dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work is a contribution to the  Labex OT-Med  (n°  ANR-11-LABX-0061 ) funded by the  French Government "Investissements d'Avenir" program  of the  French National Research Agency (ANR)  through the  A*MIDEX project  (n°  ANR-11-IDEX-0001-02 ). This project has been funded by the  ADACNI program  of the  French National Research Agency (ANR)  (project n°ANR-12-ADAP-0016; http://adacni. imbe. fr). We thank  ECCOREV Research Federation  ( FR 3098 ) for the financial support of this study. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrénées. A particular thanks to the molecular biology staff of the  IMBE laboratory  and the  Pythéas Institute's divers  for their support and assistance.
27 ). Dynamer 2 with a number average M n of 2500 Da disintegrated faster compared to dynamer 1 with M n 6300 Da with a lesser number of imine bonds. Thus, through modulation of imine bonds within the polymer backbone it is possible to regulate the extent of water disintegration and in turn its green dynamism. In order to investigate the self-healing properties of these green dynamers, NMR measurements were performed on freshly prepared films after immersion in water at 50 1C for 15 h and subsequent drying at 80 1C for 240 h. The M n values were calculated from NMR data and were found to exhibit a reduction from 13 000 to 7000 Da. However, after subsequent drying of the film, the M n recovered its initial value of 13 000, demonstrating that these green dynamers could also exhibit self-healing features upon removal of the disintegrating agent, in this case water.
Methods The IBVD (Influenza B in Vircases Database) study collected information on signs and symptoms at disease onset from laboratory-confirmed influenza patients of any age who consulted a sentinel practitioner in France. We compared the clinical presentation of influenza patients across age groups (0-4, 5-14, 15-64 and 65+ years), virus types (A, B) and subtypes/lineages (A(H3N2), pandemic A(H1N1), B Victoria, B Yamagata).
A(H3N2) or type B virus infection. To the best of our knowledge, no studies were carried out that explicitly aimed at comparing the clinical presentation of influenza cases by virus type and subtype using data collected over several consecutive years in the context of a countrywide, primary care practitioners (general practitioners [GPs] and paediatricians)-based influenza sentinel surveillance system. The IBVD (Influenza B in Vircases Database) study was conducted with the purpose of feeding knowledge on influenza B disease in terms of demographics, clinical presentation, and treatments administered. The project, led by Open Rome, a private research organization, was based on routine influenza surveillance data that are being collected since season 2003-2004 by the GROG (Regional Group for Influenza Surveillance) national network in France from outpatients who consulted a primary care practitioner due to an acute respiratory illness (ARI), and subsequently had a diagnosis of lab-confirmed influenza.
BL received travel grants to attend meetings from GSK and Roche. He is a member of ESWI. SVDW has received consulting fees from Danone through her research unit. She is a member of ESWI. The IBVD study was funded by GSK Biologicals SA. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. information on influenza activity in outpatients in France from October through April. At the national level, the epidemic period used for the purpose of surveillance is defined as the period during which at least two clinical indicators (including ARI by encounters) are increased by at least 20% compared to the October baseline, AND several similar influenza viruses (same type and subtype) have been detected or isolated in different regions (the % of positive isolates being usually above 10%).
Selection of Patients and Data Collection in the GROG Network Each sentinel practitioner collects information and provides nasal/pharyngeal swabs from a subset of their patients presenting with ARI within 48 hours of onset of symptoms and suspected for having influenza. The definition of ARI is as follows: sudden onset of at least one respiratory sign or symptom (cough, sore throat, shortness of breath, coryza. ) AND at least one systemic sign or symptom suggestive of an acute infectious disease (fever, fatigue, headache, malaise. ). The data collected in the standardized questionnaire (see S1 Fig) are demographics; signs and symptoms at medical encounter; influenza vaccination status; presence of underlying conditions with indications for influenza vaccination; antibiotics prescription; antivirals prescribed or taken during the two weeks prior to consultation; number of consultations with the practitioner and hospitalizations during the previous 12 months; and whether the patient was referred to a hospital following the consultation with the sentinel practitioner.
Study Database and exclusion criteria Some exclusion criteria were previously applied to the GROG Database to construct the IBVD Database. We excluded from the analyses vaccinated cases and patients whose age was missing. To limit the influence of others factors on clinical presentation, we also excluded patients coinfected with two different influenza viruses or with an influenza virus and another respiratory virus, as well as those with antivirals taken up to 15 days prior to consultation. Due to the emergence of the new A(H1N1)pdm09 influenza virus in 2009 and in order not to burden the Results section too much, we chose not to include the seasonal (pre-pandemic) A(H1N1) influenza virus subtype in the clinical presentation analysis as it is no longer circulating since the 2009 pandemic.
Sampling Procedures and Laboratory Diagnosis Specimens were transported to the laboratory by post (along with a copy of the questionnaire used for data collection), with a triple packaging system following the international guidelines for the transport of infectious substances (category B, classification UN 3373). The virological analyses were performed in one of the virology laboratories collaborating with the GROG network, which is in most cases the French National Influenza Centre (NIC) (Institut Pasteur, Paris, and Hospices Civils, Lyon). The specimens of swabbed ARI patient were analyzed for the presence of influenza viruses and if positive, in most cases, the virus type and subtype (for influenza A viruses) were determined. Following the WHO collaborating centres recommendations for virus isolation, subtyping and lineage characterization, the influenza B virus lineage was characterized only on a random subset of specimens that were sent to the NIC  [21].
Ethical Aspects and Consent In accordance with French ethical rules, verbal informed consents are sufficient for patients participating in an epidemiological surveillance study. Practitioner's participation in the study is advertised in the participants waiting room. Each practitioner takes responsibility for collecting the oral consent from ARI cases, at the moment of swab taking after providing appropriate information on the general surveillance characteristics. The consent is given orally but is registered in the data collection form accompanying the swab. In accordance with applicable laws and regulations, no clearance of an Ethics Committee is required in France for the retrospective analysis of anonymised data collected within routine influenza surveillance schemes. A limited access to the GROG database is granted to Open Rome through a dedicated research contract written for this study.
Results Overall, 15,004 influenza cases (11,416 A and 3,588 B) were included in the IBVD database during the study period: of these, 581 cases (439 A and 142 B) were dropped from the analysis according to exclusion criteria (Fig  1 ). Finally 14,423 influenza cases were included in the study (Table  1 ). 10,977 A (of which 9,450 subtyped, 86. 1%) and 3,446 B (of which 2,035 characterized, 59. 1%) influenza cases were kept for the analysis, including 4,483 A(H3N2), 4,022 A(H1N1)pdm09, 945 seasonal A (H1N1), 778 B Victoria and 1,257 B Yamagata. One influenza virus type or subtype was clearly dominant (>60% of positive swabs) in eight out of ten seasons (Table  1 ): A(H3N2) in five seasons and seasonal A(H1N1), pandemic influenza A(H1N1)pdm09 and influenza B in one season each. Influenza virus co-circulation was observed in 2010-2011 (A(H1N1)pdm09 and B) and in 2012-2013 (A(H3N2), A(H1N1) pdm09 and B).
Influenza patients who had been vaccinated or who had taken antivirals were excluded from the study because clinical presentation may be different and severity may be reduced among these patients  [27]. As vaccinated individuals were not included in the dataset and persons over the age of 65 have a high vaccination rate in France, this age group may be underrepresented in our study. Likewise, institutionalized patients, patients that go directly to the emergency rooms of hospitals when ill, and people who less frequently consult a GP when suffering from mild disease  [28]  (like healthy French young adults, tourists, etc. ) may be less likely to be included in the study database. However, there is little reason to suppose that the clinical presentation of influenza among these patients is different from study patients.
The study database belongs to the GROG network and was made available to Open Rome only for preparing this paper. The authors of this paper are not the owners of the study database, therefore they cannot make it available without restriction. The study report is now available and freely accessible on the Open Rome website (http://www. openrome. org/epi/ibvd. html). Data are available from the GROG Network for researchers who meet the criteria for access to confidential data. Any request to access the data must be addressed to Dr. Jean-Claude Soulary ( grog@.
In contrast, 4 ITS2 haplotypes were recorded, with only 3 polymorphic sites (Figure  4 ). Not only differences were very scarce among the nuclear DNA fragments, but also a single haplotype was largely dominating, contrarily to what was observed in some other scarab species (e. g. Wirta 2009). No clear association between any mitochondrial lineages was noticed and the largely dominant haplotype was also found in the three outgroup individuals (N. perotensis). As a result, we considered this marker inappropriate to investigate phylogeographics in Geomyphilus spp. and only performed subsequent analyses on the Co1 dataset. Species boundaries between G. barrerai and G. pierai were not confirmed either by mitochondrial or nuclear data. When considering concordance between morphological taxonomic assignation and molecular information, not only none of the 46 Co1 polymorphic sites and none of the 4 ITS2 polymorphic sites did segregate accordingly, but also, the distribution of the two taxa across the Co1 topology was dispersed and intermingled, without any clade grouping together all individuals of any of them (Fig.
As a result, G. pierai should be considered for synonymization with G. barrerai following the ICZN principle of priority. Subsequently considering the sample as monospecific, so much variation in the Co1 sequence dataset, obtained from the present locally-restricted sample is promising, providing basic tools for further analyses. Besides, several different mitochondrial lineages were found within the beetle sample, with no clear correspondence between mountains and genetic structure (Fig. 5 ). Although a group of four close haplotypes could appear to be if not mountain specific, more or less place specific (see the right orange and brown clade sampled from the two closest mountains Cofre de Orizaba and Sierra Negra in Fig. 5 ), all the main clades group together haplotypes from two or three more or less distant mountains.
More importantly, some Co1 haplotypes were found to be shared between individuals sampled from different mountains, including a priori non burrow-connected mountains (Malinche and Pico de Orizaba). Such an apparent lack of geographical structure at the Mexican Volcano Belt scale in a so diverse sequence dataset may suggest either that aerial spread commonly occurs or that some cryptic sub-structure exists, though no conclusion may be drawn from a so small sample. These preliminary results provided a first insight of the phylogeographic structure of the G. pierai/G. barrerai complex. A further study using at least Co1 as a marker on a much larger sample should help to elucidate dispersal routes and gene flow of these nidicolous rodent-associated beetles.
Acknowledgements The authors thank  Marco and Giovanni Dellacasa  for the identification of all the material and  M. A. Arnaud  who assisted in the English proof-reading of the manuscript. J. L. Sánchez-Huerta  and  A. Aceves-Aparicio  consent to include their pictures of G. barrerai and G. pierai as part of our paper. The fieldwork was funded by the  Instituto de Ecología , the  Societé d'Entomologie de France , with the  Bourse Germain Cousin  assigned to AAJ, and the doctoral school ED# 60  of the University Paul-Valéry Montpellier. Fernando Escobar helped during the fieldwork. We thank the CEFE platform of molecular biology for the help and the facilities for the molecular work. We thank the reviewers and editor whose comments contributed to improve the final version of the manuscript.
Expected output of ENMs are maps of suitable or unsuitable habitats for studied species  (Hirzel et al. , 2002) , which have many challenging applications in ecological studies (see  Guisan and Thuiller, 2005  for a review) and are currently recognized as powerful tools for supporting appropriate management and conservation plans of marine resources. Despite these potentialities, to date few ENMs application have specifically addressed at a regional spatial level the Mediterranean Sea  (Azzellino et al. , 2012; Azzurro et al. , 2013; Druon et al. , 2012 Druon et al. , , 2011;; Langer et al. , 2012; Sarà et al. , 2013)  and those based on distributional information of demersal species are rare  (Hattab et al. , 2013). Data collected of decadal scientific surveys in the Mediterranean Sea coupled with environmental data from remote sensing and circulation models were used to apply the ENM approach, with the aim of identifying the most suitable environmental conditions which could promote the aggregations of 0-group hake in nursery areas.
Biological traits of 0-group hake This first step of ENM consists in gathering the relevant ecological traits of hake recruits as regards to life stages and relation to their environment, starting with the identification of 0-group hake in our dataset. We used the main source of standardised information about distributions, abundances and size compositions of the demersal resources in the region: the MEDITS bottom trawl survey program  (Bertrand et al. , 2002). Specifically we use data collected from 1994 to 2011 in the different FAO-GFCM Geographic Sub-Areas (GSA) of the EU and bordering countries (Spain, France, Italy, Malta, Slovenia, Croatia, Montenegro, Albania, Greece and Cyprus) (Fig. 2 ). We considered as recruits those specimens that have settled on the bottom, becoming available to the fishing gear in well-defined habitats at the end of their larval -pelagic stage and which remain in these habitats before dispersing or migrating  (Bartolino et al.
Chlorophyll Surface chlorophyll-a concentrations (CHL) and fronts are used as a proxy for food availability for hake nurseries (see introduction above). CHL data were used at a daily time scale from the MODIS-Aqua (http://modis. gsfc. nasa. gov) (July 2002 (July -2011) )  and SeaWiFS (http://oceancolor. gsfc. nasa. gov/SeaWiFS/) (1998-2010) ocean colour sensors. The MODIS spatial resolution of 1/24°(about 4. 6 km) is used to identify meso-scale CHL fronts. The SeaWiFS resolution is double and CHL data were interpolated to the MODIS-Aqua's grid (which was chosen as the model grid). We used one set of CHL data at a time to derive a daily habitat and the sensor-specific habitat maps were then merged. This process allowed a substantial gain of habitat coverage, similar to the gain obtained by the merging of CHL data ($20%,  Maritorena and Siegel, 2005) , in relation to differences in observing time and thus in cloud cover.
If the daily data was used for front computation, we extracted a 3day mean CHL value in case daily data was unavailable in order to substantially improve both the comparison with hake biomass and model coverage. The 24 h variability of CHL level was thus stated to be low. The minimum value of chlorophyll-a horizontal gradient (gradCHL) in a 10 km-radius was also computed to estimate the threshold above which chlorophyll-a fronts are defined as relevant for hake nurseries.
Moreover, high bottom current velocities prevent from deposition of the particulate organic matter and may limit 0-group hake feeding at seabed. Hake recruits are indeed recognised to be distributed over seabed substrate with high organic content  (Maynou et al. , 2003)  and, reversely, to avoid sediment with very low organic matter content  (Lleonart, 2001). The physical data were produced by MyOcean Consortium (http://www. Myocean. eu), a marine core service within the European Global Monitoring for Environment and Security (GMES) Program whose objective is to develop an integrated capacity for ocean monitoring and forecasting. Monthly mean data of temperature and current velocities for the period 1998-2012 were extracted from a hydrodynamic model (Mediterranean Forecasting System) which has 72 unevenly spaced vertical levels and includes a variational data assimilation scheme for temperature and salinity vertical profiles and satellite sea level anomaly  (Oddo et al.
Bottom depth and seabed substrate The bottom depth from GEBCO at 1. 8 km resolution (http:// www. gebco. net/data_and_products/gridded_bathymetry_data/) was interpolated to the model's grid ($4. 6 km) and the depth of hauls at the cell centre was extracted for comparison with MEDITS values. Relatively little difference of depth was observed (quartiles are 5, 14 and 30 m, i. e. 5%, 12% and 24% in relative values respectively). The low arithmetic mean of depth difference (1. 7%) indicates that the difference is mostly due to the absence of interpolation from the cell centre (GEBCO) to the haul position (MEDITS). GEBCO depth data was used in the model to project back on the map the preferred depth range of nurseries using MEDITS data. The main seabed substrate types were analysed in the western Mediterranean Sea using data from EUSeaMap habitat classification (EMODNET Project, http://jncc.
SBC shows no correlation with hake BI during the sampling period (|r| < 0. 02, p > 0. 05), but 95% of hake nurseries during summer are in areas characterised by low SBC (under 0. 026 m s À1 , Table  3 ). Regarding seabed substrate, the observed upper BI quartile of 0group hake in the western basin showed the following distribution among the main categories: mud (42%), muddy sand (33%), sandy mud (13%), sand (10%), coarse sediment (2%), seagrass and rock (<1%). If the preferred substrate of 0-group hake contained a component of mud, the 10% of nurseries in the western Mediterranean Sea which are still on sand cannot be neglected. It is suggested that inaccuracies in the substrate classification might have occurred in the original database due to a highly heterogeneous substrate distribution on the continental shelf compared to the limited records.
Hake Nursery Habitat Day ;Cell ¼ Trophic Habitat 0=0:3=1 Ã Depth range 0=1 Ã SBT range 0=1 Ã SBC max 0=1 The model was parameterised using the preferred range of the selected environmental variables consistent with high productivity of hake recruits. The large 5th and 95th percentile values of the detected environmental conditions were chosen as boundary limits for suitable nursery habitat (Table  4 ) because (a) they are sensible values bearing in mind they correspond to the upper quartile BI values of hake recruits during their development period (0-5 months prior to sampling), (b) these values agree with the interpretation made out of the cluster classification Figs. 7  and 8. A slightly wider range (2. 5-97. 5th percentiles) was selected for bottom depth to account for differences between MEDITS and GEBCO data in shallow and deep environments.
Acknowledgements The authors particularly thank the  Ocean Biology Processing Group  ( Code 614. 2 ) at the  GSFC, Greenbelt, Maryland 20771, USA , and the  Myocean Consortium  for the quality and availability of the ocean colour and ocean modelling products respectively. We are also grateful to the current and former MEDITS Coordinators,  Maria-Teresa Spedicato  and  Jacques Bertrand  respectively, for their support to this research initiative. We deeply thank the overall  MEDITS Community  who produced and provided the data and especially  Enric Massuti ,  Pino Lembo ,  Loredana Casciaro ,  Isabella Bitetto ,  Giulio Relini ,  Corrado Piccinetti  and  Chiara Manfredi  for their particular involvement.
Multiple sclerosis (MS) lesion segmentation is crucial for evaluating disease burden, determining disease progression and measuring the impact of new clinical treatments. MS lesions can vary in size, location and intensity, making automatic segmentation challenging. In this paper, we propose a new supervised method to segment MS lesions from 3D magnetic resonance (MR) images using non-local means (NLM). The method uses a multi-channel and rotation-invariant distance measure to account for the diversity of MS lesions. The proposed segmentation method, rotation-invariant multi-contrast non-local means segmentation (RMNMS), captures the MS lesion spatial distribution and can accurately and robustly identify lesions regardless of their orientation, shape or size. An internal validation on a large clinical magnetic resonance imaging (MRI) dataset of MS patients demonstrated a good similarity measure result (Dice similarity = 60.
1% and sensitivity = 75. 4%), a strong correlation between expert and automatic lesion load volumes (R 2 = 0. 91), and a strong ability to detect lesions of different sizes and in varying spatial locations (lesion detection rate = 79. 8%). On the independent MS Grand Challenge (MSGC) dataset validation, our method provided competitive results with state-of-the-art supervised and unsupervised methods. Qualitative visual and quantitative voxel-and lesion-wise evaluations demonstrated the accuracy of RMNMS method.
, 2008a; Khayati et al. , 2008). Unsupervised techniques suffer from both intensity non-uniformity, in the whole image and in the lesion since this variability in intensities cannot be captured with a single global model. Furthermore, the properties of each image need to be specifically defined which can be difficult when artifacts have properties similar to lesions. The supervised methods use machine-learning techniques to extract relevant features (e. g. intensity or local gradient) and train automatic classifiers from manual or automatic lesion segmentation datasets. Then, the features of new images to be segmented are compared with the training sets to estimate the lesions. These methods can use different machine-learning approaches including: artificial neural networks (ANN)  (Zijdenbos et al.