dataesr/scientific-paragraphs-categorization huggingface dataset:
, C. dianellicola is accepted as the only confirmed Cercospora sp. occurring on this host. Gamsia kooimaniorumSandoval-Denis, sp. nov. Colour illustrations. Background, Noud and Robin Kooiman at the collection site; conidiophores bearing annellidic conidiogenous cells and chains of conidia; conidiophores bearing polyblastic conidiogenous cells and conidia in apical clusters. Scale bars = 10 µm. Named for Noud and Robin Kooiman, collectors of the sample from which this fungus was isolated. This species was discovered during a Citizen Science project in the Netherlands, 'Wereldfaam, een schimmel met je eigen naam', describing novel fungal species isolated from Dutch soils. Fungal Planet 706 -20 December 2017 Based on a megablast search using the ITS sequence, the closest matches in NCBIs GenBank nucleotide database were C.
asparagi (GenBank KY549098; Identities 533/536 (99 %), 2 gaps (0 %)), C. malayensis (GenBank MF435168; Identities 533/536 (99 %), 2 gaps (0 %)) and C. glycinicola (GenBank NR_147293; Identities 533 /536 (99 %), 2 gaps (0 %)). The highest similarities using the LSU sequence were C. ischaemi (GenBank KM055432; Identities 828 /830 (99 %), no gaps), C. senecionis-walkeri (GenBank KC677921; Identities 832/836 (99 %), no gaps) and C. sojina (GenBank KX286969; Identities 831/836 (99 %), no gaps). The highest similarities using the actA sequence were C. beticola (GenBank AF443281; Identities 552/618 (89 %), 25 gaps (4 %)), C. sojina (GenBank JQ325008; Identities 540/610 (89 %), 15 gaps (2 %)) and C. malayensis (GenBank KY082664; Identities 540/612 (88 %), 19 gaps (3 %)). The highest similarities using the cmdA sequence were C.
coniogrammes (GenBank KT037466; Identities 374/445 (84 %), 13 gaps (2 %)), C. cf. malloti (GenBank KT193753; Identities 375/446 (84 %), 13 gaps (2 %)) and C. cyperi- cola (GenBank KT193727; Identities 376/447 (84 %), 15 gaps (3 %)). Marcelo Sandoval-Denis, Westerdijk Fungal Biodiversity Institute, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: m. sandoval@westerdijkinstitute. nlEtymology.
Peak calling using uniquely mapped reads, which represent 24%-42% of tags, revealed that Tdg knockdown leads to a strong accumulation of 5fC and 5caC peaks and a slight decrease of 5mC and 5hmC peaks (Fig. 1D ). Surprisingly, 80%-90% of these unique peaks overlap with the RepeatMasker database (RMSK), further supporting our finding that repetitive elements in MEF cells represent the vast majority of cytosine modification targets (Fig. 1E ). In agreement with this, only 198 genes showed relatively marked expression change upon Tdg knockdown (Supplemental Fig. 1d ). As expected, modified cytosines were strongly underrepresented at CGIs (Supplemental Fig. 1e ). Forty-five percent of the 5mC called peaks were found at SINEs (Fig. 1E ), which represent 7. 5% of the mouse genome (Supplemental Fig.
Specific DNA methylation profiles at recently integrated IAP LTRs in differentiated MEFs and pluripotent ESCs In order to perform a comparative analysis of cytosine modification patterns between the repeat elements in differentiated MEFs and pluripotent ESCs, we compared our data on MEFs with reads overlapping with the RMSK database from previously published data sets for ESCs  (Shen et al. 2013). In addition, we reanalyzed them independently. We first characterized the Tdg-deficient-dependent changes in DNA methylation at LTR retrotransposons (also known as endogenous retroviruses [ERVs]) in MEFs. For simplicity, we will collectively further refer to LTR retrotransposons as LTRs. The RMSK database distinguishes between elements corresponding to external domains (LTR ext , containing the regulatory regions of the LTR) from those corresponding to internal domains (LTR int , containing the coding sequences of the proteins, necessary for the life cycle of the integrated viruses) (Fig.
5G ; Supplemental Fig. 5d ). Altogether, these data revealed that the densest CA repeats are the most modified and the closest to TSSs, which point to their potential role in transcription regulation. Bearing this in mind, we next studied the relationship between cytosine modifications, Pol2, and H3K9me3 distributions and the expression level of genes containing or not CA repeats (with a cutoff of [CA]n > 100 bp) (Supplemental Fig. 5e ). Using coding genes from the Ensembl 67 database, we were able to identify 8,595 (CA)n-containing genes and 10,456 (CA)n-free genes. Interestingly, the two classes of genes showed a very distinct Pol2 and H3K9me3 profiles (Fig. 5H-K ). Although the density of Pol2 followed the transcription level, we observed a much stronger accumulation of Pol2 on (CA)n-free genes than (CA)n-containing genes at equivalent transcription level (Fig.
" Peak detection was performed running MACS  (Zhang et al. 2008 ) (http://liulab. dfci. harvard. edu/MACS/) using data sets normalized to 10 million uniquely mapped reads under settings in which the input fraction was used as negative control (effective genome size: 1. 87×10 9 ; tag size: 50; b width: 300; P-value cutoff for peak detection: 1×10 -5 ). Detected peak summits were annotated using HOMER (http://homer. ucsd. edu/homer/ngs). Heatmaps, global clustering, and quantitative comparisons of the ChIP-seq/DIPseq data were performed running seqMINER  (Ye et al. 2011 ) (http://bips. u-strasbg. fr/seqminer/), using data sets representing the average of replicates normalized to 10 million uniquely mapped reads. As reference coordinates, we used the annotated RepeatMasker (RMSK) database (for repetitive elements) or the Ensembl 67 database (for coding genes) of the mouse genome (mm9).
The replicate average densities were normalized in reads per million mapped reads (rpm). In order to characterize the relationship between cytosine modification density and transcription level in repetitive elements, we computed average normalized densities (for DIP-seq data sets) and average transcription level (for RNA-seq data sets) per repetitive element (reads per kilobase per million reads [rpkm] or reads per kilobase per hundred million reads [rpk100m], as indicated). When clustered, the collected values were subjected to k-means clustering coupled to linear-based normalization. The conservation score assigned to each individual repeat element corresponds to the Smith Waterman alignment score provided by the RMSK database.
Repeat analysis For repeat analyses, reads were aligned to repetitive elements in two passes. In the first pass, reads were aligned to the nonmasked mouse reference genome (NCBI37/mm9) using BWA v. 0. 6. 2  (Li and Durbin 2009). Positions of the reads mapped uniquely to the mouse genome were cross-compared with the positions of the repeats extracted from UCSC (RMSK table in the UCSC database for mouse genome mm9), and reads overlapping a repeat sequence were annotated with the repeat family. In the second pass, reads not mapped or multimapped to the mouse genome in the previous pass were aligned to RepBase v. 18. 07  (Jurka et al. 2005 ) repeat sequences for rodent. Reads mapped to a unique repeat family were annotated with their corresponding family name. Finally, we summed up the read counts per repeat family of the two annotation steps.
Data were normalized based upon library size. For enrichment analysis, normalized read counts of DIP samples per repeat family were divided by normalized read counts of matched input samples and expressed as log 2 fold enrichment (average of the two replicates). Significance of the difference of repeat read counts between DIP and input samples was assessed using the Bionconductor package DESeq. To avoid over-or underestimating fold enrichments due to low sequence representation, repeat families with less than 100 mapped reads per DIP sample were excluded from further analysis.
Dinucleotide composition analyses CG and CA heatmaps were generated using custom perl scripts wrapping BEDTools v. 2. 26. 0 utilities  (Quinlan and Hall 2010)  and developed with custom functions. The scripts generated matrices of dinucleotide counts per bin in regions of interest. TreeView v. 3. 0  (Saldanha 2004 ) was then used to plot heatmaps from generated matrices.
Published data sets To calculate normalized density of H2A. Z at L1Md (Fig. 4D ), we used our previously published data sets obtained in MEFs and deposited in GEO under accession number GSE51579  (Obri et al. 2014). To determine the relative enrichment for each cytosine modification at repeat families in control and Tdg-deficient ESCs, we downloaded data sets deposited in GEO under accession number GSE42250  (Shen et al. 2013 ) and performed an independent analysis of reads as described in the "repeat analysis" section.
Acknowledgments We thank  Irwin Davidson  for critical reading of the manuscript and  Yi Zhang  and  Shen Li  for kindly providing us with anti-5fC and anti-5caC antibodies. This work was supported by institutional funds from  CNRS ,  INSERM ,  Université de Strasbourg (UDS) ,  Université de Grenoble Alpes , and by grants from  La Ligue Nationale contre le Cancer Equipe labellisée  (A. H. ),  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences and Health)  within the framework of the Cancer Plan  EPIG201409 ,  INCA  ( INCa_4496  and  INCa_4454 ), and  ANR  (VariZome, contract number  ANR-12-BSV8-0018-01 ). Sequencing was performed by the  IGBMC Microarray and Sequencing platform , a member of the  France Génomique consortium  ( ANR-10-INBS-0009 ).
We create a novel dataset that combines detailed balance sheet information, board composition, data on R&D expenditures, and information on companies' patenting activities. Since patent data for India, in particular for the early 2000s, is not readily available, we compile a database that contains the universe of patent filings with the Indian patent office and match these patents to companies. In order to capture Indian companies' international patenting activity, we also match our sample of Indian companies to U. S. and European patents. India is an appropriate environment to study the effect of interlocks on innovation and patenting for two major reasons. First, besides being one of the largest emerging market economies, it follows a common law legal regime, similar to the U. S. and the United Kingdom.
FIRM-LEVEL DATA We use data for all publicly listed companies in India -including the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) -during the period 2000-2007. We stop our analysis at the end of fiscal year 2007 to avoid the effect of the global financial crisis and a revision of Clause 49 in 2008 on firm behavior. The data comes from PROWESS, which is a firm-level database provided by the Centre for Monitoring Indian Economy (CMIE). For each publicly traded Indian firm, we obtain detailed information on accounting balance sheets, financial statements, industry information, group affiliation for each firm, corporate ownership data and share prices. The database also includes information on R&D. R&D is measured as the total outlay of the company on research and development during the year on its current and capital account.
The Prowess data also provides detailed information on the directors serving on the board of each firm, along with information on the number of board meetings attended, salary, directors' fee etc. The listing of these directors is unique within each time period and we undertake an exhaustive matching exercise to ensure uniqueness across time periods. 9  use a parsimonious specification in our analysis to control for other exogenous firm characteristics. Specifically, we include total book value of assets (in logs) to control for firm size. We also include total exports divided by sales of the company (in logs) to capture the effect of trade on innovation and patenting (Baldwin and Gu, 2004; Melitz and Trefler,   2012). We also include the company's board size to account for scale effects and control directly for the share of non-executive directors on the board.
PATENT DATA For international patent filings with the U. S. Patent and Trademark Office (USPTO) and the European Patent Office (EPO), we rely on EPO's PATSTAT database (version April 2010). 10 ile the USPTO and EPO data are relatively complete, there is serious under-reporting in PATSTAT of Indian domestic patent filings with the Indian Patent Office (CGPDT). We EKASWA contains all domestic patents published between January 1995 and early 2005. 12 nally, we complement the official data sources using the online portal BigPatents India. 13 r analysis focuses on the application date of a patent. 14 However, patent data are only visible after a patent has been published. Given a usual 18-month delay between application and publication date at USPTO, EPO and CGPDT, this implies that we have patent data until March 2009.
15 As an alternative to simple patent counts, we also use the number of citations received by patents. These forward citations are retrieved from a 2015 version of PATSTAT to limit the effect of citation lags. For robustness, we also create citation counts where we limit the gap between the publication date of the cited patent and the priority date of the citing patent to 3, 5, and 10 years. Since the citation data have to be extracted from PATSTAT, they are only available for filings with the USPTO and the EPO; so far no citation data for Indian patents are available. To construct technological similarity measures between firms, we also extract IPC codes. IPC codes for U. S. and European patents are extracted from PATSTAT, while IPC codes for Indian filings are retrieved directly from the Indian patent office's website.
Due to the absence of a unique identifier shared by the firm-level and patent data, the main problem in constructing our dataset consists in matching patents to firms. To match assignee names to company names, we rely on a combination of an automated matching algorithm and extensive manual checking of the (un)matched data. We also cross-check matched domestic Indian, USPTO, and EPO patents using 'equivalents', that is, we verify whether, for example, a given matched USPTO patent has an EPO equivalent for the same innovation and whether this EPO equivalent had been matched (and vice versa). 16 Section C of the online appendix explains the matching algorithm and outcome in more detail.
EMPIRICAL SPECIFICATION: IV APPROACH We first begin by using the complete set of firms in our dataset and implementing an Instrumental Variables (IV) approach. Later, we discuss two more robust identification strategies that account for concerns over endogeneity with the IV approach. However, these robust identification strategies rely only on subsets of the data. We construct our instrument based on the interaction term, (B i × R t ), where B i takes the value one if a firm was a below-threshold firm. i. e. , their board structure did not meet the requirements as set out in Clause 49, and R t is a dummy variable taking the value one for years where the reform was applicable, which varies by groups A, B, and C. We expect the net effect of our instrument on network size to be negative because below-threshold firms experienced a smaller spillover effect and, as a result, a smaller increase in their network size than above-threshold firms.
Column (4), (  5 ) and (  6 ) reports the second stage of the IV regression where the dependent variable is the total number of patents (international and domestic), domestic patents and international patents respectively. The regression includes controls for Tobin's Q, firm and year fixed effects. C1  shows the resulting matching rates. The Indian patent data was compiled in different ways. EKASWA provided us with the complete set of assignees and patents which we matched to our sample of PROWESS firms. iPairs and BigPatents India provide online access to the data. We downloaded all assignee names from iPairs and matched them to our sample of firms and then downloaded only those patents for the matched iPairs assignee names. When downloading the patent data, we checked that at least one inventor on a given patent document indicates an Indian residence.
This increases the likelihood that a given patent was filed by the headquarter/subsidiary in India. We also trawled BigPatents India for companies in our PROWESS dataset and obtained additional patents in this way. Moreover, we cross-checked our USPTO, EPO and Indian patent data using equivalents to ensure that the match is consistent. This implies that if, for example, a matched USPTO patent has an Indian equivalent, we verify that we also matched the Indian equivalent to its owner. R&D Current R&D Capital R&D Patent Count India Patent Int. Pat (Tobit) (Tobit) (Tobit) (1) (2) (3) (4) (5) (6) R&D Current R&D Capital R&D Patent Count India Patent Int. Patent (Tobit) (Tobit) (Tobit) (1) (2) (3) (4) (5) (6) ix.
Summary StatisticsThis table reports summary statistics of variables used in our regressions for the full sample of firms over the years2000-2007. The sample consists of 11,358 firm-year observations. Patent data are extracted from USPTO, EPO (PATSTAT version April 2010), EKASWA, iPairs, and BigPatents India. All firm level variables are obtained from the PROWESS database. † , all figures reported for these variables are in 10 millions (crores) of Indian Rupees. Only firms listed on the BSE are part of the sample. We report the mean, standard deviation and the range (minimum, maximum) of each variable.
Summary Statistics by category -before and after the reform This table reports summary statistics of variables used in our regressions for the full sample of firms over the years 2000-2007. We report the mean of each variable and distinguish among 3 dimensions: (1) company Group A, Group B, and Group C, (2) companies below and above the required threshold introduced by the reform, as well as (3) before and after the reform took effect. Patent data are extracted from USPTO, EPO (PATSTAT version April 2010), EKASWA, iPairs, and BigPatents India. All firm level variables are obtained from the PROWESS database. Only firms listed on the BSE are part of the sample.
This figure shows the official document that all firms listed on the BSE were required to complete as per Clause 49A. Summary Statistics by category -before and after the reform where variables are demeaned using industry-level average This table reports summary statistics of variables used in our regressions for the full sample of firms over the years 2000-2007. We report the mean of each variable after subtracting the industry-level mean, across three groups: (1) company Group A, Group B, and Group C, (2) companies below and above the required threshold introduced by the reform, as well as (3) before and after the reform took effect. Patent data are extracted from USPTO, EPO (PATSTAT version April 2010), EKASWA, iPairs, and BigPatents India. All firm level variables are obtained from the PROWESS database.
e. the number of other firms with whom a given firm shares common directors; Average co-directorships measures how 'busy' firm's board is, i. e, the average number of firms on whose boards each director of the firm serves; Tobin's Q is proxied by the M/B ratio; Total assets in logs is total book value of assets; Log Exports/Sales is total exports of the company divided by its total sales; Aggr. Industry Patents is the aggregate patents count normalized by R&D expenditure within each industry; Aggr. Business Group Patents is the aggregate patents count normalized by R&D expenditure within each business group. Standard errors clustered by firm are reported in parentheses. The original PROWESS dataset contains 25,404 unique firm names. After cleaning and standardizing, we have 25,319 unique firm names which are matched with the assignee names of CGPDT, USPTO, and EPO patents.
EPO and USPTO patent data come from EPO's PATSTAT database whereas filings with the Indian patent office come from three sources as explained in Section 3. 2 in the main text: iPairs, EKASWA, and BigPatents India. The USPTO, and EPO patent files contain 1,431, and 705 unique assignee names of Indian residents respectively. The assignees contain a large range of different assignee types, including private individuals, universities, and research institutes. We attempt to keep only private and state-owned companies because none of the other assignee types is contained in PROWESS. After dropping any assignees that are not private or state-owned companies, cleaning/standardizing assignee names, and keeping only patents applied for between 1990-2008, we obtain 375 and 314 unique names in the USPTO and EPO patent files, respectively.
Matched datasetThis table shows the matching rates between the various patent databases. USPTO and EPO patents are extracted from PATSTAT version April 2010. CGPDT patents are from EKASWA, iPairs, and BigPatents India. The original PROWESS dataset contains 25,404 unique firm names. After cleaning and standardizing, we have 25,319 unique firm names which are matched with the assignee names of CGPDT, USPTO, and EPO patents. The USPTO, and EPO patent files contain 1,431, and 705 unique assignee names of Indian residents respectively. After dropping any assignees that are not private or state-owned companies, cleaning/standardizing assignee names, and keeping only patents applied for between 1990-2008, we obtain 375 and 314 unique names in the USPTO and EPO patent files, respectively. These assignees correspond to 1,489 and 1,717 patent filings respectively.
ACKNOWLEDGEMENTS We would like to thank the editor and two anonymous referees for their very constructive feedback which has greatly improved the paper. We also thank  Cláudia Custódio ,  Naveen Daniel ,  Ming Fang ,  Bronwyn Hall ,  Gimmy Moore ,  Lalitha Naveen ,  Georg von Graevenitz ,  Subrata Sarkar , and seminar participants at the 2013 IGIDR Emerging Markets conference, the 2014 European Finance Association Meetings, the 2014 Financial Management Association meetings, the 8th  Conference on Micro Evidence on Innovation and Development (MEIDE) ,  Cyprus University of Technology, IIM Bangalore, LMU Munich, NSE India , and the  Universidad Carlos III Madrid  for helpful comments.
SUPPLEMENTARY TABLES ivTable B1 : B1 This figure shows the official document that all firms listed on the BSE were required to complete as per Clause 49A. Summary Statistics by category -before and after the reform where variables are demeaned using industry-level average This table reports summary statistics of variables used in our regressions for the full sample of firms over the years 2000-2007. We report the mean of each variable after subtracting the industry-level mean, across three groups: (1) company Group A, Group B, and Group C, (2) companies below and above the required threshold introduced by the reform, as well as (3) before and after the reform took effect. Patent data are extracted from USPTO, EPO (PATSTAT version April 2010), EKASWA, iPairs, and BigPatents India. All firm level variables are obtained from the PROWESS database.
, 2012). For all these reasons, the SPNA has been the target of several projects and broadly sampled by oceanographic cruises. As part of the OVIDE project (http://www. umr-lops. fr/Projets/ Projets-actifs/OVIDE), the OVIDE section has been sampled biennially in summer since 2002 to collect data related to the circulation and the carbon cycle. Its path between Greenland and Portugal is shown in Fig. 1  along with a schematic view of the upper, intermediate, and deep circulations in the SPNA Published by Copernicus Publications on behalf of the European Geosciences Union. P. Zunino et al. :  The GEOVIDE cruise in May-June 2014 adapted from  Daniault et al. (2016) , which will be referred to as D2016 hereafter. The international GEOTRACES program (http://www. geotraces. org/) aims to characterize the trace elements and their isotopes (TEIs) in the world oceans.