dataesr/scientific-paragraphs-categorization huggingface dataset:
Using this measure changes our interpretation of memory integration measures, which is acknowledged throughout the manuscript. The second memory test that was administered a week after scanning was not used for final analyses, because very little trials were useful. We were planning to do a memory durability analysis on these data see  27. However, since this test was exactly the same as the first memory test, including the associative recognition phase where the right scene was shown on the screen, participants could learn associations during this test as well. Therefore, only trials that were already correctly recalled (i. e. during associative recall) were usable, yielding very little incongruent trials. We therefore decided to not focus our analyses on this test. Congruency was defined as the subjective measure of congruency for each object-scene association that was indicated by the participant during the congruency rating task (see above).
Feature selection was performed using the "SelectKBest" function from the "scikit-learn" Python library. Selected voxels corresponded to the K highest values based on an ANOVA F-test. To take into account the onset of the Blood Oxygenation Level Dependent (BOLD)-response, we used the localizer scans 3 TRs (6 seconds) after stimulus onset to fit our classifier  18, 19. Because the localizer blocks lasted for 12 seconds, we investigated two scans per series: the 4th (occurring 3 TRs after stimulus onset) and the 7th (occurring 3 TRs after the 4th scan) which resulted in 40 scans (20 scenes and 20 faces). We trained the classifier using K-fold cross-validation by splitting the scans into 20 even chunks of 2 scans each, making sure that every chunk contained scans from different localizer blocks.
Our rationale for doing so was that we wanted to optimize our generalization performance by predicting the stimulus classes from scans corresponding to independent behavioral stimuli. For each fold, we computed performance accuracy estimates in terms of the proportion of scans that were correctly classified. We then computed the average across folds to estimate our overall mean classifier performance accuracy. We applied the trained classifier to our experimental data (AC-learning trials, again 6 seconds/3TRs after stimulus onset) to predict the probability of scene-related activity as opposed to face-related activity within the selected voxels. These probability values were then used to compare to behavioral measures of congruency and reactivation (see below).
Statistics. Behavioral and neuroimaging analyses were conducted using custom codes in Python (Jupyter Notebook, https://jupyter. org) and Matlab2014b (analysis code available at https://github. com/marliekevk/ Integrating-Memories). For behavioral analyses and analyses between multivariate classifier performance and behavioral measures, we used SPSS Statistics 25 (IBM) to perform one-sample two-tailed T-tests to assess whether our participants performances were above chance level, and 2 × 3 repeated measures ANOVAs to calculate effects of congruency (congruent and incongruent) and reactivation (strong, a bit, or none) on memory performance (memory score) and classifier performance. As measure of effect size, we calculated Cohen's d. For these statistical tests, alpha was set at p < 0. 05. To determine chance level for the memory score, we reasoned how many points an item would score on item recognition, associative recall and associative recognition if there was no memory, and any points were the results of a false alarm on item recognition (FA) and lucky guesses thereafter.
Acknowledgements We would like to thank all participants for their time,  Lianne de Vries  for rating the associative recall performance, the staff at the  Spinoza Centre for Neuroimaging , and many colleagues for support with analyses. We also thank  Laura de Herde  from the  University of Birmingham  for help with analyses. The authors declare no competing financial and non-financial interests. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  704506.
Methods Sample collection and sequencing. We collected EDTA-preserved blood from 25 Old World camels including nine dromedaries (C. dromedarius), seven domestic Bactrian camels (C. bactrianus), and nine wild camels (C. ferus) (Supplementary Data 1 and Supplementary Fig. 1 ; see Ethics statement below). We included domesticated camels that represent a variety of geographic locations and/or 'breeds' according to information supplied from the camels' owners, although microsatellite evidence for both dromedaries and domestic Bactrian camels suggest little genetic differentiation among breeds  [11] [12] [13]. In dromedaries, moderate differentiation exists between camels from northwest Africa (e. g. , Canary Islands and Algeria), the Horn of Africa (e. g. , Ethiopia, Somalia, Kenya), and their remaining range including northeast Africa, the Middle East, and Pakistan  11, 13.
We extracted DNA using the Master PureTM DNA purification kit for blood (Epicentre version III) and generated a 500 bp paired-end library for each sample. We sequenced each library with a single lane of an Illumina HiSeq (Illumina, USA) according to standard protocols. Due to the sampling procedure, which included a wild endangered species, it was not possible to retrieve tissue samples that would facilitate expression studies and/ or functional analyses. A follow-up project will consider this next analytical step. Read processing and alignments. We trimmed the 3′ end of sequence reads to a minimum phred-scaled base quality score of 20 (probability of error <1. 0%) and excluded trimmed reads <50 bp in length using POPOOLATION v1. 2. 2  59. We aligned all processed reads to the C. ferus CB1 reference genome (Genbank accession: GCA_000311805.
2, so values much less than this are indicative of an excess of false-positive SNPs  22. Estimates of genetic variation (i. e. π, θ, and heterozygosity) and Tajima's D were averaged across nonoverlapping 10-kb windows using VCFTOOLS and excluding SNPs on the putative X and Y scaffolds. Population clustering. To infer population clustering, we used SNPRELATE 1. 10. 1  65  to calculate linkage disequilibrium between pairs on SNPs within a 1-Mb sliding window and randomly removed one locus from each pair with a correlation coefficient (r 2 ) >0. 5. The resulting dataset contained 90,918 unlinked SNPs. We used the unlinked SNPs to examine the global ancestry of Old World camels using the principal components analysis method in SNPRELATE and a Bayesian modelbased approach implemented in ADMIXTURE v1.
The homogeneity test examines the intraspecific (polymorphism) and interspecific (divergence) genetic diversity, which are expected to be correlated under neutral evolution. Under positive selection, the amount of polymorphism is expected to be reduced along one branch. To perform the test in dromedaries, we calculated four values for each of the 17,912 protein-coding genes (longest isoform per gene) annotated in the camel genome: We then tested the null hypothesis that A C ¼ B D A B ¼ C D using a Fisher exact test for a 2 × 2 contingency table. We omitted any genes with either A or C < 1. Alpaca alleles were identified by mapping all short-insert, paired-end sequencing reads from the alpaca genome assembly  15  (BioProject accession PRJNA233565) to the camel reference genome as described above.
we emphasize that these genes are in ranked order of priority, or evidence, rather than of statistically significant effect. The above procedure was repeated for domestic Bactrian camels using the wild camels again for comparison. The second approach to identify putative positively selected genes utilized a window-based approach. We calculated nucleotide diversity (π) within and divergence (D XY ) between camelid species across 100-kb sliding windows with a step size of 50 kb using the popgenWindows. py script (https://github. com/ simonhmartin/genomics_general). Only windows with at least ten polymorphic sites were included. Next, we defined candidate positively selected regions in dromedaries and domestic Bactrian camels as windows in both the lowest 0. 5 percentile in π within species and the highest 99.
5 percentile) and a substantially negative measure of Tajima's D (D ≤ -2) in C. ferus. For all window-based analyses, protein-coding genes that overlap these windows were identified. Functional enrichment. We assigned GO terms to all annotated protein-coding genes in the C. ferus reference genome using BLAST2GO v3. 0. 8 66. BLASTP v2. 2. 30 (http://ncbi. nlm. nih. gov/blast) searches were conducted against metazoan protein sequences from the 'nr' database with an e value cut-off of 10 -3 and retaining only the top 20 hits. We tested for functional enrichment of GO terms using topGO v2. 28. 0 67 with a classic Fisher exact test and minimum annotation count of five for each GO term across the full annotation set. We also tested each set of putative positively selected genes for overrepresentation of KEGG pathways using WEB-GESTALT  68.
64dentification of Sex Chromosomes' below), with a minimum allele count <2, missing a genotype in more than five individuals, with 4 > DP > 30 per genotype, and deviating from Hardy-Weinberg equilibrium (p < 0. 0001) in VCFTOOLS v0. 1. 12b64. We used this set of SNPs as a training set to perform variant quality score recalibration in GATK, assigning a probability of error to the training set of 0. 1. This recalibration develops a Gaussian mixture model across the various annotations in the high-quality training dataset then applies the model to all variants in the initial dataset. After variant recalibration, we excluded all SNPs with VQSLOD score outside the range containing 95% of the SNPs in the training set. We hard-filtered any remaining variants missing genotypes in more than five individuals.
Acknowledgements We thank all camel owners for dedicating aliquots of commensally collected samples for research purpose, especially  J. Hare  and  K. Rae  from the  Wild Camel Protection Foundation ,  G. Gassner  (Austria),  J. Burgsteiner  (Austria), A. and  J. Perret  (Kenya) and  R. Saleh  ( Syria ) for facilitating sample collection and for their continuous support. We thank the  CSC-IT Center for Science, Finland , for generous computational resources. PAB acknowledges support by the  Austrian Science Fund (FWF)  project grants  P24706-B25  and  P29623-B25.
Microarray Analysis C E L fi l e s w e r e p r o c e s s e d a n d n o r m a l i z e d u s i n g AromaAffymetrix (https://statistics. berkeley. edu/tech-reports/ 745) and a chip definition file provided by Brainarray  (Dai et al. , 2005). Differentially expressed genes were found using limma  (Ritchie et al. , 2015)  with an adjusted P-value cut-off of 0. 01 and a minimal difference of a factor 2 (ie absolute value of the log2 of the fold change equal or greater than 1). Unique HUGO identifier of differentially expressed genes were passed to the Enrichr web application  (Kuleshov et al. , 2016)  for functional enrichment analysis. The data discussed in this publication (including raw and processed files, expression values for each gene in each sample, and fold change and adjusted P-value for each gene in the three comparisons: mucoid vs blood, serous vs blood, mucoid vs serous) have been deposited in NCBI's Gene Expression Omnibus  [46]  and are accessible through GEO Series accession number GSE125532 (https://www.
RT-qPCR Thirty-two RNA extracts were made from wbcs in COME effusion (one ear sample per child) and matching blood samples were analyzed RNA from both blood and middle ear effusion were run on custom designed 96-well TaqMan Fast array plates (Applied Biosystems) following manufacturer's instructions. There were 32 patient samples (2/32 serous, 2/32 intermediate, 2 non-classified and 26/32 mucoid) with RNA RIN scores ranging from 2. 3 to 7. 7; corresponding blood samples from these patients had RIN scores ranging from 7. 9 to 9. 0. RT-qPCR data was analyzed by DDCt method. The efficiency of the assays was ensured by the use of inventory TaqMan assays.
VEGF Protein Assay Thirty-seven COME effusion samples (one ear sample per child) and matching plasma samples were analyzed for VEGF. Tubes containing samples were weighed and blanked against an empty tube to estimate fluid volume. Depending on sample weight and viscosity the samples were diluted with 0. 5, 1. 0, 1. 5, or 2. 0 ml of PBS dispersed by vortexing for 1 min then pelleted at 3000 rpm for 10 min at 4°C. Supernatant COME effusion samples and corresponding patient serum samples were assayed for VEGF protein using MSD® MULTI-ARRAY® Human VEGF assay (Meso Scale Discovery). The COME effusions data was normalized to plasma volume by multiplying the COME assay value by its dilution (weight of glue and volume of PBS diluent). The assay read out is calibrated against standard curve and VEGF titres are in units of pg/ml.
Neutrophils and macrophages predominated in all sample classes. However, serous samples had significantly higher lymphocyte and eosinophil differential counts, while mucoid samples had significantly higher neutrophil differential counts. Macrophage differentials did not differ significantly between serous, intermediate and mucoid samples (Figure  1 ). Occasional macrophages showing erythrophagocytosis were observed in low numbers of samples (12. 5%); this may suggest that in those cases the presence of RBCs may not only be due to iatrogenic contamination during the sampling procedure, but minimal true hemorrhage may have occurred. Occasional columnar epithelial cells were observed in 14. 6% of samples. We explored associations for cytology features with patient metadata (age, racial ethnicity, duration of disease, vaccination history, antibiotic use, previous grommet surgery, occurrence of atopy).
Microarray Analysis To gain a better understanding of gene expression profiles of mucoid and serous effusions we performed microarray analysis of wbc RNA extract from effusions with a high RNA yield: Transcriptome analysis was performed on 6 serous COME effusions from six children and 6 mucoid effusions from five children (samples from both ears in one child), and their matching 11 blood samples. We used the Affymetrix Human Gene 2. 0 ST microarray, using annotations provided by BrainArray (Molecular and Behavioral Neuroscience Institute, University of Michigan)  (Xuan et al. , 2010). After data normalisation using AromaAffymetrix (http://aroma-project. org/), we performed a multidimensional scaling of the data (Figure  S1 ). Dimension 1 clearly discriminates blood from ear effusions, while dimension 2 discriminates the different ear effusions, notably but incompletely splitting mucoid from serous effusion.
RTqPCR Analysis Shows Upregulation of Hypoxia Signaling Pathways in COME Effusions Thirty-two RNA wbc extracts were made from COME effusion (one ear sample per child) and matching blood samples were analyzed for 52 selected genes by RTqPCR. This confirmed significant upregulation of hypoxia signaling pathway genes including VEGF ligands (VEGFA, VEGFB) receptors (KDR, FLT1) adapter proteins (NRP1, NRP2), and HIF transcription factor (HIF1A) in COME relative to blood (Figure  S3 ). Overall there was reasonably good concordance between RTqPCR and microarray data as 22 over 27 (81%) of the differentially expressed genes according to the array were also differentially expressed according to RTqPCR experiment (P-value < 0. 01, Figure  S3 ). RTqPCR identified 14 additional genes differentially expressed in wbcs from COME effusion and blood that were not differentially expressed in our microarray analysis, suggesting that in our experimental design RTqPCR was more sensitive than microarrays, albeit with more variability.
DISCUSSION The Effusion in COME Reveals Upregulation of Hypoxia Signaling Pathways We report that hypoxia signaling pathways are upregulated in wbcs from both mucoid and serous COME fluids compared to wbcs in venous blood. Significant upregulation was shown by microarray data analysis using Enrichr annotation strategies, including ontology and pathway analysis (using the WikiPathways database and the GO Biological Processes database), and HIF-1A regulatory pathway analysis (using the NCI-Nature database, Table  1 ). Findings were confirmed by RTqPCR for selected pathway genes (VEGF ligands, VEGF receptors and adapters, and HIF transcription factor). We also found significantly elevated VEGF protein in COME fluids when compared to plasma. Other studies have reported the presence of VEGF protein in the effusion and the middle ear mucosa of patients with COME  (Jung et al.
27E-18 JUN (human) 9. 37E-16 1. 20E-18 ETS1 (human) 3. 32E-14 5. 51E-16 GATA2 (human) 1. 81E-13 3. 77E-14 ENCODE TF ChIP-seq 2015 IKZF1_GM12878_hg19 4. 28E-28 3. 94E-25 SMARCC1_HeLa-S3_hg19 3. 14E-18 2. 57E-20 NCI-Nature HIF-1-alpha transcription factor network 4. 78E-07 0. 000002112 HIF-2-alpha transcription factor network 0. 004811 0. 07746 Single Gene Perturbations from RARA Activation -AM580 (RARA Agonist) human GSE5679 2. 55E-67 4. 65E-55 GEO up sample 2385.
SNP detection and summary statistics Due to high variability in sequence coverage between samples (37,000 -1,400,000 reads per sample; Additional file 1), only 68 samples across the nine sites were retained for analysis (Table  1 ). Of these, 50 samples were from sites where temperature data was also available (Table  1 ; Fig. 1 ). In total, 439,598 SNPs were identified across 273,397 RADtags with an average coverage depth of 14. 85x per sample. Due to low read coverage overall, only 506 variable SNPs were identified, 482 of which were from independent RADtags and could be used for population genetic analyses. Marker independence across sites was confirmed with no loci flagged as showing linkage disequilibrium. Although some loci showed significant deviation from Hardy-Weinberg, the loci involved differed across sites and were thus included in further analyses  [45].
Loci under selection Both ARLEQUIN and BAYESCAN analyses suggested that the 27 loci identified as outliers were under balancing selection (Fig. 2 ). Temperature data could not be collected from three of the study sites due to loss of equipment during the data collection period (Balearic Sea, Tyrrhenian Sea and North Africa). Temperature data was thus available for six of the study sites (Fig. 1 ) and only these study sites were included in the  environmental association analyses. Of the temperature parameters collected from six of the sites (Table  3 ), no environmental associations were found with any loci.
SNP detection and summary statistics All analyses were carried out on the Analyses and Bioinformatics for Marine Science (ABiMS) Galaxy Platform at the Station Biologique de Roscoff (galaxy. sb-roscoff. fr [87] ;). Samples with mean Phred scores of < 25 or read coverage of < 150,000 were removed from the dataset and sequences were trimmed to 60 bp length using Trimmomatic version 0. 36. 4  [88]. STACKS version 1. 46. 0  [89]  was used to de novo map the sequences and call SNPs by sequentially running "ustacks", "cstacks" and "sstacks" using the parameters suggested to be optimal for population genetic inference by  [90]  namely, allowing a minimum depth of coverage of three (ustacks: m; stack coverage), a maximum of two mismatches between reads for a single individual (ustacks: M), and four mismatches between primary and secondary reads (ustacks: N).
These settings have been found to increase the number of loci and reduce the SNP and allele error rates within a dataset  [90]. Furthermore, the low value of M used here reduces the risk of paralogous loci being merged into the same SNP  [91]. The number of mismatches allowed between loci was set at three (cstacks: n  [91] ;). The removal or break up of highly repetitive RAD tags was permitted within the program (ustacks: deleverage). The programme populations from the STACKS suite, was used to process the SNP data  [89]. RADtags with at least 10x depth of read coverage were retained to ensure accuracy of heterozygous SNP calls (populations: p  [50] ; ). Lumped paralogs can be a concern in high coverage data sets  [92] , but 10x coverage is considered low  [93]  and thus reduces this to a low risk factor.
Population structure Pairwise F ST were calculated in ARLEQUIN version 3. 5. 2. 2  [96]  and significance in certainty of the estimator assessed following Bonferroni correction for multiple tests. Straight-line distances between sites were calculated using ArcGIS Version 10. 1 (ESRI, 2011) and shortest ocean distances between sites were calculated in R version 3. 1. 2 [44]  using the package marmap  [100] , where distance was calculated excluding positive elevation  [99]. Isolation-by-distance (straight line and shortest ocean) was tested in ARLEQUIN (Version 3. 5. 2. 2) using a Mantel test  [101]  with 10,000 permutations. To visualise these relationships, neighbor-joining (nj) tree estimation plots were constructed in R package ape 5. 3  [102] , following the methods outlined in  [103] , and were constructed using F ST and shortest ocean distances for each pairwise (site) combination.
Divergence between sites was also assessed using a Discriminant Analysis of Principal Components (DAPC) with the R package adegenet 2. 1. 2 [104] , with 30 components and two discriminant functions retained in the analysis. Hierarchical structure was also considered by carrying out a second DAPC excluding the most divergent site, as identified in the first DAPC. Genetic clusters (populations) were inferred using Bayesian analyses carried out in STRUCTURE version 2. 3. 4  [54]. The analyses assumed admixture, correlated allele frequencies, and were run with 100,000 burn-in cycles and 100,000 Markov Chain Monte Carlo runs. The number of populations (K) was considered between 1 and 10, with 10 replicates per K. This was repeated with and without location priors, and using a hierarchical approach to resolve fine-scale genetic structure by removing highly differentiated populations  [45].
org) in which larvae could also settle if encountered. Larval exchange counts (i. e. the source-sink contribution) were generated after 5, 8, 10, and 12 weeks PLD. Model outputs were processed in Matlab (Mathworks, Version R2016a). Pairwise matrices of larval exchange counts, standardised using proportions, were generated from larval fates averaged across the three focus years to compare against genetic pairwise F ST matrices. Daily positions of larvae throughout the simulation were used to generate cumulative 2-D maps of larval density to highlight the predicted pathways of dispersal and areas of potential settlement. The proportion of settlers reaching each study site from each release site under the four larval duration scenarios (5, 8, 10 and 12-weeks) in the ocean circulation modelling simulations were inverted and used to form a matrix of pairwise dispersal resistances (i.
Larvae of S. alveolata can be found in the water column all year round  [42] , suggesting that they are reproductively active throughout the year, thus temperature data from all months were included in the analyses. We utilised a gene-environment association software, BAYENV2  [119] , to test for covariance between SNP allele frequencies and the 16 thermal variables. BAYENV2 controls for neutral population structure by first estimating a covariance matrix among populations for all loci and then accounting for that covariance in the gene-environment association test  [120, 121]. The programme was run with 100,000 iterations for both neutral parameterisation and association testing  [122]. Outlier loci were classed as those with a Bayes Factor > 3  [8, 123, 124].