125 option). We modified parameters in the telseq source code to adapt it for the devil genome, which include read length, total GC content (bp), and number of chromosomal ends. The number of chromosomal ends was specified as 1 in order to obtain a measure of overall telomere repeat abundance rather than an estimate of telomere length. The modified source code is available at https://github. com/TransmissibleCancerGroup/telseq. The relationship between the estimated telomere length and the year of sampling was estimated using a generalised linear model, using a log-linked gamma distribution to model errors and to reflect that the length estimates can only take positive values.
The implementation used was the glm() function in the R programming language. "Telomere length" estimated by telseq is the cumulative length of telomere repeats and will include interstitial telomere repeats. with the same copy number in the same samples are counted once, and we corrected for ploidy in tetraploid tumours. DFT1 cell lines were excluded. The difference in rate between clade C and the other clades is statistically significant (p < 0. 005, pairwise contrasts of emmeans, with Tukey multiple testing adjustment). Coloured shading represents regression standard error, and upper and lower lines represent prediction intervals. Data are available in S1      ) are labelled. CNVs 3R1 and 9, parental CNVs of the recurrent chromosome 4 back mutations observed in cell lines, are shown three times each in order to illustrate informative MIPs genotypes that were captured in different samples.
Each dot represents normalised read coverage within a 100-kb genomic window. R, ratio of tumour to normal reads, log base 2. Tumour identifiers are labelled in parentheses. Copy number plots for all tumours are available in S2 Data in https://doi. org/10. 5281/zenodo. 4046235. (PDF) S12 Fig. DFT1  telomeres. (A) Telomeric repeat length in DFT1, DFT2, and normal devils ("hosts"). Each sample is represented by a dot. Five outliers in the "host" group are not shown on the plot but were included in the data used to generate boxplot. Data are available in S1 Table. (B) Linear regression of telomere repeat length and year of collection for DFT1 biopsies with tumour purity included as a covariate. Residual errors are plotted in red, and blue shading represents the standard error of the gradient estimate.
Copy number plots labelled with CNV IDs can be found in S2 Data in https://doi. org/10. 5281/ zenodo. 4046235. (XLSX) S4 Table. Tumour genotypes in devils with multiple tumours. Only DFT1 tumours were considered in this analysis, and DFT1-DFT2 coinfection, which occurred in two devils (Devil-812 and Devil-818) in the cohort, is not included. (A) Summary of tumour genotype information for devils hosting more than one sampled tumour. Each animal has an individual ID, and individual tumours sampled from the devil are labelled "T1," "T2," etc. "Type" lists various categories of multiple tumour sampling regimens: "multiple tumours," when several tumours were sampled from an individual devil at a single sampling time and "time-course," when the same tumour was sampled more than once on different dates.
Those devils for which genotypes of all sampled tumours were indistinguishable or could be distinguished only by variation private to the individual are designated as "single infection. " Individual devils hosting tumours with distinct genotypes known to occur in other devils in the population are labelled "coinfection," although in some cases this might represent sampling of the index tumour within which the new variation arose (S4 Table). An image of a coinfected devil (Devil 165) with tumour genotypes labelled is shown on the right. Underlying data are available in S4 Table. (F) Facial distribution of DFT1 tumours. Diagram was constructed by superimposing facial tumour locations of 96 DFT1s. Raw data are available in S1 Data in https://doi. org/10. 5281/zenodo. 4046235. CNV, copy number variant; DFT1, devil facial tumour 1; km, kilometre; MRCA, most recent common ancestor; SNV, single nucleotide variant.
Copy number plots for all tumours are available in S2 Data in https://doi. org/10. 5281/zenodo. 4046235. CN, copy number; CNV, copy number variant; DFT1, devil facial tumour 1; M5, marker 5; logR, ratio of tumour to normal reads, log base 2; rDNA, ribosomal DNA; SNV, single nucleotide variant.
F) Facial distribution of DFT1 tumours. Diagram was constructed by superimposing facial tumour locations of 96 DFT1s. Raw data are available in S1 Data in https://doi. org/10. 5281/zenodo. 4046235. CNV, copy number variant; DFT1, devil facial tumour 1; km, kilometre; MRCA, most recent common ancestor; SNV, single nucleotide variant. https://doi. org/10. 1371/journal. pbio. 3000926. g001.