dataesr/scientific-paragraphs-categorization huggingface dataset:
Positive Selection and Gene Ontology Enrichment Analysis To assess the selective pressures acting on the orthologous genes between Zosterops and the other four avian species, we compared the nonsynonymous with synonymous substitution ratio (dn/ds ratio) using likelihood methods implemented in PAML v. 4. 7  (Yang 2007). Accurate estimates of dn/ds ratio requires high-confidence sequence alignments, as misalignment errors can potentially result in the false positive detection of positive selection  (Jordan and Goldman 2012). To generate such alignments, the genes were first translated into protein sequences and then aligned using four different assemblers (MAAFT, MUSCLE, KALIGN, and T_COFFEE) with M-COFFEE  (Wallace et al. 2006) , part of the T-COFFEE package v. 11. 0  (Notredame et al. 2000).
Genome-Based Phylogeny Reconstruction We used the 4-fold degenerated sites from genes that did not show evidence of positive selection to build a phylogenetic tree. We used JModelTest 2. 1  (Darriba et al. 2012 ) and identified that GTR + I was the best-fit model based on the Akaike Information Criterion (AIC). Using this substitution model, a maximum-likelihood tree was built using RAxML version 8  (Stamatakis 2014 ). To date the tree and calculate substitution rates, we used the software MCMCtree from PAML 4. 7  (Yang 2007). Because the GTR model is not available in MCMCtree, we used HKY instead. We ran 200,000 iterations (sampling every 2 iterations until the program reached 100,000 samples) after a burn-in of 50,000. We used the age range of the fossil of Vegavis, an extinct bird related to ducks and geese, to set a Genome of the Great Speciator GBE Genome Biol.
Gene Gains and Losses The perl script treefam_scan. pl implemented in TreeFam  (Li et al. 2006 ) was used to group protein sequences into gene families. A birth/death process was then modeled in CAFE 3. 0  (Han et al. 2013)  to estimate gene gains and losses, using an ultrametric tree obtained from MCMCtree. In order to provide an accurate birth/death parameter over the tree (lambda), we removed the gene families that were present in only one species, plus four with large size or variance, as required in CAFE. Possible genome assembly and annotation errors were corrected using the "errormodel" command in the Python script caferror. py.
Detailed Phylogenetic Analyses of Zosterops To determine whether the three Lifou taxa are the result of a single or multiple colonization events, and to infer when the Loyalty islands were colonized by Zosterops, we constructed a dated phylogenetic tree of all species of Zosterops from the southwest Pacific region surrounding New Caledonia, including taxa that went extinct in historical times. For this we downloaded mitochondrial DNA data (cytochrome B, ND2, ND3, and ATPase) from GenBank  (Phillimore et al. 2008; Black 2010; Black, Clegg, Phillimore, Owens, unpublished data). GenBank accession numbers are provided in supplementary table S1, Supplementary Material online. We also sequenced the ND3 gene for some of our own samples of Loyalty island Zosterops, using the PCR protocol described in  Black (2010).
2011). POPOOLATION2 was applied for calculating allele frequency differences between Z. inornatus and Z. minutus. For this we set the minimum count of minor allele to 6 and minimum and maximum coverage to 50 and 200, respectively. Fst-sliding. pl perl script was used to identify single nucleotide polymorphisms (SNPs) highly differentiated between species with a sliding window of one. The effect of low coverage on F st estimates was inspected with the Fisher's exact test  (Fisher 1922 ) using the fisher-test. pl script from POPOO-LATION2. P values were corrected for multiple testing. The significant highly divergent SNPs (F st > 0. 7, FDR < 0. 05) were mapped on Z. lateralis melanops genome using the perl script SNPdat_v1. 0. 5. pl  (Doran and Creevey 2013).
Demographic Reconstruction Historical inferences of effective population size (N e ) for Zosterops taxa were inferred using the pairwise sequentially Markovian coalescent model (PSMC;  Li and Durbin 2011). We used SAMTOOLS 0. 1. 18 (Li, Handsaker, et al. 2009 ) to generate diploid consensus sequences for each individual with the parameter -C50 to reduce the effect of reads with excessive mismatches. Before running PSMC we converted the diploid sequences into the required format using the perl script fq2psmc. pl. The PSMC model was run for the three Lifou Zosterops taxa with default parameters, 0. 5 years as generation time  (Moyle et al. 2009 ) and our previously estimated mutation rate. Reliability of our estimates in N e fluctuation was evaluated using a bootstrap test run 100 times for each individual using the 100 simulated data sets.
Plumage Divergence in Zosterops lateralis We compared the reference genome of Z. lateralis melanops (black head plumage) with genome resequencing of the closely related subspecies Z. lateralis nigrescens (green head plumage). Reads retained after quality control were synchronized with the reference genome using mpileup command in SAMTOOLS and variants were called with "bcftools. " SNPs obtained were then annotated on Z. lateralis melanops genome using the perl script SNPdat_v1. 0. 5. pl  (Doran and Creevey 2013).
036 Gb, suggesting a coverage of 64X, and with an N50 of 3. 5 Mb and 22. 9 Kb for scaffolds and contigs, respectively (supplementary fig. S1  and table  S3 , Supplementary Material online). The good quality of the genome was confirmed by k-mer and GC content analyses (supplementary figs. S2 and S3, Supplementary Material online). These genomic data have been deposited to GenBank/EBI under the accession LAII00000000.
Acknowledgments We thank the  Department of Economic Development of the Loyalty Islands Province  for permission to collect samples, and the  Waco me Wela organization  and its volunteers for providing support on Lifou. We thank  Sonya Clegg ,  Albert Phillimore , and  Ian Owens  for help and advice. Some of the mitochondrial sequences used for the phylogenetic analysis are part of unpublished work by  Black, Clegg, Phillimore , and  Owens , and we therefore thank them for making those sequences available. Alex Lord,  Thomas Duval , and  Sophie Bell  provided help with fieldwork methods. This work was funded by the  European Research Council ,  Marie Curie Actions, Alexander von Humboldt Foundation , and the  Royal Society (UK).
DoE Analysis Finally,a sp resented in detail in the literature  [1, 2] ,t he DoE method was applied to the six chosenr esponses of the DoE, which are T p1 , A p1 , T p2 ÀT g,DSC , A p2 , T p2 (2 %)ÀT p2 (0 %), A p2 (2 %)ÀA p2 (0 %) using the Design-Expert software  [ 31]. Am odelw ith n coefficients can only be investigated with aD oE consisting of at least n runs. According to the number of factors chosen and the number of materials manufactured,t he possible models are:m ean value, linear, first order interactions, or quadratic. The unknownc onstants of the model are obtainedb yo ptimization on the experimental results. An analysisofv arianced etermines the modelf orm best representingt he results while taking into account the least number of terms. As econd analysiso f variance is performed on the chosen modelf orm to eliminate the termsc orrespondingt on on-influential factorso r interactions (p value < 0.
The research was developed by the identification of four keywords (Real Options Valuation, Real Options Assessment, Black and Scholes, Real Options Pricing) and searching them in two databases, with the purpose of obtaining a wide range of scientific contribution for the analysis. Originality/value: The paper presents an accurate review of the scientific contribution on the topic of the Black and Scholes model; it defines the fields of application, opportunities offered and issues relating to its application, in order to clarify the strong and weak points of the model. Practical implications: The Black and Scholes model of the 1970s is acknowledged to be the most widely used model for evaluating options. Our study shows that this method has been adopted by decision-makers not only for evaluating options but also in other fields.
, 2013 Burnetas & Ritchen; 2005; Henseler & Romer, 2013). Among these methods, the Black and Scholes model is the most widely applied in many fields, not only for evaluating financial options. In order to define a general framework of application and gain a better understanding of this financial evaluation method, its fields of application, the opportunities offered and the relative open issues, the aim of our paper is to formulate an updated conceptualization of the scientific contributions produced over the period between 1999 and 2015 related to business and economic perspectives. The research methodology is of qualitative nature. The study proposes the scientific contribution of the last sixteen years researching four key words (Real Options Evaluation, Real Options Assessment, Black and Scholes, Real Options Pricing) in two database (Google Scholar and EBSCO).
, 2005) ; the management and evaluation of intagible assets  (Bhattacharya & Wright, 2005 , Faiferlick et al. 2004 , Park et al. , 2013) ; and the assessment of bonds and derivatives  (Driffill et al. , 2013; Ericsson & Reneby, 2005; Fabozzi et al. , 2012 , Pyo, 2008; Ren-Raw et al. , 2002; Singh, 2014 , Tompkins, 2001). In the intervening period since the term real options was coined, over 32 years ago, several other approaches have been proposed for calculating the value of real options, producing very extensive literature on the topic, which span a wide range of subjects and fields. Our research demonstrates that the Black and Scholes model and other real options evaluation models have been applied to meet the many needs linked to assessment and prediction of values. Researching the four key words (Real Options Valuation, Real Options Assessment, Black and Scholes, Real Options Pricing) trough the two databases, Google Scholar and EBSCO, the results of 729 product has been obtained.
From the initial number, 263 contributions were extrapolated relating to economic and corporate perspectives. These are shown in the following Tables  1  and 2 , for the two databases. By applying the key words to the Google Scholar databases 240 scientific contributions are identified. The data obtained are processed according to the following Table  1. Among the 240 scientific products, 109 are connected to business and administration, 31 are available and 17 related to more keywords, and a total of 21 scientific contributions are analysed. The following Table  2  shows data concerning the EBSCO databases. We retrieved 480 results from the EBSCO database, of which 154 related to economic and business purpose, 93 were available and 16 related to more keywords. In total, 84 scientific products are analysed.
In addition, 125 contributions are used for defining the state of art, to analyse the existing schools of thought. All 263 contributions relating to economic and corporate purpose are analysed to obtain the quantitative data concerning the type of scientific production. Over the period analysed  (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) , the existing literature can be classified into articles, 144 contributions; books, ten contributions; citations, five contributions; and book chapters, one contribution (Table  3 ). After the first step of analysis, we are able to classify the scholars' contributions according to the topics investigated. Some authors  (Avanzi et al. , 2013; Baduns, 2013)  argued for the utilization of real options theory in the field of decision-making  (Yonggang and Mingli, 2014; McCarter et al.
In this way, the paper provides quantitative data about the production of literature, by identifying the strong and weak points of the literature analysed for the academic community. According to the quantitative data, we are able to present the evolution of literary production during the period between 1999 and 2015. More specifically, we divided the contributions into the following categories: articles, books and citations. By analysing the selected literature, it is possible to identify the main applications of this method within business administration, by offering new spaces to be filled with literary production. The paper presents some limitations: it analysed the literature production between 1999 and 2015 and then only the first six pages obtained from the two databases EBSCO and Google Scholar.
Exome array data availability Illumina HumanExome-12v1_A Beadchip array genotypes for individuals from the 1958BC are available from the European Genome-phenome Archive (EGA, http://www. ebi. ac. uk/ega/) under accession number EGAD00010000234. Similarly, array genotypes and phenotypes for the remaining datasets in this study have been deposited to EGA and are available under accession number EGAS00001001258.
Genotyping and quality control Genotyping was conducted using Illumina HumanExome-12v1_A Beadchips in accordance with the manufacturer's recommendations (Illumina). Calling of genotypes was performed using Illumina GenomeStudio version 2011. 1 software. Cluster boundaries were determined by calling study samples simultaneously. Probes were excluded if monomorphic in all datasets, had a call rate o0. 99 in cases/controls in a series, the difference in uncalled genotypes between cases and controls was statistically significant (Po0. 05), if Hardy-Weinberg in controls Po0. 001, or if non-autosomal (Supplementary Table  1 ). Samples were excluded if the call rate was o0. 99, outlying heterozygosity (43 SD), or if a discrepancy was observed between manifest sex and X-chromosome genotype. To assess the fidelity of genotyping we examined the concordance in 493 individuals from the 1958BC,  15  which had also been sequenced  18  using TruSeq capture in conjunction with Illumina HiSeq2000 technology, and a GATK2 ref.
Statistical and bioinformatic analysis The main statistical and bioinformatics analyses were performed using PLINK v1. 07  (ref. 22)  (Cambridge, MA, USA) and R v3. 0 software (Vienna, Austria). Using the EIGENSOFT v4. 2 smartpca package  23, 24  (Cambridge, MA, USA) we performed PCA to ensure comparability of case and controls. Individuals with non-Western European ancestry were identified and excluded by merging case and control data with 1000 Genomes project data. 100 000 ld-pruned post-QC probes were used to compute eigenvectors in each cohort. Samples exhibiting significant deviations (6 SD) from the main case/control cluster up to the first 10 eigenvectors were classified as outliers and flagged for exclusion. Outlying population structure on the pruned data set was examined using fastSTRUCTURE  25  if subsequent non-comparability was apparent between cases and controls.
49 × 10 -6 (10 045 genes, two tumour types). The power of our study to demonstrate an association for alleles with different MAFs was calculated assuming a multiplicative model. In all analyses a P-value of 0. 05 was considered as representing statistical significance, after adjustment for multiple testing. Gene-set enrichment analysis (GSEA) of pre-ranked SKAT P-values, was performed on gene sets catalogued by the MSigDB v4. 0 database (updated 31 May 2013) using GSEA software 27 adopting default settings. Linkage disequilibrium (LD) r 2 metrics were estimated from UK10K whole-genome data. To restrict our analysis to genes with a higher likelihood of having a role in glioma on the basis of somatic mutation profile in tumours, we used MutSigCV version 1. 4 ref. 28  to identify genes harbouring more non-synonymous mutations than expected by chance given gene size, sequence context and mutation rate.
Thresholding at false discovery rate Qo0. 1 as advocated,  28  MutSig scores were obtained for GBM and non-GBM tumours by interrogation of TCGA (The Cancer Genome Atlas) provisional data sets using cBioPortal. 29 The Variant Effect Predictor (VEP; version 74)  30  was used to predict impact of variants on canonical Ensembl gene transcripts and functional consequences of missense variants according to SIFT,  31  PolyPhen-2 ref. 32  and CONDEL. 33 Computational modelling of the effect of amino acid changes on protein structure was carried out using the project HOPE server. 34 To assess sequence conservation we used GERP  35  and Phast_cons 36 metrics.
ACKNOWLEDGEMENTS We are grateful to all the patients and individuals for their participation and we also thank the clinicians and other hospital staff, cancer registries and study staff in respective centres who contributed to the blood sample and data collection. For the UK study, we acknowledge the participation of the clinicians and other hospital staff, cancer registries, study staff and funders who contributed to the blood sample and data collection for this study as listed in Hepworth et al (BMJ 2006, 332, 883). For the German study, we are indebted to  B Harzheim  (Bonn),  S Ott  and  Dr A Müller-Erkwoh  ( Bonn ) for help with the acquisition of clinical data and  R Mahlberg  ( Bonn ) who provided technical support. The  UK  study made use of control genotyping data generated by the  Wellcome Trust Case-Control Consortium.
CONFLICT OF INTEREST The authors declare no conflict of interest. Heinz-Nixdorf Foundation. Franziska Degenhardt received support from the BONFOR Programme of the University of Bonn, Germany. We are grateful to all investigators who contributed to the generation of this data set. UK10K data generation and access was organised by the UK10K consortium and funded by the Wellcome Trust. The results here are in part based upon data generated by the TCGA Research Network: http://cancergenome. nih. gov/. In the UK study, funding was provided by Cancer Research UK (C1298/A8362 supported by the Bobby Moore Fund), the Wellcome This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Hence, these results suggest that the function of Tet2 is linked to enhancer methylation both during normal hematopoiesis and in malignant cells. Furthermore, we found that Tet2 knockout mouse ES cells also show a pronounced and specific enhancer hypermethylation, extending the role of Tet2 in enhancer methylation to nonhematopoietic and nonmalignant cells (Supplemental Fig. S6 ). Finally, to explore whether enhancer hypermethylation could be observed in human AML patients with TET2 mutations, we took advantage of a publicly available data set (The Cancer Genome Atlas Research Network 2013) in which 194 de novo AML patients were analyzed using the Illumina Infinium 450k methylation array. Patients were filtered based on the mutational status of genes involved in regulation of DNA methylation, and the most variable probes between an AML patient control group having wild-type TET2 (n = 103) and patient groups with either a TET2 mutation (n = 9), a t(8;21) translocation (n = 6), or a DNMT3A mutation (n = 29) were extracted.
Enhancers Active distal enhancers were defined as promoter-distal (nonoverlapping with a region extending ±2. 5 kb from all transcription start sites), overlapping ChIP-seq-enriched regions of H3K27ac and H3K4me1 histone modifications with low mean H3K4me3 enrichment in duplicate samples. The resulting regions separated by <1 kb were merged to avoid redundant detection. This yielded a final high-confidence map of enhancers comprising a total of 8739 in Tet2 fl/fl ;AE cells and 9595 in Tet2 -/-;AE cells. Analysis of methylation state in TET2-deficient AML patients TCGA Illumina Infinium 450k methylation array data and patient mutational analysis data were downloaded from the TCGA homepage (The Cancer Genome Atlas Research Network 2013). Each probe on the array was assigned to a category (gene body, promoter, CpG islands, and enhancers).
Acknowledgments We thank  Kirsten Grønbaek  and the  Helin laboratory  for discussions. We thank  Lotte Frederiksen  and  Anna Fossum  for assistance with histology and flow cytometry, respectively. K. D. R. is supported by a post-doctoral fellowship from the  Danish Medical Research Council  ( 1333-00120B ). This work was supported by the  European Research Council  ( 294666_DNAMET ), the  Danish Cancer Society ,  Danish National Research Foundation  ( DNRF 82 ), the  Danish Council for Strategic Research , and the  Novo Nordisk Foundation  and through a center grant from the  Novo Nordisk Foundation  (the  Novo Nordisk Foundation Section for Stem Cell Biology in Human Disease ).
I. INTRODUCTION In the last few years, breakthroughs in the development of convolutional neural networks have led to impressive state of the art improvements in image categorization and object detection. These breakthroughs are a consequence of the convergence of more powerful hardware, larger datasets, but also new network designs, and enhanced algorithms  [1] ,  [2]. Is it possible to benefit from these progresses for the affective movie content analysis? Large and publicly available datasets composed of movies annotated along affective dimensions start to emerge  [3]  and, even if they are far from being as large as datasets such as ImageNet  [4] , tools exist to benefit from the Convolutional Neural Networks (CNN) frameworks, composed of tens of millions parameters, trained on huge datasets  [1].
In this work, we aim not to maximize absolute performance, but rather to study and compare the performance of four state of the art architectures for the prediction of affective dimensions. It contributes to the affective movie content analysis field as follows: • Benchmark of four state of the art architectures for the prediction of dimensional affective scores: fine-tuned CNN, CNN learned from scratch, SVR and transfer learning. To the best of our knowledge, it is the first approach in the literature using CNNs to predict dimensional affective scores from videos; • Public release of a large dataset composed of 30 movies under Creative Commons licenses that have been continuously annotated along the induced valence and arousal dimensions. The paper is organized as follows. Section II provides background material on continuous movie content analysis work, as well as CNNs and Kernel Methods.
III. CONTINUOUS MOVIE ANNOTATION The LIRIS-ACCEDE dataset proposes 9,800 excerpts extracted from 160 movies  [3]. However, these 9,800 excerpts have been annotated independently, limiting their use for learning models for longer movies where previous scenes may reasonably influence the emotion inference of future ones. Thus, as a first contribution, we set up a new experiment where annotations are collected on long movies, making possible the learning of more psychologically relevant computational models.
A. Movie Selection The aim of this new experiment is to collect continuous annotations on whole movies. To select the movies to be annotated, we simply looked at the movies included in the LIRIS-ACCEDE dataset  1  since they all share the desirable property to be shared under Creative Commons licenses and can thus be freely used and distributed without copyright issues as long as the original creator is credited. The total length of the selected movies was the only constraint. It had to be smaller than eight hours to create an experiment of acceptable duration. The selection process ended with the choice of 30 movies so that their genre, content, language and duration are diverse enough to be representative of the original LIRIS-ACCEDE dataset. The selected videos are between 117 and 4,566 seconds long (mean = 884.