dataesr/scientific-paragraphs-categorization huggingface dataset:
} 14: end for 15: build the final classifier C W1∪. ∪W k Purity optimization. Another family of methods [19] is based on the optimization of the purity of each cluster found by an initial clustering K on the dataset. The evaluation of the purity Π of a cluster is based on two criteria: the class impurity which measures the percentage of minority examples in the different clusters of K;the number of clusters k, which should be kept low in a majority of cases.
Results Genome sequencing, assembly and annotation. We selected a male great tit (hereafter the reference bird) from a recent captive population (four generations in captivity) in the Netherlands for genome sequencing and de novo assembly (see Methods and Supplementary Information for a detailed description of genome assembly and annotation). We generated a total of 114 Gb of Illumina HiSeq sequence data; after gap-filling and removal of adaptor sequences, the assembly consisted of a total of 2,066 scaffolds with an N50 scaffold length of over 7. 7 Mb and an N50 contig length of 133 kb. Taken together, the assembled contigs span 1. 0 Gb. We were able to assign 98% of the assembled bases to a chromosomal location using the recently described highdensity linkage map  19  (Supplementary Data 1).
3 , Supplementary Data 5a). Among these categories were dendrite, cognition and several other categories related to neuronal function. Therefore genes related to neuronal function are likely to have been targets of recent positive selection. Learning and cognition. One of the striking genes presented in the learning/cognition category was early growth response protein 1 (EGR1), whose gene expression has been particularly well studied within vocal communication and social contexts in Passerines  24, 25. EGR1 is one of the immediate early genes, which are known to be important in learning and memory  26. In zebra finches, its expression has been shown to be correlated with song learning and practice and is social context dependent  25, 27. To further investigate the role of EGR1 in great tit evolution, we obtained additional EGR1 sequences from 45 bird species (Supplementary Data 6).
These genes are generally enriched for neuronal traits, such as cerebellar Purkinje cell layer formation and axon extension (Supplementary Data 5b). Taken together, it becomes apparent that selection on traits for brain function has played a major role in the evolution of great tits. The great tit methylome. Epigenetic control of gene expression is increasingly recognized as playing a major role in many different cellular processes affecting a large variety of traits  31  , with DNA methylation of cytosines being the most widely studied epigenetic mark. Great tit DNA methylation patterns were investigated by performing whole-genome bisulfite sequencing in whole brain and blood tissue of the reference bird. A total of 10. 2 million CpG sites, representing 66. 7% of all CpG sites, could be called in both tissues.
In this study, the total input genome coverage of Illumina HiSeq sequences was B95 Â (fragments, 3 and 8 kb spanning inserts) based on a genome size estimate of 1. 2 Gb. The combined sequence reads were assembled using the ALLPATHS software  40. This draft assembly was referred to as Parus_major 1. 0. 1. This version has been gap filled and cleaned of contaminating contigs. The assembly is made up of a total of 2,066 scaffolds with an N50 scaffold length of over 7. 7 Mb (N50 contig length was 133 kb). The total assembled contigs spans 1. 0 Gb, and has an assembled coverage of 40 Â using  fragment reads aligned to the assembly. The final assembly (Pmajor1. 04) has been at DDBJ/EMBL/GenBank under the accession JRXK00000000. The version described in this paper is version JRXK01000000. The genome is also publicly available in https://genomes.
Although the final genome assembly therefore is not completely de novo, these contigs only represent 2. 4% of the assembled sequence and 97. 6% of the assembly represents a truly de novo genome assembly. RNA sequencing and assembly. RNA was extracted from eight tissues (bone marrow, homogenized half of the brain, breast filet, higher intestine, kidney, liver, lung and testis) from the reference bird and was then used to prepare tissue-specific tagged Illumina sequencing libraries. The tagged libraries were pooled and sequenced using five lanes on one flowcell of Illumina HiSeq 2000 (same run on the machine). This resulted in 100 bp paired-end unstranded RNA sequencing data. The number of reads per tissue ranged from 98 to 229 million with a total number of 1. 25 billion paired-end reads (Supplementary Data 7).
The sequence reads were checked for quality with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and low-quality sequences were trimmed with Fastq-Mcf  41  resulting in a final number of 1,096,140,415 paired-end reads that were used for the annotation. The RNA sequencing data per individual tissue has been submitted to Genbank (GT_BoneMarrow SRS863935, GT_Brain SRS866013, GT_BreastFilet SRS86603, GT_HigherIntestine SRS866033, GT_Liver SRS866035, GT_Kidney SRS866036, GT_Lung SRS866044, GT_Testis SRS866048). The combined 1 billion reads from all eight tissues were simultaneously de novo assembled using the Trinity software package  42  v. r2013-02-25. Because of the high depth of the RNA sequencing data, we first normalized the data. Following the normalization, the Trinity assembly was subsequently run using the default settings.
We obtained a total of 101,289 assembled transcripts ranging in size from 201 to 16,061 bp, with an average size of 1,335 bp. We also did a reference-based RNA assembling by using the normalized RNA sequencing data in Tophat version v2. 0. 10 (ref. Genome annotation. For the genome annotation, both PASA v. 2-r20130605 (ref. 45) and MAKER v. 2. 31. 5 (ref. 46) pipelines were used (Supplementary Fig. 7 ). First PASA (program to assemble spliced alignments) was used for the identification of spliced transcripts and the grouping of the identified transcripts belonging to the same gene. PASA was run using the assembled Trinity transcripts and the Cufflinks gtf output file from Tophat/Bowtie as input. Alignment within PASA was done using gmap. The PASA analysis resulted in a total of 74,229 different assembled transcripts with an average size of 1,564 bp.
A comparison (using blast) with the annotated transcriptomes of chicken  21  , zebra finch  20  , flycatcher  47  and ground tit  48  (derived from Ensembl release 75 or NCBI refseq release 66) indicated that these transcripts represented 13,626 different genes in these other birds. In the MAKER pipeline, the output of PASA was used as EST evidence. From the ab initio predictors, AUGUSTUS version 3. 0. 2 (ref. 49) was applied by using the chicken gene model. The same RNA-seq de novo assembly as used in PASA was included in MAKER. In addition to de novo assembly, reference-based RNA-seq assembly was used. Last, protein evidence from zebra finch (version 3. 2. 4. 75) and chicken (version 4. 75) obtained from Ensembl version 75 was aligned to the great tit genome. By combining these two pipelines, we obtained 21,057 transcripts for 13,036 great tit genes.
See additional information about functional annotation and repeat/RNA masking in Supplementary Information and Supplementary Data 8. Resequencing and SNP calling. We analysed 29 wild great tit individuals (Supplementary Data 2) covering a wide range of the species distribution (Fig. 1 ); 10 individuals were from the Wytham population in Oxford (UK), and the remaining 19 birds were sampled from 15 European populations. Each bird was sequenced to E10 Â coverage. Paired-end sequencing libraries of each sample were built with an insert size of 300 bp and sequencing was performed on a HiSeq 2000 platform with a read length of 100 bp. The raw reads were trimmed and filtered with Sickle (https://github. com/najoshi/sickle) using default parameters and a length restriction of 75. We then used the Burrows-Wheeler Aligner  50  to map the filtered raw reads onto the assembled great tit reference genome.
The P values were adjusted for multiple testing (Benjamini and Hochberg) with a false discovery rate of 0. 2; w 2 test results for an enrichment of positively selected genes were qualitatively similar for FDRs ¼ 0. 1, 0. 3, 0. 4 and 0. 5 (P ¼ 2. 17 Â 10 À 5 , 3. 97 Â 10 À 3 , 2. 58 Â 10 À 3 and 2. 17 Â 10 À 3 , respectively). GO enrichment analyses. Human orthologues were obtained for the great tit genes by using a combination of Ensembl and Uniprot databases. In the sweep areas, orthologues were found for 460 genes. Functional relatedness of GO terms was done using the Cytoscape plugin ClueGo 2. 1. 4 (ref. 62). ClueGo constructs and compares networks of functionally related GO terms with kappa statistics. A two-sided hypergeometric test (enrichment/depletion) was applied with GO term fusion, network specificity was set to 'medium' and false discovery correction was carried out using the Bonferroni step-down method.
To further confirm our GO enrichment results, we randomly selected 50 sets of genes from outside the sweep area, each containing B460 genes (the same number as in our selective sweep set), and analysed them using the same Cluego settings. We found that the GO term groups significantly enriched in our selective sweep set appeared no more than three times except for phosphatidylinositol-mediated signalling GO group which appeared seven times. This additional analysis further supports the robustness of our results. Test of accelerated evolution in great tit EGR1. We used BLAST to obtain orthologous sequences of the EGR1 gene from 45 additional bird species  37, 38  downloaded from Ensembl version 75 or from Gigascience (http://gigadb. org/ dataset/101000, Supplementary Fig. 2 , detailed species list provided in Supplementary Data 6) to test whether there was additional evidence for the action of positive selection during the evolution of EGR1 in birds.
The brain Tophat alignment was analysed with Cufflinks v2. 2. 0 using the same settings as above, except that the annotation was also included (-g option). Expression levels of brain genes were extracted from the Cufflinks output. Methylation analysis. Blood and brain DNA libraries were constructed according to the Epitect whole-genome bisulfite sequencing workflow (Illumina) with 18 PCR cycles. Whole-genome sequencing data were generated using the Illumina HiSeq 2,500 platform at Business Unit Bioscience, Wageningen UR. The number of paired-end reads (101 bp) were 358M and 292M for the brain and the blood, respectively. Raw sequencing reads were trimmed for quality (Z20) and adaptor sequence using trim_galore v. 0. 1. 4 (http://www. bioinformatics. babraham. ac. uk/ projects/trim_galore/).
The methylation data has been submitted to NCBI with accession numbers SRR2070790 and SRR2070791 for the brain and the blood, respectively. Trimmed sequences were aligned to the reference genome using BSseeker2 v2. 0. 6 (ref. 63) with Bowtie2 v. 2. 1. 0 (ref. 44) in the local alignment mode. A total, 97. 63% and 99. 93% of the genome was covered to an average depth of 31. 88 Â and 33. 04 Â in brain and blood, respectively. Methylation levels for each site were determined using the BSseeker2 methylation call script. All the analyses were done using sites covered by a minimum of 10 reads in both the samples. Only genes found to be 1:1:1 orthologues with chicken and zebra finch were used for methylation analysis. Gene bodies (annotated gene boundaries excluding the 5 0 5% of genes) and TSS (300 bp upstream to 50 bp downstream of the annotated starting position of each gene) for which we had information from at least 50% of the potential methylation sites were used in the dN/dS and expression correlation analysis.
Acknowledgements We thank  Eveline Verhulst  for help with the methylome data,  Christa Mateman  for lab assistance,  Martijn Derks  for calculating the sliding windows,  Tieshan Xu  for the help with the Trinity assembly,  Louise Dittmar  for the help in dN/dS and diversity analysis,  Christian Huber  for help on the sweep analysis and  Jun-Mo Kim  who designed the SNP chip. K. M. S. was supported by a grant from the  Cooperative Research Program for Agriculture Science & Technology Development  ( PJ009103 ) of the  Rural Development Administration, Republic of Korea. T. I. G. , P. C. and K. Z. were supported by a  BBSRC grant  ( BB/K000209/1 ) and a  NERC grant  ( NE/L005328/1 ) awarded to K. Z. , C. J. G. was funded by  Natural Environment Research Council (NERC)  ( NE/K01126X/1 ). K. J.
F. V. was funded by the  Dutch Organisation for Scientific Research, NWO   VIDI grant  ( 864. 10. 008 ). B. C. S. was funded by  ERC   Advanced Grant  ( 250164 ) and by a  Wolfson Merit Award  from the  Royal Society. J. S. was funded by a  European Research Council (ERC)   Starting grant ,  Avian EGG  ( 202487 ) and a  Natural Environment Research Council (NERC) ,  The Great Tit HapMap Project  ( NE/J012599/1 ). M. E. V. was supported by the  Netherlands Organisation for Scientific Research  ( NWO-VICI grant ) and the  European Research Council  ( ERC-2013-AdG 339092 ).
All the tribunals covered areas including economic, political, and cultural violence against women. Hearings and interventions also take place on local levels: e. g. the Women's Courts (Nari Adalats) managed by barefoot village-level legal activists (International Center for Research on Women 2002). India's women's movements also take part in international campaigns to end violence: on 25 November (International Day for the Elimination of Violence against Women) the campaign for '16 days of activism against gender-based violence' starts to raise awareness building up to International Human Rights Day, 10 December. Similarly, as a response to the UN data stating that one billion girls and women are subjected to violence globally, a large-scale campaign was launched in 2013. The call of the campaign is simple -One Billion Rising (people and not only women) must rise to stop violence against women.
One fifth of the microtiter plate's volume was used for Cell counting, using a FACSCanto II flow cytometer (Becton Dickinson, Franklin Lakes, NJ) equipped with an air-cooled laser providing 15 mW at 488 nm with the standard filter set-up. Becton Dickinson Trucount TM beads were used to calculate the abundance of the cells as described by  Pecqueur et al. (2011). A total of 20 ml of mixed fluorescent beads 1 mm in diameter (Molecular Probes Inc. , Eugene, OR) were added as an internal standard to 300 ml of the diluted sample (20th dilution). The flow rate of the cytometer was set to high (acquisition time: 1 min). Eukaryotic pico-phytoplankton cells were detected and analyzed using natural chlorophyll fluorescence (chlorophyll a FL3 670 nm LP). The flow cytometry data were analyzed using BD FACSDiva (Becton Dickinson).
Statistical analysis First, to investigate the relationship between fitness, G, and the number of sequential generations, we used data from those lines that survived throughout the experiment: 21 lines for O. tauri, 24 for O. mediterraneus, eight for B. prasinos, and seven for M. pusilla. We performed an ANOVA on the control data to test whether G changed significantly between bottleneck times. The change in fitness of MA lines as a function of time was thereafter analyzed by dividing the growth rate in the MA lines by the growth rate in the control, G r , to remove the variation in the experimental set-up through time. For each line, the relationship between the relative fitness (G r ) and the number of generations was tested using Pearson's correlation. Second, for fitness assays in stressful conditions, S T was calculated in all conditions using G MA and G control at each condition as explained above.
ACKNOWLEDGMENTS We acknowledge  Hervé Moreau ,  Sheree Yau , and the  Genomics of Phytoplankton lab  for support and stimulating discussions. We also thank three anonymous referees for their constructive comments on a previous version of this manuscript. We are grateful to  Sebastien Peuchet ,  Aurelien De Jode ,  Claire Hemon , and  Elodie Desgranges  for technical assistance with the mutation accumulation experiments from 2011-2013, and to the  Agence Nationale de la Recherche  ( ANR ) for supporting them (PICOVIR, DECOVIR, TARA-GIRUS;  BLAN07-1_200218 ,  ANR-12-BSV7-0009 ,  ANR-09-PCS-GENM-218 ). This work was funded by grant  ANRJCJC-SVSE6-2013-0005  to G. P. and S. S. F.
This results in modification of binding sites for qPCR primers or loss of internal retrotransposon domains, with concomitant creation of solo LTRs  [53] [54] [55] , which would not be accounted for by our method, based on amplification of the internal RT gene. Furthermore, the expected number of annealing sites from our BLAST analysis (Additional file 1A) is almost always lower than the calculated copy number by qPCR (Fig. 2 ) in Bethune (this happens for 5 out of 6 families). This is probably a result of unassembled genome regions which are yet to be reported in the database (in general regions which are difficult to assemble are rich in repeats such as TEs). Nonetheless, the copy numbers we reported for each family for Bethune (Fig. 2 ), were positively correlated with the number of TEs identified by BLAST alignment of primer binding sites to the Bethune genome assembly (r = 0.
The same procedure was conducted to find out the copy numbers for the reference gene. The copy numbers of the reverse transcriptases of each family for each sample were normalized to the copy numbers of the reference gene (ETIF1) and the average absolute copy number and standard deviation (from the 8 replicates), were calculated and plotted for each cultivar and TE family. Statistical differences for each TE copy number among cultivars, were determined using the non-parametric test of Kruskal-Wallis, followed by multiple comparisons using Dunn's test, using GraphPad Prism version 6. 0 (GraphPad Software, La Jolla California USA). Correlation coefficients were calculated in excel for the relationship between the expected copy number of TEs in each family estimated using the primer pairs to BLAST against the flax genome, and either, the calculated copy numbers from qPCR, or the number of scored bands in the SSAPs.
When the band was not present in this cultivar then the similarity to the LTR and the flanking region was found in different sections of the genome as expected. Once mapped on the genome, the IDs of the flax genes with associated TE insertions, were used to find the closest Arabidopsis thaliana ortholog from a database previously obtained by performing blast analysis of flax transcripts against the peptide TAIR database (release 10). Arabidopsis ortholog IDs were then used to perform functional Gene Ontology (GO) classification using the GO annotation search from TAIR  [96] , to find out what categories of genes were predominantly affected by TE insertions. An additional enrichment analysis was performed using AgriGO  [97, 98]  by comparing the Arabidopsis orthologs to the background of all Arabidopsis genes using a Fisher test, a Yekutieli multiple test adjustment and a minimum of 1 mapping read.
These were used to generate the geometric mean for relative quantification of the test genes using the ΔCt of the referencethe test gene. Statistical differences in each gene among cultivars were calculated using unpaired two-tailed t-tests after a Bonferroni correction for multiple comparisons (p < 0. 005), using GraphPad Prism version 6. 0 (GraphPad Software, La Jolla California USA). Samples were aliquoted in 384-well plates (with three technical replicates per sample and tissue combination) using a Biomek 3000 Laboratory Automation System (Beckman Coulter, Brea, CA, USA), and the qRT-PCR was run using a QuantStudio 6 Flex Real-Time PCR system (Applied Biosystems-Life Technologies, Carlsbad, CA, USA). Sample reactions were done in 10 μL with 5 μL of SYBR-green (Molecular Probes -Thermo Fisher Scientific, Waltham, MA, USA), 2.
Funding Genome Canada ABC Program  grant TUFGEN;  Natural Sciences and Engineering Research Council (NSERC -Canada)   Discovery Grant   2014-03596  to MKD;  NSERC Alexander Graham Bell Canada Graduate Scholarship  ( CGS D) ,  NSERC   Michael Smith Foreign Study Supplement (CGS-MSFSS ), and  Alberta Innovates -Technology Futures  (AITF) top-up award to LGG. The IJPB benefits from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ) to MAG.
Participants Altogether, 132 participants were recruited for this study, of which 12 patients were excluded due to motion during the MRI acquisition (see the section "MRI processing"), resulting in a total of 120 participants, comprising 44 patients with prodromal DLB (pro DLB group),  16  patients with DLB at the mild dementia stage (mild DLB group), 13 patients with prodromal AD (pro AD group), 26 patients with AD at the mild dementia stage (mild AD group), and 21 healthy elderly controls (HC group). Patients sharing both DLB and AD criteria were not included in the study. Demographic and clinical data are presented in Table  1. The five groups were examined by clinicians with expertise in dementia, who performed a complete anamnesis and medical examination. Using  [29] , akinesia, rigidity, and tremor at rest were rated from 0 to 4 (0 for no symptoms to 4 for serious impairment).
Data acquisition The pulsed ASL sequence was performed on a Siemens Verio 3T scanner equipped with a 32-channel head coil (Siemens, Erlangen, Germany). In total, 121 whole-brain T2 * -weighted (gradient echo) echo planar images were acquired using the QUIPPS II sequence provided by the manufacturer. The parameters were: The tagged volume was 10 cm thick, positioned at the neck, and its distal part was 23 mm below the first slice to avoid saturation. Bipolar gradients were used to eliminate the signals from fast moving spins (>10 cm•s -1 ). The first volume recorded corresponded to M0, and non-tagged images were acquired in alternation with tagged images. A 3D MPRAGE T1-weighted image was also acquired at the same session. The parameters were: imaging matrix 192 × 192 × 176 and 1 mm 3 isotropic voxels.
MRI processing Images were processed using SPM8 (Welcome Department of Cognitive Neurology, London, UK) and in-housedeveloped software. All data were processed for each participant separately. Functional images were first corrected for motion and magnetic field B0 inhomogeneities. According to the motion parameters provided by SPM, patients with translations and rotations higher than 2 mm and 2°, respectively, were removed from the analysis. As the TE is high enough to make the ASL sequence sensitive to blood oxygen level-dependent fluctuations, the signals were high-pass filtered at 0. 1125 Hz according to the method of Chuang et al. [37]. One CBF map per subject was then calculated according to the TE-corrected method published by Foucher et al. [38]. The M0 map was coregistered to the T1 image and the transformation parameters were used to coregister the CBF map in the same way.
Statistical analyses Analyses were performed in Matlab (R2012b, Mathworks, Natick, MA). The gender and age distribution between groups were assessed by chi-squared tests and a oneway ANOVA, respectively. A global decrease in perfusion in patients relative to healthy controls was evaluated by two-sample T-tests on values of whole-brain mean perfusion. Voxel-wise statistical analyses of perfusion were performed using ANOVA, with age  [39, 40]  and gender  [40]  as regressors of non-interest. These analyses were both conducted in absolute and relative measurements, the latter being corrected for the global mean value. The relative measurement corrects for inter-individual differences in global perfusion and explains local differences in perfusion. The statistical analyses combined a p uncorrected < 0.