dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we sequenced the same three individuals using NGS and analysed the data using bioinformatics pipelines (Table  1 ). By comparing the results with the Sanger data, we assessed the impact of SNP calling methods on diversity results. For this, we made the assumption that the Sanger sequencing gives the correct calling and that any discrepancy between Sanger and NGS data is due to NGS sequencing or calling errors. Sanger data were aligned to the available yam chloroplast reference (NC_024170. 1) using GENEIOUS PRO 4. 7. 6  (Kearse et al. 2012)  with default values of the GENEIOUS ALIGNMENT tool. For NGS data from WGS, SNP calling was performed using a combination of three different software programs with different options, leading to nine different SNP calling methods:   2 ). 1 GATK v3. 3-0- The full scripts used to perform the bioinformatics analyses are available in Supporting information.
We only kept the positions where a Sanger sequence was available, and we recorded all the discrepancies between Sanger and NGS data for each individual. We refer to a false negative when NGS data do not see an alternate homozygote found with Sanger sequence and inversely a false positive when NGS data identify an alternate homozygote not observed by Sanger sequence. False polymorphism refers to a intra-individual polymorphic genotype from NGS data instead of a homozygote alternate or reference allele from Sanger sequence. We calculated the percentage of error as the number of disagreements between Sanger and NGS divided by the total number of genotypes (identical + different) over all three samples and SNPs positions. We also calculated a special case of the previous measure as the percentage of intra-individual polymorphism error, that is the number of disagreements with intra-individual polymorphic calls divided by the total number of genotypes.
SNP calling is a critical step when analysing NGS data. Different methods have been proposed and tested to minimize the number of false positives and false negatives  (Nielsen et al. 2011; Liu et al. 2013; Warden et al. 2014). Several studies have already compared the use of different software for SNP calling, but results are contentious. For human data, for example, GATK usually outperformed SAMTOOLS but the more accurate algorithm is either UnifiedGenotyper  (Cornish & Guda in press)  or HaplotypeCaller  (Pirooznia et al. 2014). We could not generalize human DNA analysis to our specific case, because we are dealing with a haploid genome. Still, we need to obtain as few intra-individual polymorphic positions as possible because, as we showed earlier, these intra-individual polymorphic positions are mainly an artefact of plastid DNA transfers.
Acknowledgements This project was supported by  Agropolis Foundation  through the  "Investissements d'avenir" program  ( ANR-10-LABX-0001-01 ) under the reference ID  1202-040  (CHLORODIV) to  T. Couvreur  and the  Agence Nationale de Recherche  ( ANR,  project  AFRICROP ANR-13-BSV7-0017 ) to  Y. Vigouroux.
Data accessibility All sequences are available on either GenBank (Sanger fasta sequences), NCBI-SRA (. fastq files) or DRYAD DOI 10. 5061/dryad. 31733 (aligned. bam files) as listed in Supporting information. Moreover, the. vcf files with SNP calling are also available on DRYAD DOI: 10. 5061/ dryad. 31733 (as listed in Supporting information).
Evidence for htrA representing an essential gene in H. pylori To obtain final proof that htrA is an indispensible gene and to investigate if its serine protease activity is necessary for H. pylori, we next focused on experiments using pharmacological inhibition of HtrA. We have recently performed a structure-based virtual screening for small-molecule inhibitors of HtrA in H. pylori and C. jejuni. After screening of compound databases and biochemical tests, we identified HtrA inhibitor compound-1 as a novel small-molecule inhibitor that efficiently blocked E-cadherin cleavage in vitro  (Perna et al. , 2015)  (Fig. 7A ). When H. pylori HtrA was incubated with recombinant E-cadherin and increasing amounts of the inhibitor, 50 μM concentration was sufficient to reduce HtrA-mediated E-cadherin cleavage significantly  (Perna et al.
A. Total RNA was isolated from H. pylori strain 26695 grown either to mid-exponential growth phase (ML) or under acidic stress conditions (AS). DNase I-treated RNA samples were used for the construction of cDNA library pairs: one denoted as (-), in which the total RNA was left untreated, and the other (+), in which total RNA was treated with terminator exonuclease treatment (TEX). High-throughput sequencing of cDNA libraries was performed on either the Roche 454-or Illumina sequencing platform. The ML -/+ TEX and AS -/+ TEX 454 datasets were previously published  (Sharma et al. , 2010). New ML-/+ Illumina data were generated to increase sequencing coverage (26695:  Bischler et al. , 2015; G27: S. Pernitzsch and C. Sharma, unpublished). The generated cDNA sequencing reads without [black, (-) libraries] or with [red, (+) libraries] terminator exonuclease treatment were mapped to the htrA locus in the H.
Therefore, RNA was either left untreated or subjected to terminator-5′-phosphatedependent exonuclease (TEX, Epicentre Biotechnologies) treatment prior to cDNA library construction. In general, cDNA libraries were generated by vertis Biotechnology AG, Germany, as described by  Sharma et al. (2010). Following quality control, cDNA libraries were sequenced using either an Illumina Genome Analyser IIx or HiSeq2000 sequencer. Computational analysis of raw sequencing data was performed by the automated RNA-seq processing pipeline READemption  (Förstner et al. , 2014). For data visualisation, graphs representing the number of mapped reads per nucleotide were calculated and visualised using the Integrated Genome Browser (IGB) version 6. 5. 3 software from Affymetrix (http://genoviz. sourceforge. net/).
Acknowledgements This work was supported by a grant of the  German Science Foundation  (project  B10  in  CRC-796  and  A4 in CRC-1181 ) to SB. We thank  Christiane Weydig  for cloning the HtrA mutagenesis constructs in Suppl. Fig. S3C andD. The work of SW was supported by the grant  P-24074  from the  Austrian Science Fund (FWF). Colombian biopsy specimens were kindly provided by  Oscar Gutierrez  ( Universidad Nacional de Colombia, Bogota, Colombia ) and the US biopsy specimens were obtained by  David Y. Graham  ( Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas, USA). We would also like to thank  Samantha Shi Min Ling  ( Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore ) for general support.
Acknowledgments We thank  Kathryn Wagner  for tissue samples and  Fedick Rahimov  for the cDNAs produced from the muscle biopsies (Wellstone Foundation). We also thank  Min Chen  for bioinformatics assistance ( UT Dallas ) and  Jean-Marie Rouillard  from  Biodiscovery, LLC , for providing reagents. M. C. G. is the recipient of a fellowship from the  Association Française contre les Myopathies (AFM). This work was supported by  AG01228  from the  US National Institute of Aging  (W. E. W. ) and  NIH   Postdoctoral Training Fellowship  ( T32 CA124334 , A. T. L. ). This work was performed in laboratories constructed with support from  National Institutes of Health  grant  C06 RR30414.
Models and Data Analysis CMIP5 and MAREMIP models. We compare model simulations for the period 2012 to 2100 which are forced by rising atmospheric CO 2 concentrations according to the RCP8. 5 scenario  [Meinshausen et al. , 2011]. We use a subset of models from the Marine Ecosystem Model Intercomparison Project (MAREMIP)  [Vogt et al. , 2013; Hashioka et al. , 2013; Sailley et al. , 2013; Laufkötter et al. , 2015]  and from the Coupled Model Intercomparison Project Phase 5 (CMIP5)  [Taylor et al. , 2012]. We use all models that contain at least two phytoplankton functional types and provide all data necessary for our analysis (at least export production and CO 2 flux), resulting in eight model simulations. We use the first ensemble member for CMIP5 models (r1i1p1). Selected models are either ocean-ice-ecosystem models (MITgcm-REcoM2, NEMO-PlankTOM5.
We thank two reviewers for their constructive comments on the manuscript. All model outputs are available from either the CMIP5 (http://pcmdi9. llnl. gov/esgf-web-fe/) or MAREMIP (access to ftp server can be provided upon request, Taka Hirata: tahi@ees. hokudai. ac. jp) archives. In addition, the data used to produce the figures are available at http://dx. doi. org/10. 1594/PANGAEA. 849079.
Acknowledgments This research was supported through the  Helmholtz Postdoc Programme and  through the  EU   FP7  project  CARBOCHANGE  (grant agreement  264879 ). S. Doney  and  I. Lima  were supported by the  U. S. National Science Foundation  through the  Palmer LTER Project  ( NSF PLR-1440435 ). We acknowledge the  World Climate Research Programme 's Working.
The scientific purpose is ultimately to determine the core mechanisms of small vessel stroke and other forms of brain damage resulting from small vessel disease. Reliable comprehensive quantification of all disease features is an essential component of achieving that goal. The suitability of this protocol for studying stroke and all other presentations of cerebral SVD is evaluated using imaging data from patients recruited for two studies of stroke mechanisms. Thus, we present a robust image processing protocol, which operationalizes the recommendations of the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) guidelines  (Wardlaw et al. 2013b).
Subjects Our protocol was developed and evaluated using data from 250 consecutive patients who presented to a hospital stroke service with the first clinically evident nondisabling (i. e. , mRS <3) lacunar or mild cortical ischemic stroke. Diabetes, hypertension, and other vascular risk factors were not criteria for exclusion. However, patients with unstable hypertension or diabetes, other neurological disorders, major medical conditions including renal failure, contraindications to MRI, unable to give consent, those who had hemorrhagic stroke, or those whose symptoms resolved within 24 h (i. e. , transient ischemic attack) were excluded. The studies that provided data for evaluating this protocol were approved by the Lothian Ethics of Medical Research Committee (REC 09/81101/54) and the NHS Lothian R+D Office (2009/W/NEU/14), and conducted according to the principles expressed in the Declaration of Helsinki.
Brain MRI acquisition protocol All MRI scans were acquired using a 1. 5T GE Signa Horizon HDxt clinical scanner (General Electric, Milwaukee, WI, USA) operating in research mode and using a selfshielding gradient set with maximum gradient of 33 mT/ m and an 8-channel phased-array head coil. Imaging data were obtained soon after presenting to hospital with acute stroke symptoms (median 12 days, IQR 4-27 days) and 1 year (n = 200)  (Heye et al. 2015)  or 3 years (n = 50)  (Wardlaw et al. 2009 ) later to provide information on disease progression. The acquisition protocol (Table  1 ) was designed to provide high-resolution images with multiple contrasts and quantitative parameter maps within a total acquisition time acceptable to mild stroke patients (total acquisition time of approximately 30 min).
Imaging data storage and processing Imaging data are anonymized and stored on secure backed-up servers. Unique study identification numbers identify case records. Identifiable information is held in a separate password-protected database. The quality of all imaging data is visually verified, and the acquisition parameters and relevant imaging information are stored on an imaging database. Fourier-reconstructed images in DICOM (Digital Imaging and Communications in Medicine standard) format are then viewed in workstations with high-resolution displays for visual analysis by expert neuroradiologists and converted to Analyze 7. 5 and nifti 1. 1 formats for further computational analysis. All results from the computational analyses are double checked, independently, and manually edited by trained analysts, supervised by neuroradiologists.
Brain and Behavior, doi: 10. 1002/brb3. 415 (4 of 18) ª 2015 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Collaborative Group, 2015), type and size of all cortical and subcortical infarcts (new and old), presence of cortical siderosis, microbleeds, iron deposition, perivascular spaces, WMH, and atrophy (see scores on Table  2 ) at baseline, as well as their progression at follow-up. Examples of proformas to record these data can be accessed from http://www. ed. ac. uk/schools-departments/clinicalsciences/neuroimaging-sciences/imaging-services/imageanalysis-tools. At baseline, the presence or not of the index stroke lesion is recorded. If the lesion is present, MRI modality in which it is visible, location, type, shape, maximum diameter  (Del Bene et al. 2013) , and whether it affects the cortex or not, according to standard templates  (Wardlaw and Sellar 1994; The IST-3 Collaborative Group, 2015)  are also recorded.
Computational assessment All images are converted from DICOM to Analyze 7. 5 format using software developed in-house. All structural sequences obtained at baseline and follow-up are rigidly and linearly registered to the T2W image of the baseline scan using FSL-FLIRT (http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/ FLIRT)  (Jenkinson et al. 2002). To guarantee the quality in the registration, given the high anisotropy of the voxels, it is done in two steps: (1) registration to the T2W image acquired at the same time point, (2) application of the matrix that defines the space transformation of the T2W image from follow-up to baseline. The measurements obtained computationally from the structural images are described in Table  2 , performed separately per scanning session and blind to all other results; with the exception of the intracranial volume (ICV), which is performed only once at baseline and used for all the follow-up analyses.
The resultant "non-brain" binary masks are subtracted from the ICV to provide a measure of total brain tissue volume. The brain tissue binary masks are input to MCMxxxVI to define and extract all FLAIR hyperintensities on each dataset. These correspond to the regions in yellow color on the coregistered FLAIR and  Punctate or diffuse areas in the white matter and deep gray matter of the cerebral hemispheres or in the brainstem that were 3 mm or larger in diameter, and hyperintense with respect to normal-appearing tissues on T2W and FLAIR. Some hypointensity on T1W is allowed as long as this is not as hypointense as CSF. Hyperintensities subtler than the very obvious ones are included when they have outstanding intensity differences with respect to the white matter considered "normal" as per identified on T1W images Properties: Measured on the FA, MD and T1 maps after multiplying these parametric images by the WMH binary masks.
These were assumed to be related to or in the stroke lesions, and were labeled "cavities in index stroke at follow-up" or "cavities in old stroke". When their size was equal to or less than 3-mm diameter or their intensity was higher than that of the CSF, this was annotated for further consideration in the analyses. If the cavities of more than 3-mm diameter were not related to a stroke lesion at baseline, they were disregarded. T2*W mapped in green and red, respectively. The software that implements this technique, generates binary masks of the FLAIR and T2W-hyperintense regions, which are converted into object maps to be further separately classed as WMH, old stroke lesions, index stroke lesions, and new lesions appearing at follow-up, using the Region of Interest tool in Analyze 11. 0 TM and definitions/ criteria from Table  2.
2015)  (https:// github. com/aglatz/mineral-deposit-segmentation-pipeline/tree /master/libBRIC/mineral-deposit-segmentation), and a semiautomatic segmentation of those located elsewhere  (Valdes Hernandez et al. 2011). Briefly, we perform a rigid body transformation to register the T1W volume to the T2*W using FSL-FLIRT with the correlation ratio as optimization criterion. N4  (Tustison et al. 2010 ) is used to correct for nonanatomical intensity variations caused by multiple factors including inhomogeneous performance of the radiofrequency transmit and receive coils. Non-brain structures visible on T1W volumes are removed by transforming the brain masks from T2*W to T1W space and applying them to the corresponding T1W volumes. Subcortical structures (including basal ganglia, brainstem, hippocampi, and thalami) and NAWM are segmented as explained above.
Evaluation of measurements' accuracy and interanalyst agreements All quantitative methods have been previously evaluated  (Hernandez et al. 2010; Valdes Hernandez et al. 2011 , 2012a,b,c). On a sample of 150 individuals aged 71-72 years, the mean difference between a reference standard manually outlined by an experienced analyst and the measurements of ICV performed with the method described in this protocol was 2. 7% (95% CI AE7%)  Valdes Hernandez et al. 2012c ). In test-retest analyses for accuracy of measuring WMH volume on 14 datasets from volunteers aged 65-70 years and patients with mild nondisabling stroke aged 50-90 years, MCMxxxVI had a coefficient of variation for repeated measurements of 0. 21  (Hernandez et al. 2010). On a subsequent test on a sample of 20 septuagenarian individuals, selected to represent the whole range of WMH load, the spatial agreement (i.
Diffusion-weighted imaging analysis From the diffusion-weighted images, parametric maps of fractional anisotropy (FA) and mean diffusivity (MD) maps are generated. For each dataset, nonlinear registration  (Modat et al. 2010 ) is used to align the tissue masks at baseline in the structural space (T2W) with the parametric maps in the diffusion space. We apply NiftyReg from http://sourceforge. net/projects/niftyreg/ using Trac-toR (http://www. tractor-mri. org. uk/diffusion-processing) to obtain the transformation between the structural T2W brain image and the averaged diffusion volume with b = 0 s/mm 2. To avoid partial volume averaging with CSF due to registration inaccuracies, the CSF mask is dilated by one voxel in each direction and then subtracted from the NAWM, gray matter, and WMH masks in the diffusion space.
The mathematical demonstration of how and why this distortion occurs appears in the Appendix S2. To illustrate with an example, we delineated the index stroke lesion as per this protocol (Table  2 ) on brain MRI datasets from 187 patients who presented with acute lacunar stroke symptoms and had a recent small subcortical infarct confirmed on MR diffusion imaging  (Valdes Hernandez et al. 2015). Then, we mapped the delineated lesions on a common "standard" space, and measured the lesion volume before and after the space transformation. Lesion volumes after the space transformation were 2. 5 times bigger (IQR 0. 67) than when measured in their native space, thus representing a median increase in 1. 36 times (IQR 0. 12) per dimension (see comparative graph in Appendix S2).
This is the subject of ongoing work. Giving the complexity of the image contents, perhaps this is an area where machine learning approaches, spatial 3D modeling and more detailed serial imaging would be better at than the more traditional approaches to date. This information was collected from nine analysts with experience on the image analysis tasks performed on the datasets. Each analyst reported the estimated time that takes for him/her to manually rectify the boundaries of the "biomarker to segment", and the (also estimated) number of datasets on which he/she did the referred assessment in the way this protocol describes. ª 2015 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Brain and Behavior, doi: 10. 1002/brb3. 415 (15 of 18).
Acknowledgments The studies that provided data to develop the present protocol were funded by:  Wellcome Trust  ( 075611 ),  Chief Scientist Office of the Scottish Executive  ( 217 NTU R37933 ). Other sources of funding were:  Row Fogo Charitable Trust  (MCVH, PAA),  Scottish Funding Council Scottish Imaging Network A Platform for Scientific Excellence Collaboration (JMW) ,  Age-UK Disconnected Mind Study and Medical Research Council (SMM) , and  The Brain Research Imaging Centre Edinburgh  (http:// www. bric. ed. ac. uk/) (KS, ES, MJT). We thank participants in the studies, the radiographers and staff at the  Brain Research Imaging Centre , the following individual contributions:  G. Barclay ,  D. Anblagan ,  E. Sakka ,  I. Hamilton ,  N. A. Royle ,  D. A. Dickie , and  S. Wiseman , for providing data for Table 3,  L.
Verbatim label data are given for primary types. Additional data by the present authors are given in brackets. BMNH DNA was extracted from hind legs of dried specimens. DNA barcodes (658 bp of the COI mitochondrial gene) were generated at the Canadian Centre for DNA  Barcoding (CCDB, Guelph). A total of seven specimens were sequenced (Table  1 ), all collected in French Guiana by the third author (CLV). These newly generated barcodes were compared to five DNA-barcodes of Philonome clemensella (Table  1 ), one (jflandry0875) available at the Barcode of Life Data Systems (BOLD; www. boldsystems. org; also see  Ratnasingham and Hebert 2007)  and the other four unpublished. Barcode data were analysed using the analytical tools of BOLD such as Neighbour Joining and pairwise genetic distance matrix.
nigrescens and P. rivifera) or absent; ductus bursae slender; corpus bursae obovate or elliptical; signum absent. Biology. Chambers (1878)  mentioned that he repeatedly collected Philonome clemensella from the type locality where Gleditschia triacanthos L. , Ulmus americana L. , Prunus serotina Ehrh. , and Celtis occidentalis L. grow in the immediate vicinity. He then assumed that the larvae may feed on some weeds or shrubs growing nearby. Forbes (1923)  noted that the larvae of P. clemensella feed on hickory and linden. These records were, however, based on ambiguous label data which state only plant names without details and thus require verification. Nothing is known about the biology for other congeners of P. clemensella. Included species (arranged by the similarities in the forewing pattern and the male genitalia): Philonome cuprescens  Walsingham, 1914: 346; Davis 1984: 25.
Types. Lectotype (designated here): male, "48" [hand-written], "Type No. Host plants. Hickory (Juglandaceae: Carya) and linden (Tiliaceae: possibly Tilia americana L. )  (Forbes 1923 ). These are from the label data in the USNM collection. The collection also includes a specimen whose label data states that it came from oak (Fagaceae: Quercus). The label data give no details other than plant common names. Therefore, it is not clear if these records refer to larval host plants or where the adults were collected. Diagnosis. This species is similar to Philonome curvilineata in external appearance but differs from the latter in having the longitudinal and costal fasciae separate (continuous in P. curvilineata and larger apical protrusion on the tegumen in the male genitalia. Adult (Fig. 6 ). Head: Vertex orange on posterior 2/3, pale orange on anterior 1/3; scales on dorsum of occiput dark grayish brown; scales between antennal scapes lustrous pale orange; frons lustrous pale yellow.
Specimens used for the DNA barcoding analysis. Both the Process ID and sample ID codes are unique identifiers linking the record in the BOLD database and the voucher specimen from which the sequence is derived. Additional collecting and specimen data are accessible in BOLD's data set (http:// dx. doi. org/10. 5883/DS-PHILONO) as well as GenBank (http://www. ncbi. nlm. nih. gov/genbank/).