dataesr/scientific-paragraphs-categorization huggingface dataset:
( with unit length of 3, and then 6 and 9) fell into the first category (coding). The distribution of the 3-mer STRs are significantly higher than expected in the coding regions and significantly lower in the UTRs (chi-square p-value < 0. 00) as expected due to the nondisturbance of the open reading frame by those repeats (Figure4). The rest were mostly in the UTRs or in transcripts without ORFs. The distribution of both marker types in the different regions of the transcriptome is consistent with the expected distribution of genetic variants as discovered in other studies as well (e. g. MANOUSAKI et al. 2014 ) and provide a high quality dataset for future genetic analysis in this new but important to aquaculture species.
Scytherecognizes adapter sequences taking into account quality information especially at the 3' end where quality falls. Thus, this step was applied prior to any quality-based trimming (prior contamination rate set in 0. 1 '-p 0. 1'). Then, low quality (Phred quality threshold of 20 and minimum reads length of 45 nt) reads trimming was performed with Sickle (https://github. com/najoshi/sickle). Sickle scans the reads in sliding windows and based on the quality it determines whether a read requires trimming in the two ends or complete removal (parameters 'pe -g -t sanger -q 20 -l 45'). The surviving reads were used as input to Trimmomatic (BOLGER et al. 2014) to further remove 5' and 3' adaptor sequences and apply extra filtering steps (parameters 'PE -phred33 ILLUMINACLIP:adapter_file. fa:2:30:10 quality was assessed with FastQC (ILIAS et al.
2015)rmatics. babraham. ac. uk/projects/fastqc/) and subjected to quality control following a pipeline including multiple steps and published elsewhere(ILIAS et al. 2015)Briefly, we first used Scythe -a bayesian adapter trimmer (version 0. 994 BETA) (https://github. com/vsbuffalo/scythe), to identify adapter substrings in reads. LEADING:3 TRAILING:3 SLIDINGWINDOW:4:25 MINLEN:45 CROP:99'). Finally, we used PrinSeq (SCHMIEDER AND EDWARDS 2011) to filter out low complexity sequences.
(CONESA et al. 2005)an results were input in Blast2GO V. 2. 8. 0(CONESA et al. 2005)where GO terms and Enzyme Commision (EC) numbers were retrieved and assigned to transcripts. Finally, sequences with EC numbers were further annotated with Kyoto Encyclopedia of Gene and Genome (KEGG) pathways using custom perl scripts. The assembled transcripts were annotated through a BLASTx similarity search against the SWISSPROT protein database (e-value threshold 10 -5 ; keeping the top twenty hits). To improve the speed of this long process, we implemented BLASTx in parallel using (JONES et al. 2014)))through gVolante (https://gvolante. riken. jp/)selecting the Core Vertebrate Gene (CVG) set(HARA et al. 2015). ParaNOBlast (https://github. com/jacqueslagnel/ParaNoBLast)describedin(LAGNELetal. 2009). Further, scan against protein domain signatures was done with InterProScan(JONES et al.
Treatment Definition and Inclusion/Exclusion Criteria For the purpose of this analysis, eligible studies were those reporting on the outcome of adult patients who underwent VA-ECMO for acute heart failure and/or respiratory failure after cardiac surgery. Studies that met the Population, Interventions, Comparison and Outcomes (PICO) criteria (Tab. 1) were included in the present meta-analysis. To enter this analysis, studies had to fulfil all these inclusion criteria: (1) provide data on patients who required VA-ECMO after cardiac surgery procedure; (2) include patients aged 18 years or older; (3) report data at least on in-hospital or 30-day mortality; (4) be a prospective or retrospective observational investigation; (5) be published in English language as a full article; (6) include at least 10 patients; (7) less than half of patients had heart transplantation; and (8) be published since 2000.
Articles were ineligible for study inclusion if they (1) reported ambiguous or inaccurate data (discrepancies between data reported in the text and tables); (2) did not provide specific information on the type of ECMO used in the study population and its related outcome; (3) did not provide specific information on the timing of insertion of VA-ECMO; (4) reported data on other than cardiac surgery interventions; (5) included pediatric patients; (6) included only patients undergoing heart transplantation. In particular, we excluded from this analysis those studies which did not clearly state that VA-ECMO was performed through any peripheral or central arterial and venous cannulation. In this regard, the definitions of cardiopulmonary resuscitation or postcardiotomy ECMO were not considered sufficient to consider the study population as having been treated with VA-ECMO.
Data Extraction Data were independently collected from the retrieved articles by two investigators (M. G. and D. B. ) and checked by two investigators (F. B, and E. M. K. ). Disagreement on collected data was settled by consensus between these investigators. No attempt was made to obtain specific or missing data from the authors. The following data were extracted: first author, year of publication, study period, overall number of adult cardiac surgery procedures performed during the study period, type of intervention, number of patients, gender, major comorbidities and outcome measures. VA-ECMO was considered as inserted intra-or postoperatively when not otherwise specified. The quality of the included studies was assessed by two investigators (F. B, and E. M. K. ) using the National Heart, Blood, and Lung Institute (NHBLI) criteria for study quality assessment of case-control series (https://www.
Statistical Analysis Statistical analysis was performed using the Open Meta-Analyst software (Brown University, Providence, RI, USA; http://www. cebm. brown. edu/openmeta/). To control for the anticipated heterogeneity among observational studies, absolute values and means were pooled using random effects models. Heterogeneity across studies was evaluated using the I 2 test. The results are expressed as pooled untransformed proportions and means with their 95% confidence intervals (CI). Leave-one-out sensitivity analysis was performed to confirm consistency of the overall analysis. Mean differences were estimated to evaluate the differences in continuous variables between survivors and deaths. The impact of risk factors on hospital survival was evaluated by two-arm analysis and meta-regression.
Discussion The present pooled analysis showed that VA-ECMO is a valid mean to salvage one third of patients with profound cardiogenic shock after adult cardiac surgery. Older age and hyperlactatemia before starting VA-ECMO seems to have a significant negative impact on hospital survival. The estimated hospital survival (36. 1%) was poorer than the one (55. 8%) reported by Maxwell et al. 4  from the Nationwide Inpatient Sample database. However, the analysis of this administrative dataset did not include information on the type of ECMO used in these patients. This finding was not confirmed even by a more recent analysis of the same registry whose hospital survival after postcardiotomy ECMO was 40%. 3  Indeed, VA-ECMO after adult cardiac surgery is associated with higher risk of in-hospital mortality compared with other conditions.
Wagon-wheel experiment Background parasitism was found in one replicate of the wagon wheel experiment, which was excluded from the analysis. The final dataset included data from three full infestation replicates and four half infestation replicates for each cabbage accession. Host-plant finding by C. glomerata was analysed as presence/absence data. If eggs of C. glomerata were found in at least one of the recollected caterpillars of a plant at a location on the spokes of the wagon wheel, parasitism at this location was recorded as 'present'. We used a generalized linear mixed model (GLMM) with binomial error distribution and plant accession (categorical, levels: 'Christmas drumhead' and 'Badger shipper'), spatial arrangement (categorical, levels: 10 and 20 m), distance from central plot (continuous) and day after release (continuous variable) as fixed effects.
The factors 'week' (i. e. week in which the replicate was held) and 'plant ID' (as each plant was used for four consecutive days) were included as random effects. Three-way and higher term interactions were not fitted because the biological interpretation of such interactions is problematic. A full model containing all fixed variables with two-way interactions and random effects was the starting point for model selection using the dredge procedure in the R package MuMIN. This procedure results in a complete set of sub-models with combinations of the terms of the full model, and sorts the sub-models based on the corrected Akaike information criterion (AICc). Models with a ΔAICc of < 2 have a substantial support from the data. We present tables for the simplest model of the submodels with substantial support, with the fewest degrees of freedom.
In setups with 10 m spacing between infested plants, accession identity had no effect on host plant finding (Fig. 2 , Tukey post hoc, z-value = 1. 709, p = 0. 28). There was no difference in host-plant finding between setups of 'Christmas drumhead' (attractive) with 10 or 20 m spacing (Tukey post hoc, z = -0. 540, p = 0. 94) and between setups of 'Badger shipper' (less attractive) with 10 m spacing and 'Christmas drumhead' with 20 m spacing (Tukey, z = -0. 079, p = 1. 0). In setups with 20 m spacing between infested plants, fewer plants with parasitized caterpillars were found in wagon wheels with the less attractive 'Badger shipper' accession than in those with the attractive 'Christmas drumhead' accession (Fig. 2 ; Tukey   post hoc, z-value = 4. 521, p < 0. 001). Caterpillars were parasitized at a distance of 60 m after a time interval of 24 h after release (Fig.
Herbivore-induced plant volatiles consist of a complex blend of tens of chemical compounds produced by plants under herbivore attack, which provide  important information to both vertebrate and invertebrate natural enemies of herbivores that forage for insect herbivores  (Poelman and Kos 2016). Our field study shows that accession identity does not only influence the preference of parasitoids at a close range  (Poelman et al. 2009 ), but also the distance from which parasitoids are recruited in field situations: the spatial scale of parasitoid attraction ranges from 10 to 20 m for the less attractive cabbage accession to 20 m and likely beyond for the more attractive accession over the time scale of the experiment. The hosts of Cotesia glomerata parasitoids, the lepidopteran Pieris brassicae, lays clutches of eggs and the emerging caterpillars feed as a group; to avoid competition, the butterflies distribute their egg batches patchily and distances between plants with hosts can easily be beyond 20 m, but within the range of the current field experiment.
While accession identity did not influence the attraction of parasitoids at 10 m plant spacing in the field experiment, observations of parasitoid behaviour revealed that host finding success at distances smaller than 10 m was higher for the attractive accession than for the less attractive accession in a semi-field tent setting. Parasitoid searching behaviour was further influenced by the distance between the plant and the release point, and presence of hosts on the focal plant. Parasitism rates declined with time after release and increasing distance from the centre of the wagonwheel setup. While the movement ecology and perceptual range of larger animals, such as ungulates, have been relatively well studied  (Zollner and Lima 2005, Fryxell et al. 2008) , few studies have assessed the spatial scale of volatile emission and effects on the foraging behaviour or attraction of arthropods  (Aartsma et al.
00 hr and 18. 00 hr on the same day at each habitat patch during pilot work in August 2012 using a digital soil thermometer (Digitron FM15). Soil temperature was monitored at each habitat patch during the main study period in July 2013 using Maxim Integrated DS1921G F I G U R E 1 The 32 terrestrial sites in the Hengill geothermal region. Site names are annotated on the map in bold, consisting of a number (corresponding to the stream names in previous publications from the system) and a letter (corresponding to the bank of the stream: L = left; R = right). Each site name is followed by the mean soil temperature (°C) as calculated from 48-hr temperature logger data in July 2013. The "=" symbol, used at two points along the main river, indicates a much greater distance than shown on the map. Three habitat patches were selected along the stream bank at each terrestrial site.
This, and other (national) standards, should be considered when defining the particle shape (ISO, 2013a). For nanomaterials, EM is one of the few techniques that have a sufficient resolution to provide reliable images and thereby information on the shape of the nanoparticles. As already mentioned, EM provides a 2D projection of 3D particles and care should be taken to avoid bias due to preferential orientation effects. It is especially important when analysing platelet-like particles as their smallest dimension is often not well accessible for EM and hence can remain undetected. Although there are approaches to standardise reporting of shape data  (ISO, 2008) , a number of different protocols, i. e. ways of expressing the relevant dimension, are still used. AFM and STM provide 2. 5D-images of particles, however, shape analysis using these techniques is prone to errors due to tip effects, usually time consuming and thus expensive.
An important step towards agreement on reporting information is the development of OECD Harmonised Templates (OHT) for some of the endpoints  (OECD, 2016f). Additional data logging templates for environmental, health and safety assessment of nanomaterials have been proposed in the EU FP7 project NANoREG  (Totaro et al. , 2017)  and are implemented in the eNanoMapper database available at https://data. enanomapper. net/.
Acknowledgements The authors wish to recognise the contributions from the  WPMN delegates  and especially from experts participating to the two expert meetings (OECD, 2014a; OECD, 2016b), and the experts evaluating the methods for the report " Evaluation of Methods Applied in the OECD WPMN Testing Programme 1: Methods for Physico-Chemical Properties " (OECD, 2016a) as well as the constructive discussions in the WPMN forum and its subgroups. We would like to thank our colleagues  Dr G. Roebben ,  Dr P. S. Haastrup  and  Dr P. N. Gibson  from the  JRC  and  Prof. W. Peijnenburg  from  RIVM  for comments to earlier versions of this paper. ISO 14887:2000ISO 14887: , 2000. Sample Preparation -Dispersing Procedures for Powders in Liquids. ISO 13318-1:2001, 2001. Determination of Particle Size Distribution by Centrifugal Liquid Sedimentation Methods -Part 1: General Principles and Guidelines.
Strain identification by MALDI-TOF MS and 16S rRNA gene sequencing MALDI-TOF MS protein analysis was carried out as previously described  (Seng et al. 2013). The resulting 12 spectra of strain Arc-Hr T were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the main spectra of halophilic and methanogenic archaea including the spectra from Haloferax alexandrinus, Methanobrevibacter smithii, Methanobrevibacter oralis, Methanobrevibacter arboriphilus, and Methanomassilicoccus massiliensis. The 16S rRNA gene amplification by PCR and sequencing were performed as previously described  (Lepp et al. 2004 ). The phylogenetic tree was reconstructed according to the method described by  Elsawi et al. (2017).
Analysis of fatty acid methyl ester and membrane polar lipids Polar lipids were extracted and identified by one-dimensional TLC as described by  Cui and Zhang (2014). Cellular fatty acid methyl ester (FAME) analysis was performed by GC/MS. Three samples were prepared with approximately 80 mg of bacterial biomass per tube harvested from several culture plates. Fatty acid methyl esters were prepared as described by  Sasser (2006). GC/MS analyses were carried out as described before  (Dione et al. 2016). Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S, Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK).
Genome assembly Illumina reads were trimmed using Trimmomatic  (Lohse et al. 2012) , then assembled thought Spades software  (Nurk et al. 2013; Bankevich et al. 2012). Contigs obtained were combined together by SSpace  (Boetzer et al. 2011)  and Opera software  (Gao et al. 2011)  helped by GapFiller  (Boetzer and Pirovano 2012)  to reduce the set. Some manual refinements using CLC Genomics v7 software (CLC bio, Aarhus, Denmark) and homemade tools in Python improved the genome. Finally, the draft genome of strain Arc-Hr T consisted of 8 contigs.
Genome annotation and comparison Non-coding genes and miscellaneous features were predicted using RNAmmer  (Lagesen et al. 2007) , ARAGORN  (Laslett and Canback 2004), Rfam (Griffiths-Jones et al. 2003) , PFAM  (Punta et al. 2012), and Infernal (Nawrocki et al. 2009). Coding DNA sequences (CDSs) were predicted using Prodigal  (Hyatt et al. 2010 ) and functional annotation was achieved using BLAST +  (Camacho et al. 2009 ) and HMMER3  (Eddy 2011)  against the UniProtKB database (The UniProt Consortium 2011). A brief genomic comparison was also made between strain Arc-Hr T (CSTE00000000), Haloferax alexandrinus strain Arc-Hr (CCDK00000000), Haloferax gibbonsii strain ARA6 (CP011947), Haloferax lucentense strain DSM 14919 (AOLH00000000), Haloferax volcanii strain DS2 (CP001956) and Haloferax prahovense strain DSM 18310 (AOLG00000000).
To estimate the mean level of nucleotide sequence similarity at the genome level between strain Arc-Hr T and the four closest species with an available genome, we used the Average Genomic Identity of Orthologous gene Sequences (AGIOS), in a laboratory's pipeline. Briefly, this pipeline combines the Proteinortho  (Lechner et al. 2011 ) software (with the following parameters: e value 1e -5 , 30% identity, 50% coverage and algebraic connectivity of 50%) for the detection of orthologous proteins between genomes compared pairwise, retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity between orthologous ORFs using the Needleman-Wunsch global alignment algorithm  (Ramasamy et al. 2014). Strain Arc-Hr T genome was locally aligned 2-by-2 using BLAT algorithm  (Kent 2002; Auch et al.
Strain identification and phylogenetic analysis Using MALDI-TOF MS identification, no significant score allowing a correct identification was obtained for strain Arc-Hr T against our database (the Bruker database is constantly incremented with URMITE data), suggesting that our isolate did not belong to any known species; and consequently, spectra from strain Arc-Hr T was added to our database (http://www. medit erran ee-infec tion. com/artic le. php?laref =256&titre =urms) (Fig. 1 ). PCR-amplified 16S rRNA gene of strain Arc-Hr T (HG964472) exhibited a 99. 2% sequence similarity with Haloferax prahovense JCM 13924 (NR113446), the phylogenetically closest validly published species with standing in nomenclature (Fig. 2 ). As 16S rRNA gene sequence comparison has been proven to poorly discriminate Haloferax species, we sequenced the complete genome of strain Arc-Hr T and a digital DNA-DNA hybridization (dDDH) was made with four of the closest Haloferax species (see the part on genome comparison).
Genome sequencing information and annotation Strain Arc-Hr T 's genome was sequenced as part of a culturomic study aiming at isolating all prokaryotes species colonizing the human gut  (Lagier et al. 2016 ) and because of its phylogenetic affiliation to the Haloferax genus. Strain Arc-Hr T represents the 13th genome sequenced in the Haloferax genus. The draft genome of strain Arc-Hr T contains 4,015,175 bp with a G + C content of 65. 36% and consists of 8 contigs without gaps (Fig. 5 ). The genome was shown to encode at least 64 predicted RNA including 3 rRNA, 57 tRNA, 4 miscellaneous RNA and 3911 proteincoding genes. Among these genes, 490 (13%) were found to be putative proteins and 291 (8%) were assigned as hypothetical proteins. Moreover, 2335 genes matched at least one sequence in Clusters of Orthologous Groups (COGs) database  (Tatusov et al.
Colonies are red, smooth, shiny and measure 0. 5-1 mm. Strain Arc-Hr T has exhibited positive catalase and oxidase activities. Using API strips, positive reactions were observed for alkaline phosphatase, acid phosphatase, esterase (C4), esterase lipase (C8), leucine arylamidase, naphthol-AS-BI-phosphohydrolase, β-glucuronidase, glycerol, d-fructose, l-rhamnose, potassium 2-ketogluconate and potassium 5-ketogluconate. Strain Arc-Hr T was susceptible to rifampicin and trimethoprim/sulfamethoxazole. The genome of Haloferax massiliense is 4,349,774 bp long and exhibits a G + C% content of 65. 36%. The 16S rRNA and genome sequences are deposited in EMBL-EBI under accession numbers HG964472 and CSTE00000000, respectively. The type strain Arc-Hr T (= CSUR P0974 = CECT 9307) was isolated from a stool specimen of 22-year-old Amazonian obese female patient as part of a culturomics study.
InTroduCTIon Vision impairment is of great importance for quality of life and for the socioeconomics and public health of societies and countries. In the recent Global Burden of Disease Study (GBD) 2015, sense organ deficits including vision impairment and hearing impairment ranked second after low back and neck pain and before depressive disorders among the all-age causes for years lived with disability (YLDs) worldwide. 1 Within the population aged 65+ years, sense organ deficits were the most common causes for YLDs. In a previous meta-analysis including data and results of most of the up-to-then available population-based studies in ophthalmology worldwide, 32. 4 million people were blind (defined as presenting visual acuity <3/60 in the better eye) in 2010, and 191 million people had a moderate and severe vision impairment (MSVI; defined as presenting visual acuity <6/18 but ≥3/60 in the better eye).
MeThods 5] [6]  A brief overview is given as follows. The study was approved by the ethics committee II of the Medical Faculty Mannheim of the University of Heidelberg, Germany. Using data in the Global Vision Database, we estimated 1990-2015 trends in vision impairment prevalence and their uncertainties, by age and gender, for 188 countries in the 21 GBD regions. 7 The super region of 'high-income countries, Eastern and Central Europe' addressed in this report consists of the regions of Asia-Pacific, Australasia, North America high income, Western Europe, Central Europe and Eastern Europe. The distribution of countries within these regions is presented in table  1. Using definitions and an analytical framework similar to that applied by Stevens and colleagues, we used statistical models to estimate the prevalence of two of the core categories of vision impairment: blindness, defined as presenting visual acuity worse than 3/60 in the better eye, and MSVI, defined as presenting visual acuity in the better eye of worse than 6/18 to 3/60 inclusive.