dataesr/scientific-paragraphs-categorization huggingface dataset:
This study as also supported by grants from the  National Institute on Aging :  AG08122  and  AG033193  (Seshadri). Drs. Seshadri  and DeStefano were also supported by additional grants from the  National Institute on Aging : ( R01 AG16495 ;  AG031287 ,  AG033040 ), the  National Institute of Neurological Disorders and Stroke  ( R01 NS17950 ), and the  National Heart, Lung and Blood Institute  ( U01 HL096917 ,  HL093029  and  K24HL038444 ,  RC2-HL102419  and  UC2 HL103010. FundacioACE would like to thank patients and controls who participated in this project. This work has been funded by the  Fundacion Alzheimur (Murcia) , the  Ministerio de Educacion y Ciencia  ( PCT-010000-2007-18 ), ( DEX-580000-2008-4 ), ( Gobierno de Espan ˜a ),  Corporacion Tecnologica de Andalucı ´a  ( 08/211 ) and  Agencia IDEA (841318)  ( Consejerı ´a de Innovacion, Junta de Andalucı ´a ).
The authors thank to  Ms. Trinitat Port-Carbo  ´and her family for their generous support of FundacioACE research programs. The Rotterdam Study: The  Rotterdam Study  was funded by  Erasmus Medical Center  and  Erasmus University, Rotterdam ; the  Netherlands Organization for Health Research and Development ; the  Research Institute for Diseases in the Elderly ; the  Ministry of Education, Culture and Science ; the Ministry for Health, Welfare and Sports; the  European Commission ;and the  Municipality of Rotterdam ; by grants from the  Research Institute for Diseases in the Elderly  ( 014-93-015 ;  RIDE2 ),  Internationale Stichting Alzheimer Onderzoek, Hersenstichting Nederland , the  Netherlands Genomics Initiative-Netherlands Organization for Scientific Research (Center for Medical Systems Biology  and the  Netherlands Consortium for Healthy Aging) , the  Seventh Framework Program  ( FP7/2007-2013 ), the  ENGAGE  project (grant agreement  HEALTH-F4-2007-201413 ),  MRACE-grant  from the  Erasmus Medical Center , the  Netherlands Organization for Health Research and Development (ZonMW Veni -grant no.
Patient recruitment for the  MRC Prion Unit/ UCL Department of Neurodegenerative Disease collection  was supported by the  UCLH/UCL Biomedical Centre  and their work was supported by the  NIHR Queen Square Dementia BRU. LASER-AD was funded by  Lundbeck SA. The Bonn group would like to thank  Dr. Heike Koelsch  for her scientific support. The Bonn group was funded by the  German Federal Ministry of Education and Research (BMBF) :  Competence Network Dementia (CND)  grant number  01GI0102 ,  01GI0711 ,  01GI0420. The AgeCoDe study group was supported by the  German Federal Ministry for Education and Research  grants  01 GI 0710 ,  01 GI 0712 ,  01 GI 0713 ,  01 GI 0714 ,  01 GI 0715 ,  01 GI 0716 ,  01 GI 0717. The Homburg group was funded by the  German Federal Ministry of Education and Research (BMBF) :  German National Genome Research Network (NGFN) ;  Alzheimer's disease Integrated Genome Research Network ; AD-IG:  01GS0465.
Genotyping of the Bonn case-control sample was funded by the  German centre for Neurodegenerative Diseases (DZNE) ,  Germany. The  GERAD Consortium  also used samples ascertained by the  NIMH AD Genetics Initiative. Harald Hampel  was supported by a grant of the  Katharina-Hardt-Foundation ,  Bad Homburg vor der Ho ¨he, Germany. The KORA F4 studies were financed by  Helmholtz Zentrum Mu  ¨nchen; German Research Center for Environmental Health; BMBF;  German National Genome Research Network  and the  Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the  Heinz Nixdorf Foundation  ( Dr. Jur. G. Schmidt , Chairman) and  BMBF. Coriell Cell Repositories is supported by  NINDS  and the  Intramural Research Program  of the  National Institute on Aging. The authors acknowledge use of genotype data from the 1958 Birth Cohort collection, funded by the  MRC  and the  Wellcome Trust  which was genotyped by the  Wellcome Trust Case Control Consortium  and the  Type-1 Diabetes Genetics Consortium , sponsored by the  National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International.
The  Kathleen Price Bryan Brain Bank at Duke University Medical Center  is funded by  NINDS  grant #  NS39764 ,  NIMH MH60451  and by  Glaxo Smith Kline. Genotyping  of the TGEN2 cohort was supported by  Kronos Science. The TGen series was also funded by  NIA  grant  AG034504  to AJM, The  Banner Alzheimer's Foundation ,  The Johnnie B. Byrd Sr. Alzheimer's Institute , the  Medical Research Council , and the state of  Arizona  and also includes samples from the following sites:  Newcastle Brain Tissue Resource  (funding via the  Medical Research Council , local NHS trusts and  Newcastle University) ,  MRC London Brain Bank for Neurodegenerative Diseases  (funding via the  Medical Research Council) ,  South West Dementia Brain Bank  (funding via numerous sources including the  Higher Education Funding Council for England (HEFCE) ,  Alzheimer's Research Trust (ART) ,  BRACE  as well as  North Bristol NHS Trust Research and Innovation Department and DeNDRoN) ,  The Netherlands Brain Bank  Acknowledgments This work was made possible by the generous participation of the control subjects, the patients and their families.
Background: Extant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes. Results: Here we describe PhylDiag, a software that identifies statistically significant synteny blocks in pairwise comparisons of eukaryote genomes. Compared to previous methods, PhylDiag uses gene trees to define gene homologies, thus allowing gene deletions to be considered as events that may break the synteny. PhylDiag also accounts for gene orientations, blocks of tandem duplicates and lineage specific de novo gene births.
Implementation The complete algorithm has been implemented in Python. Pairwise comparisons of chromosomes are performed in parallel since they are independent. In Algorithm 1, the MHP matrix is stored considering that it is a sparse matrix to reduce memory usage and the merging process is optimised. Combinations in probability formulas are computed using Pascal's rule and dynamic programming. On a single 3,0 GHz processor with 32 Gb RAM, loading the data in memory requires 3 seconds, and the running time for the pairwise analysis of the Human and Mouse genomes requires less than 3 seconds. Without any optimisation of the memory allocations the peak of RAM consumption is 221 Mb, thus a standard personal computer can run PhylDiag.
Results To evaluate the performances of PhylDiag, we performed a comparative analysis with i-ADHoRe 3. 0  [14] , a state-ofthe-art algorithm used in many recent studies. To make comparisons possible however, we used a version of the program provided by the authors. Indeed, i-ADHoRe 3. 0 first rewrites genomes in tbs like PhylDiag, but allows a user-defined "tandem_gap" between genes in a tb. In version 3. 0, the minimal tandem_gap is 2, and it is not possible to set the tandem_gap to 0, as in PhylDiag. In the version provided (i-ADHoRe 3. 0. 2a) this option is enabled. When ADHoRe compares two chromosomes, it first generates "baseclusters" which correspond to PhylDiag's sbs. ADHoRe uses the DPD metric to build baseclusters containing gaps ≤"gap_size" in the matrix of homologies. ADHoRe also uses the "prob_cutoff " parameter for the statistical filtering and a last parameter is the "q_value", a real value between 0 and 1, indicating the minimum r 2 (a measure for the linearity of baseclusters in the matrix of homologies) that a cluster should display.
Furthermore, a meticulous attention to tandem duplicates and gene orientations allow PhylDiag to reach a high resolution in the analysis of rearrangements, down to single gene inversion. Finally, the statistical validation of putative synteny blocks filters out putative false positives due to randomly convergent gene order. PhylDiag is a software for synteny block inference that benefits from extensive parameters, including gap max , distance metric, p-value threshold, filtering of extant genomes and ancestor for the gene family definition. Their values can be set by PhylDiag (default values are based on previous benchmarks or set automatically based on the data) or set by the user. These features, together with postprocessing graphical analysing tools and printed statistics (number of tandem duplicates in extant genomes, number of dispersed duplicates, number of homologies involved in the pairwise comparison) contribute to making PhylDiag a user-friendly method to find synteny blocks.
Acknowledgements We thank  Alexandra Louis  and  Pierre Vincens  for their help with computer facilities and  Wassim Abou-Jaoudé  for his assistance with mathematical problems. We also thank  Yves Van de Peer ,  Klaas Vandepoele  and  Jan Fostier , the authors of ADHoRe who provided the version i-ADHoRe 3. 0. 2a. This work is funded by  Centre National de la Recherche Scientifique (CNRS)  and grants from the  Agence Nationale de la Recherche (ANR)  [ Ancestrome  Project  ANR-10-BINF-01-03 ,  ANR Blanc-PAGE   ANR-2011-BSV6-00801 ].
The two genomes had similar GϩC contents of 65. 8%. Genome annotation was performed by the NBCI Prokaryotic Genome Annotation Pipeline (http://www. ncbi. nlm. nih. gov/genome/annotation _prok/) and predicted 6,646 genes, 4 rRNAs, and 61 tRNAs for DHS01 and 6,807 genes, 4 rRNAs, and 55 tRNAs for DHS29. Comparison of the two genomes with Mummer software (5) revealed 1,446 single nucleotide polymorphisms (SNPs) and 1,197 insertions/deletions (indels). We determined the specific regions of the two isolates using a cross-mapping of raw reads and assembled contigs using the Maximum Entropy Method (MEM) algorithm of the BWA software  (6). Seventeen genes were specific to DHS01 and 136 to DHS29. A gene encoding a 2=-aminoglycoside nucleotidyltransferase was present in the aminoglycosideresistant DHS01 and absent from the aminoglycoside-susceptible DHS29.
The DHS29-specific genes were grouped into three clusters, of which two were similar to the regions pa1247-pa1292 and pa1381-pa1449 of the reference strain PAO1  (7) , and included the coenzyme B12 biosynthesis gene cluster, the flagellum biosynthesis cluster fli, and the quorum-sensing-associated genes lasR, rsaL, and lasI. The absence of lasR in DHS01 was consistent with the iridescent sheen surface of its colonies on rich agar media  (8). We show here that during its spread into a hospital setting, an epidemic clone of P. aeruginosa not only accumulates numerous SNPs and indels but also acquires or loses gene clusters. Nucleotide sequence accession numbers. These wholegenome shotgun projects of P. aeruginosa strain DHS01 and DHS29 have been deposited at DDBJ/EMBL/GenBank under the accession numbers AYNE00000000 and AYON00000000, respectively.
TERA explores all reconciled gene trees that can be amalgamated from a sample of gene trees. Using a large scale simulated dataset, we demonstrate that TERA achieves the same accuracy as the corresponding probabilistic method while being faster, and outperforms other parsimony-based methods in both accuracy and speed. Running TERA on a set of 1099 homologous gene families from complete cyanobacterial genomes, we find that incorporating knowledge of the species tree results in a two thirds reduction in the number of apparent transfer events.
Implementation and validation TERA is implemented in Cþþ and is freely available from http:// mbb. univ-montp2. fr/MBB/download_sources/16__TERA. Posterior samples of gene trees, for both simulated and real alignments, were downloaded together with the 'true' gene trees used to simulate alignments from the dryad data repository (doi:10. 5061/dryad. pv6df) provided by  Szo ¨llo †si et al. (2013b). For all the parsimony-based species tree aware methods, including TERA, we used the DTL costs d ¼ 2, s ¼ 3, k ¼ 1 obtained by  David and Alm (2010)  using a criteria based on minimizing the change in ancestral genome sizes on a large biological dataset. We ran TreeFix-DTL with default parameters, JTT/GTR with a gamma distribution as models of evolution, and as a starting tree the PhyML tree. MowgliNNI was run with default parameters, a threshold of 50 for weak edges, and with the PhyML tree-with bootstrap values-as a starting gene tree.
AnGST was run with default parameters using the dated species tree on samples of 1000 gene trees, whereas JPrIME-DLTRS was run with JTT with a gamma distribution as model of evolution, 100 000 iterations, a thinning factor of 10 and a time out of 10 h. Finally, we ran TERA with a starting c A of 0. 1 and for G samples from 10 up to 10 000 gene trees for each simulated alignment. The gene trees reconstructed by ALE were downloaded from the above mentioned data repository. Note that, from a practical perspective, the DTL costs we use are the default parameters for all the parsimonious methods described in the article, and seem to work well for several parts of the Tree of Life. If the user suspects that these values are not suited for the analyses, these parameters should be estimated beforehand, e.
Results To test the accuracy of gene trees reconstructed using TERA we chose a dataset based on 1099 homologous gene families present in 36 cyanobacterial genomes. This dataset, published in Szo ¨llo †si et al. (2013b), was constructed using homologous families from the HOGENOM database  (Penel et al. , 2009)  and contains both real and simulated alignments as well as the gene trees used to simulate sequences. The mean number of genes per family in this dataset is 36. 66, the largest family has 114 genes and the smallest 21 genes; the mean number of species in which a family is found is 31. 49, with a minimum of 4 and a maximum of 36; the mean copy number per genome-counting as zero genomes in which a family is absent-is 1. 012, with a minimum of 0. 5833 and a maximum of 3. 17. We chose this dataset, because (i) it contains a diverse set of gene families from a reasonably large and divergent set of species, and (ii) the parametric bootstrap-like simulation procedure used attempts to retain as much of the complexity of the underlying biological dataset as possible  (Szo ¨llo †si et al.
Validation on simulated data For the simulated alignments, both the 'true' gene tree used to generate the sequences and the species tree-along which the gene trees evolved-are known. Consequently, it is possible to directly assess the accuracy of different reconstruction methods in recovering the correct gene tree. As shown in Figure  1a  in the online appendix, the number of possible amalgamations increases roughly exponentially with increasing sample size in the simulated dataset, but the median reconstruction accuracy achieved by TERA begins to saturate (Figure  1b  in the online appendix). To compare the accuracy of our method to that of others, we reconstructed gene trees using six different 'species tree aware' methods: (i) the TERA algorithm described here, (ii) ALE (Szo ¨llo †si et al.
1) and is significantly more accurate than TreeFix-DTL  (Bansal et al. , 2014 , submitted for publication) (paired Wilcoxon test P < 10 À8 ) as well as the other species tree aware method MowgliNNI  (Nguyen et al. , 2013). TERA also outperformed jPrIME-DLTRS, although the accuracy of the latter may have been limited by the available run time (recall that a time out of 10 h per each data set was given). For an input of 1000 samples, TERA is less accurate than either TERA or ALE with 10 000 input samples (paired Wilcoxon tests P < 10 À8 ), statistically indistinguishable from jPrIME-DLTRS, slightly more accurate then TreeFix-DTL (paired Wilcoxon test P ¼ 0. 026), and still significantly more accurate than MowgliNNI and AnGST (paired Wilcoxon tests P < 10 À8 ). Results for AnGST are only shown for sample sizes of 1000 gene trees, due to the very large memory requirement of the AnGST implementation.
To investigate the effect of using a joint sequence-reconciliation score we also ran TERA with c A ¼ 0, i. e. emulating AnGST in only optimizing the reconciliation score. We found that on a sample of 1000 trees AnGST was more accurate then TERA with c A ¼ 0 with an n-R-F of, 0. 156 and 0. 166, respectively. However, using TERA with only 1000 samples, but estimating c A , resulted in a mean n-R-F of 0. 146. The average c A estimated by TERA was 0. 49 while the average N A was 6. 57. An important difference of TreeFix-DTL compared with the other methods considered here, is that it does not use information of the time order of speciation events in the species tree (note that AnGST can also run without information on time ordering). Therefore TreeFix-DTL uses less information, which may explain the difference in performance in comparison to TERA on the simulated dataset.
Nonetheless, we ran TERA with 10 random time orderings of the species tree and this resulted in statistically identical n-R-F values when using the correct time order of speciations (Wilcoxon rank sum test P ¼ 0. 6). A potential concern regarding methods that optimize a joint reconciliation-sequence score is that we may overfit the species tree. If overfitting of the species tree occurs we expect the reconstructed gene trees to become too similar to the species tree. In the context of the simulated dataset used here, we expect that the reconstructed gene trees will become more similar to the species tree than the true trees used to simulate alignments. To test for such a signal of overfitting, we require a measure of similarity between gene trees and the species tree. The most straightforward solution is to restrict our analysis to gene families that have exactly one copy in each species.
In this case, we can simply use the n-R-F distance between the species tree and each of the gene trees as our similarity measure. In Figure  2b , we show the results for the 431 single copy universal gene families in our simulated dataset. We measure the extent of over and underfitting as the difference in n-R-F distance between the species tree and the reconstructed gene tree and the n-R-F distance between the species tree and the true gene tree. We observe that the species tree unaware method, PhyML, as expected, reconstructs trees that are more distant to the species tree than the true tree. The results for the species tree aware methods are more variable: ALE, based on an explicit probabilistic approach, exhibits a median difference of zero and produces only a few examples of overfitting.
Results on real data In order to test TERA on biological data, we again used the dataset published in Szo ¨llo †si et al. (2013b), but focusing on real alignments. As inputs to TERA we used: (i) the tree samples obtained from real alignments and (ii) the ML species tree unaware gene trees obtained using PhyML from the same alignments. Similar to the results of ALE (Szo ¨llo †si et al. , 2013b), we find that the number of transfer and loss events (but not duplication events) in most parsimonious reconciled gene trees is substantially lower than those found in the most parsimonious reconciliations of PhyML trees: the mean and median number of transfers per family was 3. 914 and 3 compared with 10. 38 and 9, respectively; the mean and median number of losses per family was 5. 088 and 4 compared with 7.
Discussion We have presented a detailed description of the TERA algorithm, a parsimony-based species tree aware method of gene tree reconstruction. We demonstrate that TERA reconstructs gene trees with nearly identical accuracy as the more complex ML based ALE method and, at least on the simulated datasets considered here, outperforms the other parsimony based species tree aware methods. Examining a subset of single copy universal gene families we show that TERA does not overfit or underfit the species tree. This result lends credibility to TERA's results on biological data, whereby two thirds of apparent gene transfers in gene trees reconstructed without taking into consideration the species tree are not recovered given knowledge of the species phylogeny. Although parsimony based methods are fundamentally limited in some aspects compared with model based probabilistic methods, in the case of species tree aware gene tree reconstruction our results indicate that parsimony based methods can closely approach their accuracy.
Funding C. S. and E. J. were supported by the  French Agence Nationale de la Recherche Investissements d'avenir/ioinformatique  ( ANR-10-BINF-01-02 , Ancestrome). G. J. Sz. was supported by the  Marie Curie CIG 618438 'Genestory' and the Albert Szent-Gyo ¨rgyi Call-Home Researcher Scholarship   A1-SZGYA-FOK-13-0005  supported by the  European Union  and the  State of Hungary , cofinanced by the  European Social Fund  in the framework of  TA ´MOP 4. 2. 4. A/ 1-11-1-2012-0001  ' National Excellence Program '. This publication is contribution no. 2014-178  of the  Institut des Sciences de l'Evolution de Montpellier [ISEM ,  UMR 5554 ].
cdc. gov/EID/ article/20/9/13-0225-Techapp1. pdf). The 29 strains were MSSA and belonged to ST398 (n = 25) or to a new ST submitted to the MLST Database (http://www. mlst. net/) as ST2658 (n = 4). ST398 and CC398 were the most prevalent genotype and clonal complex identified: 25/125 (20%) and 29/125 (23. 2%) isolates, respectively (Figure  2 ). The prevalence of MSSA CC398 carriage was 3. 2% (2/63) in HCWs and 5. 6% (5/89) in patients. The prevalence of MSSA CC398 infection was 2. 25% (2/89 patients) (Figure  1 ). These patients were hospitalized during the same period; nosocomial pneumonia developed after nasal colonization, and was associated with bacteremia in 1 case. Demographic and clinical characteristics were similar in patients colonized or infected with MSSA CC398 or with other genotypes (Table  1 ).
frequently in the ICU environment (15/36, 41. 7%) than in patients (5/33, 15. 2%; χ 2 4. 7, p = 0. 03) and HCWs (2/26, 7. 7%; χ 2 7. 1, p = 0. 007) (online Technical Appendix). Molecular typing of CC398 strains and microbiologic results are shown in Table  2. Four strains belonged to the new ST2658, which differed from ST398 by a synonymous mutation (A→G) at position 198 of the pta gene. These 4 strains were isolated from nasal carriage samples (n = 2), bronchial colonization samples (n = 1), and pneumonia testing samples (n = 1) from 2 patients hospitalized at the same time. All MSSA CC398 strains were agr type 1, spa type t571 (determined by using DNAGear software; http://w3. ualg. pt/~hshah/DNAGear/), and DLST type 144-186 (DLST spa 186 corresponding to spa type t571). Genes encoding Panton-Valentine leukocidin, toxic shock syndrome toxin 1, and staphylococcal enterotoxin A were not detected.
Acknowledgments We thank the  Institut de Recherche pour le Développement , the  Centre National de la Recherche Scientifique , the  University of Montpellier 1, and the Laboratory of Bacteriology (Arnaud de Villeneuve Hospital, Montpellier ) for their technical support; and HCWs, patients, and patient families in the medical intensive care unit ( Gui de Chauliac Hospital, Montpellier ) for participating in the study. This study was supported by a grant from the  Association pour l'Assistance et la Réhabilitation à Domicile ), Montpellier; and the  Institut de Recherche pour le Développement , the  Centre National de la Recherche Scientifique , the  University  of.