dataesr/scientific-paragraphs-categorization huggingface dataset:
fao. org/figis/vrmf/finder/search/#stats). For liners, for which no such list was available, we instead computed the geometric mean of the tonnage of the 50 largest EU vessels (deemed to be representative of the EU industrial fleet; only 47 records available in the case of WCPFC) from each database of the three regional fisheries management organizations under which EU agreements operate (Atlantic Ocean: ICCAT; Indian Ocean: IOTC; Pacific Ocean: WCPFC). For each vessel type, we then fitted an exponential regression (Figure  2 ), which we used to convert numbers of vessels to GRT capacities (due to the low number of data points, we did not perform any analyses of the normality of the residuals). Finally, non-tuna vessel capacity was also provided in numbers of vessels in seven cases, and we used correspondences given in other agreements for the same type of gear or species at the same period, and as much as possible, in the same region.
Fees In order to account for inflation over a given time-period, it is necessary to convert nominal values (i. e. , the actual price in a given year) to real values that are comparable. All values in this paper are therefore given in real 2012 EUR (1 EUR = 1. 28 USD), rather than nominal EUR. From a European perspective, this conversion of the fees from nominal to real values required us to apply a Consumer Price Index (CPI) 'deflator' to nominal values: Real value i ~nominal value i CPI i CPI 2012 ð1Þ where i represents the year for which the nominal value is converted into 2012 real value. We extracted annual CPI data from the International Monetary Fund's World Economic Outlook (http://www. imf. org/external/ pubs/ft/weo/2012/02/weodata/index. aspx) for 'Advanced Economies'. Note that this 'Advanced Economies' time-series exists for the entire 1980-2012 period, in contrast to Eurostat's time-series of harmonized CPI for 'Eurogroup' (http://epp.
eurostat. ec. europa. eu/portal/page/portal/hicp/data/database). However, these two time-series are very similar for the common 1992-2012 period, and thus we used the former. Estimating the real value of the fees received by the host countries required that we performed one additional step. First, nominal EUR had to be converted to local currency units (LCU; exchange rates extracted from http://databank. worldbank. org; 2012 rates extracted from www. fxtop. com, up to October 2012) via US dollars, to which the host countries' CPI was then applied, following Equation 1. However, host countries' CPI time-series were not complete in the World Bank database, and we had to perform several interpolations to fill gaps, as presented in Table  S3  (final values are provided in Dataset S1). This methodology is thought to represent an improvement on a preliminary estimate of Madagascar's income  [31] , as we believe it estimates more accurately what host countries perceive they received over time by accounting more effectively for the inflation and money devaluation in host countries.
It was developed following discussions with the European Directorate of Maritime Affairs and Fisheries (DG-MARE) and several colleagues. However, we note that although this new method provides a more accurate view of what a host country perceives it received, it does not change either the overall trend nor the discussion implied by such trends (at least in the case of Madagascar; see Figure  S3 ). Finally, global ex-vessel prices for the main species of tuna (yellowfin: Thunnus albacares and skipjack: Katsuwonus pelamis), as well as small pelagic fish, demersal fish species, shrimps and other crustaceans, and cephalopods were also extracted from a worldwide ex-vessel price database for the 1980-2006 period  [58] [59]. These ex-vessel prices were only collected for coastal countries of the European Union (Belgium, Bulgaria, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Malta, the Netherlands, Poland, Portugal, Romania, Slovenia, Spain, Sweden, and the UK), as we assumed they were the ones mainly contributing to/benefiting from public EU fishing agreements.
These ex-vessel prices, originally in nominal USD, were converted to 2012 EUR using annual USD-EUR exchange rates and Advanced Economies' CPIs (from the World Bank and the International Monetary Fund, respectively). They are provided in Dataset S1 and were used to estimate the ratio of 'fees paid by the industry/gross revenue generated'. We provide the final dataset in the appended Excel workbook (Dataset S1), as well as references for each country and year in Table  S1  and References S1. Dataset S1 also includes comments to explain specific assumptions when the general methods presented here did not apply to a specific fleet, country and/or year.
EU Subsidies Some countries (but certainly not Russia: http://ec. europa. eu/ commission_2010-2014/damanaki/headlines/press-releases/ 2013/04/2010426_en. htm) or individuals may not consider the fees paid by the EU to be fisheries subsidies. However, they clearly confer a benefit to the fishing industry, thus meeting the World Trade Organization's definition of subsidies  [60]. These subsidies -paid by the EU taxpayers for each GRT allowed to fish in host countries' waters -declined by half between 1980 and 1985 (from around 200 EUR/GRT/year [256 USD/GRT/year] to 100 EUR/GRT/year [128 USD/GRT/year]), then further declined to approximately 50 EUR/GRT/year (64 USD/GRT/year) in the 2000s (Figure  4A ). The wide ribbon around the median (which corresponds to the limits beyond which any data point would be considered as an outlier, i.
This targeted bycatch therefore represents an important part of the industry's gross revenue and must be accounted for during the negotiation of financial compensation. Although there are operating costs that the industry must cover (e. g. , wages, fuel, taxes), it is expected that the net benefit accrued to the industry represents a sizeable part of the gross revenue, hence there is some room to raise the industry fishing fees. To assess what would be a 'fair' fee is a political decision requiring better understanding of the profits made by fishing firms operating under these access agreements. However, because account books are kept private, it is currently extremely difficult to estimate the industry's net benefits, thus indirect methods via (global) fishing cost  [75]  and ex-vessel price databases  [58] [59]  would be required.
[61]line data and estimate of the 'total fee/gross revenue' ratio for the mixed agreement with Mauritania over the 2008-2010 period (details are shown in the appended Dataset S1; 'Calculations Mauritania' spreadsheet). Total fee' was calculated by multiplying the fee per unit of fishing capacity (in 2012 EUR/GT) by the amount of capacity (GT) reported to have been used in each fishing category[61]. doi:10. 1371/journal. pone. 0079899. t002.
This is a contribution from the  Sea Around Us  project, a collaboration between The University of  British Columbia  and  The Pew Charitable Trusts  (www. pewtrusts. org),which supports FLM, DC, KK, URS, DZ and DP. We would also like to thank the  Mava Foundation  (www. mava-foundation. org)for additional funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Purpose: To study the effect of intravitreal ranibizumab on idiopathic epiretinal membranes (ERMs). Methods: A retrospective cohort study on a consecutive series of ranibizumab intravitreal injections for epiretinal membranes was performed. Four cases were identified by reviewing a claims database linked to electronic medical records. All patients received a total of three 0. 05 mg/0. 05 ml ranibizumab intravitreal injections at a monthly interval. The primary outcome measure was the final best-corrected visual acuity (BCVA) at the end of the injection series, and the final central macular thickness (CMT). Results: All four patients completed 3 months follow-up after the last ranibizumab injection. The mean baseline CMT was 509 microns (SD = 111). A trend was noticed for reduction in CMT (D = 41 microns) P = 0.
Methods We reviewed an administrative claims database to identify patients with ERM who received ranibizu mab. Four consecutive cases were identified. In all four cases, pat ients had a thorough informed consent performed and understood that the treatment they were to receive was off-label for this indication given their symptoms and as an alternative for those who refused surgical intervention. Patients underwent routine best corrected visual acuity (BCVA) testing and intraocular pressure measurement, fundus fluorescein angiography, and optical coherence tomog raphy (OCT) scans with Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, California) both before and one month after the injection series. We used the built-in anatomic tracking software to measure the same central macular location at follow-up.
Discussion Based on immunop athologic studies, VEGF may play an important role in the development of ERM. 7, 8 In our present study, we evaluated the CMT and BCVA for patients with idiopathic ERM before and after intravitreal injection of ranibizumab. We found a trend in the reduction in CMT of patients with idiopathic ERM after injectio n of three doses of ranibizu mab. This reduction appears to be relate d to a decrease in macular edema, presumably related to ranibizumab's effect on vascular permeability. The memb rane was still visible in all cases and showed no appreciable change at the resolution level of spectral domain-OCT. BCVA improved by one line in three patients and was maintained at the same level in the remaining patient. No ocular or systemic adverse events were noted. There are no published data evaluating the effect of ranibizumab on idiopathic ERMs.
Lack of hematopoietic toxicity of Vectofusin-1 Hematopoietic toxicity was first evaluated in vitro by analyzing colony-forming cell (CFC) generated from CD34 + cells exposed to optimal concentrations of Vectofusin-1 during transduction. For these experiments, we used highly purified VSV-G-LV to transduce CD34 + cells because these vectors exhibit no measurable hematopoietic toxicity in this assay. 4 There was no evidence of toxicity from Vectofusin-1 either. Exposure of CD34 + cells to Vectofusin-1 did not affect their subsequent growth as CFC and myeloerythroid differentiation (Figure  4a ). Also, we did not observe any differences in the type and appearance of the CFCs transduced in the presence of Vectofusin-1 (data not shown). Genomic DNA extracted from single colonies was tested by Taqman real-time PCR (quantitative PCR) to evaluate transduction and VCNs at the clonal level.
5-3 hours at 4 °C with the pre-cooled vector/peptide mix. Cells were then washed and half of the cells were lysed to evaluate the total membrane-bound p24 (top panel). The remaining cells were further cultured for 2-2. 5 hours at 37 °C to induce viral fusion. Next, an aliquot of cells was used for the BLAM fusion assay (middle panel), whereas the remaining cells were further cultured. After 9 days, the transduction efficiency was evaluated as in a by monitoring ΔNGFR expression (lower panel). Data are expressed as the average of three independent experiments ± SD (**P < 0. 01; *P < 0. 05, Student's t-test). AZT, azidothymidine; BLAM, β-lactamase; MOI, multiplicity of infection; SEVI, semen enhancer of viral infection; VLP, virus-like particle. medium with a neutral pH. Therefore, we hypothesized that during transduction, Vectofusin-1 is able to insert itself in the viral and/or cellular membranes, leading to a modulation of the adhesion and the fusion steps of LVs with the membrane of CD34 + cells.
SELECTION OF THE STUDIES Studies were all peer-reviewed and published in English before June 2012. The PubMed and Scopus Medline databases were searched using the following keywords in text and/or abstract/title and Boolean operators: "creativity, creative thinking, creative process, unusualness, hypothesis generation, idea generation, aha, eureka, novel ideas, original ideas, originality, insight problemsolving, insight solution, artistic" AND "brain imaging, cerebral imaging, MRI, fMRI, PET, neural correlates, cerebral correlates, brain activation, functional magnetic resonance. " In order to ensure that inclusion criteria was as unbiased as possible, we did not systematically search for studies on domains that may be relevant to creativity (such as imagination, metaphors, music improvisation or expression, mental imagery, counterfactual thinking), or studies that explore various processes presumably involved in creativity (such as cognitive flexibility, inhibition of prepotent responses, working memory, planning, and so on), but such studies were included if the authors related explicitly to creativity in their work.
General principles We performed a meta-analysis of functional neuroimaging data on creativity using Activation Likelihood Estimation (GingerALE) software (http://brainmap. org/ale/cli. html; Laird et al. , 2005; Eickhoff et al. , 2009 Eickhoff et al. , , 2012;; Turkeltaub et al. , 2012). ALE is a coordinate-based meta-analysis method that uses published activation peaks reported in functional imaging studies in a normalized coordinate referential. ALE delineates the regions in the brain where convergence across all included studies is higher than would be expected by chance (null distribution of randomly generated activation likelihoods)  (Eickhoff et al. , 2009). In other words, ALE evaluates the "inter-experiment" reliability of the involvement of brain regions in given processes-in this case in creativity tasks.
Global analysis The ALE analyses were conducted using the GingerALE software v2. 2 (www. brainmap. org; Eickhoff et al. , 2009 Eickhoff et al. , , 2012;; Turkeltaub et al. , 2012). Coordinates collected from studies that were reported in Talairach space were converted into the MNI space using the tal2mni algorithm implemented in Matlab (http:// imaging. mrc-cbu. cam. ac. uk/imaging/MniTalairach). In the first step, activation foci from each included study were modeled as Gaussian distributions and merged into a single 3D volume. To address the problem of the independence of observation within the same study, we used the modified ALE algorithm  (Turkeltaub et al. , 2012)  and organized datasets according to subject groups. The algorithm also modeled spatial uncertainty  (Eickhoff et al. , 2009 (Eickhoff et al.
, , 2012)) -and thus probability distributionof each focus, using an estimation of the inter-subject and inter-laboratory variability typically observed in neuroimaging experiments, rather than a pre-specified full-width half maximum (FWHM). Thus, the number of participants in a given study influenced the spatial extent of the Gaussian function used. GingerALE first modeled the probability of activation over all studies at each spatial point in the brain, returning localized "activation likelihood estimates" or ALE values. In a second step, ALE values were compared to a null distribution created from simulated datasets with randomly placed foci in order to identify significantly activated clusters. ALE maps were calculated using 10,000 permutations. We used a cluster correction for multiple comparisons  (Eickhoff et al.
Conventional phenotypic approaches for bacterial identification For identification of probiotic bacterial strains, phenotypic tests or commercial miniaturized identification systems such as Analytical Profile Index tests are inadequate for species level resolution. In fact, it is recommended that biochemical characterization should not be used as a stand-alone approach for identification of any probiotic culture. Commercial identification systems may be useful to obtain a first tentative classification at the genus level in conjunction with primary phenotypic tests, but the identification result should in any case be confirmed by other (molecular) methods. It is important to highlight that the usefulness of (commercial) biochemical systems for identification of probiotic bacterial strains is limited, due to the high intraspecific phenotypic variability observed in many bacterial species, and due to the fact that updates of the identification databases linked to these systems are slow or even missing, are often incomplete, and that species entries are poorly documented without listing the reference strains used  [25].
Molecular approaches for bacterial identification Despite the event of sequence-based approaches, DNA-DNA reassociation is currently considered as the "gold standard" for delineation and description of new bacterial species, but is impractical in routine identification of bacterial cultures. Other molecular methods are therefore preferred provided that they offer sufficient experimental reproducibility and a proper taxonomic resolution and that they make use of updated and easily available and validated identification databases. The use of molecular approaches for identification of species used in probiotic applications is covered in large detail in several review articles  [14, 15, 26]. Sequence analysis of the partial or complete 16S ribosomal RNA (rRNA) gene is now commonplace as the first tool to use for the taxonomic positioning of probiotic cultures  [27].
Next to the universal distribution of this gene in all bacterial species, which avoids the use of group-specific protocols, other obvious advantages of using this and other sequence-based methods include the high level of data reproducibility and exchangeability. Sequencing is now commonly outsourced to specialized service providers and sequencing centers, often with satisfactory to excellent results. The major bottleneck of the 16S rRNA gene sequencing approach for nonexperienced users, however, is the taxonomic interpretation of the sequence data by comparison with public sequence databases such as European Molecular Biology Laboratory (http://www. ebi. ac. uk/embl/) and GenBank (http://www. ncbi. nlm. nih. gov/genbank/). These databases often contain loads of unreliable, poorly documented, or incomplete sequence entries, which may compromise the accuracy of the identification result.
Therefore, experienced users will have to make sure that only validated and complete 16S rRNA gene sequences are used for identification purposes and that full 16S rRNA gene sequences of multiple taxonomic reference strains per taxon (certainly including the type strain) are included in the identification database to encompass the genomic variation within the taxon. The Ribosomal Database Project (http://rdp. cme. msu. edu/) and the Silva rRNA database project (http://www. arb-silva. de/) are two free web-based applications that provide ribosomerelated data and services to the scientific community, including online data analysis and aligned and annotated 16S rRNA gene sequences. In addition to their use for straightforward identification of pure cultures, ribosomal sequences have also been exploited for culture-independent detection of probiotic microorganisms.
delbrueckii group, the Lactobacillus casei group, and the Lactobacillus plantarum group are difficult to separate even when using full 16S rRNA gene sequences  [16, 29]. In the case the probiotic strain belongs to one of these species, results of 16S rRNA gene sequence identification may only be reliable at the genus level and need to be complemented by other molecular methods to obtain classification at (sub)species level  [21]. To this end, a series of DNA fingerprinting methods including repetitive DNA element (rep)-PCR, amplified fragment length polymorphism (AFLP), amplified ribosomal DNA restriction analysis, and tDNA intergenic spacer PCR have been evaluated and optimized for species identification of most probiotic microorganisms (Table  2 ). In contrast to sequence-based approaches, however, fingerprint data are much less exchangeable and reproducible between laboratories and strongly rely on the availability of an extended and up-to-date database of reference profiles.
For this reason, fingerprint-based identifications are best performed in expert labs that use standardized protocols and have access to in-house databases for identification of unknown cultures. The use of protein-encoding housekeeping genes essentially combines the technological advantages of 16S rRNA gene sequencing and the taxonomic resolution of many fingerprinting methods. Sequencing of one or preferably multiple of these genes as taxonomic markers is a crucial step forward in the development of standardized and globally accessible methods for the identification of probiotic cultures. For LAB, the combined sequence analysis of the atpA, pheS, and rpoA genes has been successfully explored for species identification of enterococci  [30] , lactobacilli  [31] , leuconostocs  [32] , and pediococci  [33].
The current availability of complete genome sequences of thousands of bacterial strains allows to investigate new avenues for bacterial phylogeny and identification. The average amino acid identity and the average nucleotide identity  [39, 40]  are two parameters resulting from pairwise genome comparisons and averaging the sequence identities of shared orthologous genes (amino acid or nucleotide, respectively) that are already being used to compare wholegenome sequences for taxonomic purposes and, ultimately, to describe new bacterial species. Within the LAB and Bifidobacterium species used as probiotics for human applications, the complete genome sequences of more than 60 strains have been submitted to GenBank (Table  3 ). Finally, MS methods are becoming increasingly important for classification and identification of bacteria  [41].
One of these methods, MALDI-TOF MS, allows to measure peptides and other compounds in the presence of salts and to analyze complex peptide mixtures which make it an ideal method for measuring nonpurified extracts and intact bacterial cells. The resultant MALDI-TOF MS spectra can be used to generate identification libraries for simple and high-throughput identification of unknown bacterial isolates. These reference libraries can either be constructed or provided on a commercial basis, such as the one integrated in the MALDI Biotyper system (Bruker Daltonics, Bremen, Germany), or can be generated on a local basis. Such local databases have been successfully used for the identification of Lactococcus lactis  [42] , the LAB genera Leuconostoc, Fructobacillus, and Lactococcus  [43] , LAB from traditional fermented foods  [44] , and the Lb.
Yeast identification Identification and classification of yeasts have traditionally been based on morphological, physiological, and biochemical traits. Various commercial biochemical kits have been developed for rapid yeast identification, but mostly for the clinical market. Currently, different molecular biology techniques are available for identification of yeasts. A reliable starting point for the identification of probiotic yeasts is the D1/D2 large subunit rRNA gene sequence database, which encompasses virtually all known yeast species  [47]. However, due to the fact that some distinct species show low-sequence divergence, the use of D1/D2 large subunit sequences is often complemented by PCR amplification of repetitive DNA elements  [48]  and by determination of additional gene sequences, such as the internal transcribed spacer (ITS) region of the rRNA gene cluster  [49]  or protein-coding genes such as the actin gene (ACT1;  [50] ), and the mitochondrial cytochrome oxidase 2 (COX2) and cobalamin-independent methionine synthase (MET6) genes  [51].