dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing Over 1000 P. aeruginosa Genomes: Objectives and Strategy By generating a comprehensive genome sequence database truly representative of the worldwide P. aeruginosa population, we will: (1) Assemble a large and representative strain collection, with associated genome data, useful for antimicrobial testing, identification of resistance markers, and data mining for new therapeutic targets; (2) Develop platforms and pipelines to enable synergy between genomic and clinical data, which will allow identification of prognostic markers and stratification of patients, leading to improvements in patient care; (3) Transform CF diagnostic microbiology through innovations in genomics; (4) Develop user-friendly tools that will enable CF clinicians to interpret genomic data leading to better informed decisions on issues of cross infection.
Our working hypothesis is that a high-quality, large-scale bacterial genome database available through a user-friendly pipeline will have a major impact on epidemiology, diagnostics and treatment. A major objective is to identify representative isolates from groups of closely related genomes to become reference type isolates and provide reference type genomic data. To this end, genomes are initially sequenced on an Illumina MiSeq instrument with an average median coverage of 40. This first sequencing step will help to determine whether there is already a good reference genome for each new genome by investigating both core and accessory genetic material. First, for conserved regions among isolates, i. e. , the core genome, we will perform phylogenetic analysis and identify single nucleotide polymorphisms (SNPs).
Second, we will determine whether a new genome significantly contributes to expansion of the full genetic repertoire of P. aeruginosa (i. e. , the pan-genome), for instance, with at least 0. 1% of its genome (about 6000 bp) representing previously unknown accessory genetic material. It will also be possible to identify indels and structural variations among genomes. We will analyze genomes for the presence of genomic islands using IslandViewer and related genomecomparison and sequence-composition based tools  (Langille and Brinkman, 2009; Grant et al. , 2012; Dhillon et al. , 2015). Finally, each new genome will be characterized based on the presence/absence of regions of genome plasticity  (Klockgether et al. , 2011) , the virulence factor (VF) database  (Barken et al. , 2008) , curated VF data at www.
The International P. aeruginosa Consortium Database (IPCD) The IPC's strain collection is harbored at the Institute for integrative and systems biology (IBIS), in Quebec City, Canada. It currently contains 1514 entries for P. aeruginosa isolates spanning 135 years back to 1880 and covering 85 locations, in 35 countries, on five continents. It includes previously described collections  (Pirnay et al. , 2009; Stewart et al. , 2011; Kidd et al. , 2012)  and was assembled with the aim of representing maximal genomic diversity. To this end, various criteria were taken into consideration, including geographic origin, previous genotyping, phenotype, and in vivo behavior. We envisage that the collection can accommodate over 10,000 isolates. In order to manage phenotypic and genomic data for the growing P.
aeruginosa collection in addition to sharing this data, we created the International P. aeruginosa Consortium Database (IPCD), an open source web application available at http://ipcd. ibis. ulaval. ca/. It includes isolate identification, host, researcher, date of isolation, geographical origin, phenotypic data, anonymized patient information, DNA extraction details, NGS information and genome assembly. Its structured vocabulary is being developed further. IPCD currently contains NGS data and unpublished draft genomes from CF patients and from most of the other types of known human infections. For comparative genomics purposes, IPCD also contains animal infection isolates and environmental isolates from plants, soil and water. Researchers who provided strains have priority access to corresponding genome sequences through personal user accounts, but all genome sequences produced by the IPC will become publically available.
Phylogeny of P. aeruginosa P. aeruginosa is well known to have an adaptable genome (5. 5-7 Mbps) that enables it to colonize a wide range of ecological niches; comparative genomics approaches have identified changes in surface antigens, loss of virulence-associated traits, increased antibiotic resistance, inserted genomic islands including phage, and pyocin operons, overproduction of alginate and the modulation of metabolic pathways. Its genome also has many regions that exhibit plasticity  (Klockgether et al. , 2011). The IPC will provide fine-scale analyses to evaluate these changes, allowing the comparison of VFs, loss/gain of function mutations and antibiotic resistance genes as well as complete core and accessory genomes. As a proof of concept for this aim, we used the 389 genomes that constitute our current sequence dataset, including the 335 draft genomes produced to date by the IPC, to perform phylogenetic analysis of the core genome using the Harvest suite  (Treangen et al.
, 2014). We found that P. aeruginosa strains can be divided into three major groups (Figure  1A ), a result providing concrete support and agreement to previous results with a limited but diverse set of 55 strains  (Stewart et al. , 2014). The tree presented here also provides novel information since it shows new subgroups and provides unmatched resolution. The high number of strains included in our analysis and the fact that these strains come from a wide array of sources (including environmental, clinical and animal strains, and a wide geographical spread) may suggest that group 1 strains, including PAO1, are naturally more abundant than group 2, which includes PA14. However, it is noteworthy that the opposite conclusion was reached in a previous typing-based study  (Wiehlmann et al. , 2007) , and that our dataset is biased toward clinical isolates.
, 2012). Reliable methods for the phylogenetic analysis of our dataset are used including analysis of core genome SNPs using Harvest  (Treangen et al. , 2014). Close attention to the links between the presence of strain specific genomic islands and patterns of SNPs in the core genome will help identify diagnostic sequences and SNP combinations for the development of new P. aeruginosa typing methods with the highest resolution to date. This will be done using a combination of de novo island prediction using IslandViewer  (Langille and Brinkman, 2009; Dhillon et al. , 2015)  and analysis using the CG View Comparison Tool  (Grant et al. , 2012). As an additional feature, we plan to link IPCD with The Comprehensive Antibiotic Resistance Database (CARD;  McArthur et al. , 2013)  available at http://arpcard.
The Canadian CF Registry was created so that data entry could be standardized in CF clinics across Canada. We will use this system to link IPCD with clinical data for CF isolates. , 2013). The bar plot shows in how many strains each profile was observed. On the left, proteins are grouped according to their biological function or the resistance they confer. In rare cases, more than a single copy of a resistance gene may be present within an individual strain. For those genes with resistance conferred by mutation (labeled with an asterisk), all detected mutations are known from other pathogens and may require functional verification in P. aeruginosa. Genes labeled as "putative" ("put. " in the figure) are similar to a number of known sequence variants within a family of AMR genes. All perfect matches to OXA β-lactamases are OXA-50.
Future Genomic Analyses and Biological Studies of P. aeruginosa We are committed to continuously improve the IPCD and pseudomonas. com by adding P. aeruginosa genomes from other human, animal, and environmental isolates as well as by enhancing metadata. The consortium has identified a number of research priorities in the international P. aeruginosa community, many of which will be long-term projects shared among members of the consortium as a research working group. The goal of the IPC is to avoid duplication of efforts in P. aeruginosa genomics and enhance interest from researchers having common goals. Additional members are welcome to join in so that the "depth and breadth" of P. aeruginosa biology expands beyond what we outlined for the initial consortium. We also intend to seek collaboration with other groups to connect our database with those developed for other Pseudomonad genomes.
Acknowledgments We express our gratitude to members of the Plateforme d'analyses génomiques and bioinformatics platform at IBIS. We also thank  Tariq Elsayegh  ( Royal College of Surgeons in Ireland ) for his assistance with annotation of polymyxin resistance genes. JJ is supported by a  Cystic Fibrosis Canada postdoctoral fellowship. RL is funded by  Cystic Fibrosis Canada  and by a  CIHR-UK  team grant. CW, JF, and MM are supported by the  UK Cystic Fibrosis Trust. CW and NL would like to acknowledge funding from  Fight for Sight. JB was supported by  NIH  grant  P30 DK089507. SB is supported by a  Queensland Health Fellowship. SB, TK, PR, and KG were supported by grants by  NHMRC  (# 455919 ) and the  TPCH Foundation. TK is the recipient of an  ERS-EU RESPIRE2 Marie Skłodowska-Curie Postdoctoral Research Fellowship -MC RESPIRE2 1st round 4571-2013.
IL is supported by grants from  CureKids ,  Cystic Fibrosis New Zealand , and the  New Zealand Lotteries Board (Health). AM holds a  Cisco Research Chair in Bioinformatics , supported by  Cisco Systems Canada, Inc. GD Wright's laboratory is funded by the  Canadian Institutes of Health Research (CIHR) , the  Natural Sciences and Engineering Research Council (NSERC) , and a  Canada Research Chair. NW is supported by a  CIHR Doctoral  research award. GW and FB acknowledge  Cystic Fibrosis Foundation Therapeutics and Genome Canada  for support. ED's laboratory is supported by  Canadian Institutes of Health Research (CIHR)  operating grant  MOP-142466  and a  Canada Research Chair.
The primers used for these RT-PCR experiments are as follows: 16S RNA, forward, GATCATG-GCTCAGATTGAACG, and reverse, AGTTATCCCCCTCCATCAGG; gyrB, forward, AGTATTAAAAGGGCTGGATGC, and reverse, ACCGA-CACCGAGTTATCAGC; pelE, forward, AGCGAATTCAAAGCAG-CACT, and reverse GGCGTTTCGATGTACAGGTT; and asr, forward, GCTCTGGGTCTGTCCTCTGT, and reverse, CTGAGCTTTCTGCGT-TGC. DNA microarray experiment and data analysis. The microarrays used in this study were custom designed and produced by Roche Nimble-Gen, Inc. (Madison, WI) based on the annotated sequence of D. dadantii, available at GenBank accession no. CP002038, which comprises 4,597 coding sequences (CDSs)  (62). The 4-plex expression microarrays consist of 60-mer oligonucleotides, triplicated in three blocks on the array (5 oligonucleotides per CDS). For microarray analyses, cDNA was synthesized, labeled, and hybridized by Roche NimbleGen, Inc.
Functional gene groups were taken from the respective GO branches provided by the KEGG database. Determination of structural-functional domains. In order to obtain structural domains, we investigated the correlation of the feature dynamics (gene expression density, hyp genes, rel genes, leading-strand preference, and average negative melting energy) under the growth conditions analyzed. For a pair of features, we first computed for each window a Pearson correlation of the window feature Z scores. Taking all windows together, we obtained a feature correlation ring for each pair of features. The term "ring" is used here to describe the set of all windows (500-kb size, 4-kb shift) with the contained values. In the next step, we determined common changes of correlations in all correlation rings (comprising all combinations of features).
High values can be interpreted as a change of the functional composition of genes in the two windows. These peaks were compared to the positions of the structural domains. For each position in one set of borders (e. g. , functional borders), the nearest neighbor to a border of the other set (e. g. , domain borders) was determined. For a robust result, we took the median of the derived distances to the nearest neighbors of all borders. In 10,000 repetitions of this procedure, 98. 0% of the random borders showed a larger median nearest neighbor than the actual border positions, indicating a tight connection between the functional and structural domain borders. Microarray data accession numbers. The microarray data from this study are compiled in the EBI Gene Expression (ArrayExpress) database (http://www.
ACKNOWLEDGMENTS This work was supported by grants from the  French Agence National de la Recherche  ( ANR ),  ANR  Blanc Régupath 2007, and  Bonus Qualité Recherche (BQR) INSA de Lyon 2012 , and a research grant from the  Deutsche Forschungsgemeinschaft  to  G. M. Exchanges  between the French and German teams were supported by  CNRS Programme International de Cooperation Scientifique  ( PICS 2009 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank  Servane Le Guillouzer  for help with RNA extraction for novobiocin experiments.
This paper looks into the multiplication process in a Micromegas detector, with particular focus on the avalanche charge fluctuations which limit the energy  [22]  and position  [23]  resolutions. Although experimental data quantifying these fluctuations are critical for gaseous detectors simulation programmes, they are rather scarce in tables and literature. This paper proposes a method ideally suited to get such data. It is based on measurements of the single-electron response (SER) of a Micromegas detector, operated with helium, neon and argon gas mixtures with Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/nima isobutane. Compared to the traditional use of a radioactive source or a charged particles beam, this method provides a direct measurement of the absolute gain and the relative gain variance but requires an optimised test-bench, in terms of detector mechanics and electronics signal-to-noise ratio, to enable single electron detection in a wide range of electric field, down to gains as low as a few 10 3.
BACKGROUND: Statin intolerance limits many patients from achieving optimal low-density lipoprotein cholesterol (LDL-C) concentrations. Current options for such patients include using a lower but tolerated dose of a statin and adding or switching to ezetimibe or other non-statin therapies. METHODS: ODYSSEY ALTERNATIVE (NCT01709513) compared alirocumab with ezetimibe in patients at moderate to high cardiovascular risk with statin intolerance (unable to tolerate $2 statins, including one at the lowest approved starting dose) due to muscle symptoms. A placebo run-in and statin rechallenge arm were included in an attempt to confirm intolerance. Patients (n 5 361) received single-blind subcutaneous (SC) and oral placebo for 4 weeks during placebo run-in. Patients reporting muscle-related symptoms during the run-in were to be withdrawn.
Study design ODYSSEY ALTERNATIVE (NCT01709513) was a randomized, double-blind, double-dummy, active-controlled, parallel-group study conducted at 67 sites in 8 countries (Austria, Canada, France, Israel, Italy, Norway, the UK, and the USA), with enrollment from November 2012 to October 2013. The study rationale and methods have been published. 21 he study was conducted according to the Declaration of Helsinki and the International Conference on Harmonisation Guidelines for Good Clinical Practice. The institutional review board and ethics committee at each center approved the protocol; all participants gave written informed consent.
Study population The population comprised patients ($18 years) with primary hypercholesterolemia. 21  Patients at moderate or high cardiovascular risk (as defined in Supplemental Text 2) were eligible if they had a calculated serum LDL-C concentration $ 100 mg/dL (2. 6 mmol/L) at screening; those at very high risk were eligible if they had a calculated serum LDL-C $70 mg/dL (1. 8 mmol/L). 21 During screening, each patient completed a questionnaire that collected data on history of statin therapies and symptoms. Statin intolerance was defined as the inability to tolerate 2 or more statins because of unexplained skeletal muscle-related symptoms (eg, pain, aches, weakness, or cramping), other than those due to strain or trauma that began or increased during statin treatment and resolved with statin discontinuation (see Supplemental Text 2).
3% of the population for each arm, based on product information. 22 he ITT analysis used for evaluation of the primary end point included all calculated LDL-C values, irrespective of treatment adherence, up to week 24. Missing data were accounted for using a mixed-effect model with repeated measures approach. 23, 24 The consistency of the treatment effect for the primary end point was assessed across prespecified subgroups. 21 A P value of #. 05 was considered to be statistically significant. A hierarchical testing procedure was used to control type I error and handle multiplicity for analyzing the key secondary end points. 21 The first secondary end point was an on-treatment (modified ITT) analysis. Further secondary end points, in order of hierarchical testing, included percent change in LDL-C from baseline to week 12 (ITT and on-treatment), the percent change in other lipid parameters, and the proportion of patients reaching their cardiovascular-risk-based LDL-C goal at week 24 in both the ITT and on-treatment analyses (see also Supplemental Table  1 ).
The modified ITT population included all randomized and treated patients with a baseline LDL-C measurement and at least 1 measurement after 4 to 24 weeks of treatment, as long as the patient was on study treatment. For this analysis, all available measurements from weeks 4 to 24 within the on-treatment time window were used in the mixed-effect model with repeated measures model. Binary efficacy end points were analyzed using the multiple imputation approach followed by logistic regression; continuous efficacy end points anticipated to have a non-normal distribution (ie, triglycerides and lipoprotein[a]) were analyzed using the same multiple imputation approach followed by robust regression. The analysis was performed using SAS version 9. 2 software.
Study limitations The study design, which included placebo run-in and statin rechallenge to confirm statin intolerance, may have resulted in hypervigilance by study participants regarding potential statin re-exposure. The definition of statin intolerance differs from those used in other reports, and these results cannot be generalized to all individuals with statinassociated muscle symptoms, particularly those who refuse to be rechallenged with a statin and who may have had even more severe symptoms than the participants. It should also be noted that the study population was predominantly white-an aspect that would benefit from being addressed in future studies. Finally, the study duration was short, and the rate of treatment discontinuation may increase over the longer term. Cardiovascular outcomes with alirocumab are currently being evaluated in a large ongoing study (http:// clinicaltrials.
Acknowledgment ownership interest in Abbvie,  Abbott Labs, General Electric , and  J&J ; and has been a consultant/advisory board member for  Amgen, AstraZeneca ,  Regeneron ,  Merck ,  Genoma ,  Runners World ,  Sanofi ,  Esperion , and  Amarin. Dr Cannon  has received personal fees from  Sanofi and Regeneron Pharmaceuticals, Inc  during the conduct of the study; has received grants from  Accumetrics ,  Arisaph ,  AstraZeneca , and  Boehringer-Ingelheim , has received personal fees from  CSL Behring, Essentialis, Janssen, Lipimedix, BMS , and  Pfizer , and has received grants and personal fees from  GlaxoSmithKline ,  Merck , and  Takeda , outside the submitted work. Dr Guyton  has received research support from  Regeneron/Sanofi, Genzyme/Sanofi ,  GlaxoSmithKline ,  Amarin Pharma , and  Amgen ; has received speaker honoraria from  Merck ; holds stock in Eli Lilly; and has received consulting fees from  Regeneron/ Sanofi ,  Novella , and  Merck.
Sanger sequencing. Sanger sequencing was performed on a total of 89 yam fragments, approximately representing 50% of the total yam chloroplast genome  (Scarcelli et al. 2011, Supporting information). Amplification was performed with Failsafe enzyme mix (Epicentre), and sequencing PCRs were performed using BigDye terminator kit (Applied Biosystems). Sequencing was performed on an ABI prism 3130 (Applied Biosystems, at INRA, Montpellier, France) using both forward and reverse primers. All sequences are available on either GenBank (Sanger fasta sequences), NCBI-SRA (. fastq files) or DRYAD (. fastq and aligned. bam files) as listed in Supporting information.
Bioinformatics analysis Demultiplexing, data cleaning and mapping. Demultiplexing based on the 6-bp barcodes was performed using the freely available PYTHON script DEMULADAPT (https:// github. com/Maillol/demultadapt), using a 0-mismatch threshold. Adapters and low-quality bases were removed using CUTADAPT 1. 2. 1  (Martin 2011)  with the following options: quality cut-off = 20, minimum overlap = 7 and minimum length = 35. Reads with a mean quality lower than 30 were discarded afterwards using a freely available PERL script (https://github. com/ SouthGreenPlatform/arcad-hts/blob/master/scripts/ar-cad_hts_2_Filter_Fastq_On_Mean_Quality. pl). Mapping was performed using BWA MEM 0. 7. 5a-r405  (Li & Durbin 2009)  with -M and -B 4 options, using the appropriate chloroplast reference genomes (rice: NC_001320.
We tested different values for this minimum frequency for 0%, 5%, 10% and 15% (methods BVar0, BVar05, BVar10 and   2 ). The percentage of observed intra-individual polymorphism was calculated over the total number of polymorphic positions. The full script used to perform the bioinformatics analysis is available as Supporting information. Then, we kept only the reads mapped to the chloroplast genome and we mapped them to the closest available mitochondrial genome (O. sativa, BA000029. 3) and to the nuclear genome (ADWL00000000. 1). We report the number of reads mapping on both the chloroplast and the mitochondrial and on both the chloroplast and the nuclear genome. To assess whether our results can be applied to other species, we performed the same analysis on pearl millet, yam and the palm genus Podococcus.
WGS and LR-PCR were undertaken for one sample per species. For the CpE approach, we had access to data of eight different samples per species (Table  1 ). This allowed us to assess the variance in intra-individual polymorphism between samples. For these species, we only calculated the intra-individual polymorphism fraction for a minimum variant allele frequency of 15% (BVar15). To assess whether the observed differences between the three sequencing methods are significant, intraindividual polymorphism levels were compared using a binomial test. Impact of SNP calling methods on chloroplast diversity assessment. Evaluating NGS data analysis methods and pipelines requires 'true' reference sequences. In a previous study  (Scarcelli et al. 2011 ), 50% of three yam chloroplast genomes was sequenced using the traditional Sanger approach.