dataesr/scientific-paragraphs-categorization huggingface dataset:
Parameter Value Parameter Value Parameter Value α 1 1 δ 4 ε x a 0. 001 α 2 30 k 20 ε x n 0. 001 α 3 1 ν 8 ε y a 0 α 4 7 ν th 0. 25 ε w 0 γ 1 ω 0. 6 We may note that the numerical result given in Figure  8  has a rigorous nature as is visible by a careful estimation of the error of Matlab approximation. For example, considering Equation (  13 ), we may provide (following the idea of  [41] ) the detailed analysis of the error E n = y n -y(t n ) , where y(t n ) is the value of the true solution at point t n and y n is the approximation of the solution at point t n , showing that |E n | < 4. 5 • 10 10 • h 4. Taking h = 1 1000 the error is smaller than the variation in the second derivative. Moreover, the rounding error can be neglected because the machine epsilon (see  [42] ) is sufficiently small compared with h.
Pelagic Habitat indicator 1 (PH1) "Changes in phytoplankton and zooplankton communities" uses the relative changes in abundances of lifeform pairs based on functional traits to indicate ecological change  ( Tett et al. , 2008; McQuatters-Gollop et al. , 2015; OSPAR, 2017b). For example, in the pairing of diatoms and dinoflagellates, the dominance of the latter could indicate eutrophication resulting in less desirable food webs. Pelagic Habitat indicator 2 (PH2) "Changes in Phytoplankton Biomass and Zooplankton Abundance" provides an indication of deviations in total biomass or abundance of plankton from the assumed natural variability in time-series  (OSPAR, 2017c). The REPHY is implemented and managed by the French Research Institute for the Exploitation of the Sea (IFREMER). The database of RESOMAR-Pelagos hosts plankton data collected from most of the French coastal marine stations and observatories.
From the RESOMAR-Pelagos database, we filtered for stations where samples were collected and analysed using consistent methodology, were sampled at a minimum monthly frequency, which contained minimal gaps in the sampling, and which simultaneously sampled nutrients and hydrological factors. This selection resulted in the station of  SOMLIT-Astan (2007 -2013, fig. 1) , a coastal long-term monitoring station situated 4. 6 km from the coast that is characterized by permanently mixed waters with limited continental influence. Twice a month, seawater samples are collected at 1 m depth using a 5 liters Niskin bottle for phytoplankton analysis. Samples are fixed with acid Lugol's iodine solution and then stored according to the methods described in  Sournia (1978). Cell counts are made under an inverted light microscope at 200-400x magnification.
Further details on phytoplankton quantification and identification protocols for SOMLIT-Astan can be found in  Guilloux et al. (2013). Environmental data from the site are collected by the Station Biologique de Roscoff and hosted by the SOMLIT (Service d'Observation en Milieu LITtoral, INSU-CNRS) database; they were retrieved from their online platform (http://somlit. epoc. u-bordeaux1. fr/fr/). Data on salinity (psu), temperature (°C), inorganic nutrients (ammonia, nitrate, nitrites, silicate, phosphates; in µmol L -1 ) and oxygen (ml L -1 ) were used in the analysis. In the Bay of Biscay, data from two REPHY sites, Ouest Loscolo and Le Croisic, were made available for analyses  (Catherine Belin, pers. comm. ). These sites are shallow, meso-to macrotidal, with a moderate wave exposure at 2.
9 km from the coast for the Ouest Loscolo station and 0. 2 km from the coast for Le Croisic station. They are both under the influence of riverine output, namely from the Loscolo and the Loire River. Water samples are collected on a bi-monthly basis at the surface in order to determine phytoplankton cell abundance and taxonomic composition. Phytoplankton samples are fixed with Lugol's solution (neutral or acidic) and counted according to the Utermöhl method  (Utermöhl, 1958). Further details about sampling and processing of phytoplankton and physico-chemical parameters are available in the literature  (Neaud-Masson, 2015). The level of taxonomic identification depends on the analytical method used and the experience of the phytoplankton analyst. Changes in the taxonomic analyst may lead to heterogeneous data regarding taxonomic classification and hence to a misinterpretation of phytoplankton time-series  (Hernández-Fariñas et al.
, 2013) ; this is true of many multidecadal datasets. Consequently, although phytoplankton data in SOMLIT-Astan has been collected from the year 2000 onwards, only the period 2007-2014 was considered for analysis since the same two operators worked closely for the analyses of the samples during this time-period. Across datasets, most taxa were identified to the species level but for consistency and again to reduce bias from misidentification, abundance data (expressed as number of cells per liter) of the taxonomic units were grouped to the genus level and pooled monthly. If the identification was at a lower taxonomic level (Class, Phylum, as is the case for the smaller species), then these were also taken into account but cells that where classified as "non-identified" were not used in the analysis.
Data analysis To select the most appropriate indices for the assessment of GES for pelagic habitats, diversity indices were tested on the three sites in a range of simple and multivariate analyses. After pre-selecting diversity indices from the literature, we have adopted some criteria that biodiversity measures should satisfy for their use in quality assessment  (van Strien et al. , 2012; Buckland et al. , 2005). The final indicator should (1) provide ecological information on the state condition of phytoplankton communities using several aspects of biodiversity: richness, dominance, and evenness; and detect significant temporal changes in the structure of the phytoplankton community (2) be mathematically consistent, (3) have a link with environmental conditions.
Link with environmental conditions Biodiversity metrics that respond differently to environmental factors can be considered complementary  (Gascon et al. , 2009; Gallardo et al. , 2011). Hence, we investigated to what extent the selected biodiversity measures reflected changes in the environmental conditions and if certain indices are interrelated. A standardized Principal Components Analysis (PCA;  Jolliffe, 1986 ) was applied to the potential environmental correlates of phytoplankton diversity to determine: (1) the environmental variables that explained the largest variation in the data set, (2) the relationships among these potential environmental predictors, and (3) how the scores of the principal components were related to the phytoplankton diversity metrics. The procedure was applied to each single time-series separately.
The RF is the degree of dispersion of individual genera over time in relation to the number of all the genera which occurred in the time-series. RFi = (fi/F)*100 (5) where fi is the number of occurrence of the genus i and F is the total number of occurrence of all the genera. For these analyses, only monthly abundance time-series data (at the genus level) from the Ouest Loscolo and Le Croisic site (Bay of Biscay) were considered, as these long time-series (>25 years) provided the most robust analyses compared to the shorter available data set of SOMLIT-Astan. In the graphical representations, only the top 5 genera with the highest IVI values are shown. All analyses were carried out using the software package MATLAB R2015a.
Results Species accumulation curves showed that our observed richness values likely underestimated the total richness of the phytoplankton communities (Figure  S1 ). For the three datasets, there is an increasing trend in the number of species along the time-series and the curves did not reach saturation level indicating that the total community has not been sampled yet. Using all nine indices, correlation analyses investigated the likely redundancy between indices from a mathematical perspective. Similar results were obtained for all sampling sites but only the results for SOMLIT-Astan are presented here (Table  1 ). As expected, strong correlations between diversity measures were found. This is not surprising as they represent aspects of the same phenomenon  (Morris et al. , 2014). For the richness group, the Margalef's index (d) and the number of genera (S) were highly and positively correlated (r 2 =0.
Most importantly, the index is based on within-taxon trends and requires a robust calculation where each taxon is recorded in every year. Since phytoplankton datasets are generally characterized by a small number of abundant species and many rare species, the index is likely to exhibit high variance and unstable behaviour when species are not consistently present in the community. A potential solution would be to calculate the index on only those taxa that are present in every sample but then the index would reflect trends of the subset of taxa and not the whole community, and as such, the index has limited use as a community diversity measure to assess GES of pelagic habitats. Compared to phytoplankton biomass indicators, the development of community composition indicators for water quality assessment is in its early stages.
Microphytoplankton counts obtained from three coastal time-series in the Western Channel and the north of the Bay of Biscay (fig. 1) were used here to test the efficiency and the performance of several diversity indices for assessing GES of pelagic habitats under the MFSD. More specifically, we tested these diversity indices for their ecological relevance, mathematical consistency and link to marine hydrological factors. Materials and methods 1. Phytoplankton and environmental datasets Microscopic counts of phytoplankton data from the Western Channel and the north of the Bay of Biscay, France, were collated from two sources, namely RESOMAR-Pelagos (Pelagic database of the Réseau National des Stations et Observatoires Marins; http://resomar. cnrs. fr/Base-de-donnee-Pelagos) and REPHY (Réseau d'Observation et de Surveillance du Phytoplancton et des Phycotoxines;http://envlit.
Acknowledgements I. R. and A. A. received funding from the  French Ministry for the Ecological and Solidary Transition (MTES) , the  French National Centre for Scientific Research (CNRS-INEE ,  CNRS-INSU) , the  French National Museum of Natural History (MNHN)  and the  EU DG ENV/MSFD/Action Plan Project  ( 11. 0661/2015/712630/SUB/ENVC. 2OSPAR ) Applying an ecosystem approach to (sub) regional habitat assessments (EcApRHA). A. M-G would like to thank the  UK National Environmental Research Council  for support through the  NERC Knowledge Exchange fellowship scheme. We want to thank all members of the  REPHY program  and the  SRN network of the Institute for the Exploitation for the Sea (IFREMER)  for the use of the  Ouest Loscolo and Le Croisic data  and the  RESOMAR-Pelagos and SOMLIT team  for the use of the  SOMLIT-Astan data.
If one considers only the binary data (implicitly assuming that the distance between any cities is the same), the shortest path from Busan to Almería would be crossing Chicago (one stopover vs. the two stopovers of the other possible path). However, if the distance between cities is considered, it can be easily seen that the path Busan-Copenhagen-Marseille-Almería is much shorter (~11000 vs. ~18000 Km). For an ecologically relevant example, we constructed a connectivity matrix for a hypothetical bird species living between 500 and 2000 m above sea-level, and with a typical habitat size of about 15 km 2. For this purpose, the Global Relief Database ETOPO1  39  data in the region we considered were coarse-grained to 15 km 2 horizontal resolution, resulting in 787 habitat patches (Fig. 4a ). Dispersal probability between patches was calculated as p ij = exp (-α d ij ) following ref.
35 , where d ij is the geographical distance between the patches boundaries, and α is a parameter chosen to be 0. 03 in order to have a (hypothetical) median dispersal distance of 100 km. This probability can be stored in a connectivity matrix (see Supporting Information, Fig. S1 ) and graph theory can be used to identify the habitat patches that have high Betweenness and Closeness centrality scores. Calculating BC and CC on binary and weighted versions of this dataset resulted in markedly different outcomes. None of the 20 habitat patches with highest BC and CC in the binary network (Fig. 4b, c ) match the ones obtained from the analysis of the weighted network (Fig. 4d, e ). Note that to calculate Betweenness and Closeness centralities in weighted data we first inverted the edge weights using log(1/p ij ) (see next section for a detailed explanation).
5b ). Using a real-world case, the impact of not reversing edge weights can be illustrated using the Global Mammal Parasites Database (GMPD, https://parasites. nunn. lab. org) containing data on 542 primate species and their 750 parasites (ref. 40 and references therein). To identify the species mediating the transmission of parasites (similarly to ref. 41  ), a connectivity matrix linking primates that have been found to host the same parasite was built (see Supporting Information, Fig. S2 ). Edge weights were defined as the number of shared parasites between any two species. Therefore, edge weight is directly proportional to the relationship strength between two species and, as in the previous example, the quantity should be transformed before calculating the shortest paths. In Fig. 6a  we show the lack of correlation between species rankings based on the centrality scores calculated on modified and unmodified edge weights.
Without that edge transformation, the path ABC will be the sum of the probabilities of paths AB and BC, resulting in the identification of most improbable paths as those more central (see ref. 24  for a detailed explanation, and Fig. S6  for an example using the ETOPO1 dataset). one or all shortest paths?. In most networks, whether binary or weighted, there may be more than one shortest path connecting any two nodes. To account for this fact, an alternative definition of Betweenness centrality based on random walks  43  and a generalization of node centrality that considers both edge weight and number when calculating centrality measures  35  have been developed. Although the discussion of the pros and cons of the different formulations falls beyond the scope of this study, we encourage the reader to be aware that considering a single or all shortest paths may also introduce differences in the resulting centrality values, and the decision should hence also be reported.
Acknowledgements This work was supported by the  Institute for Basic Science (IBS) ,  Republic of Korea , under IBS- R028-D1. AMMG is supported through a  Marie Skłodowska-Curie Individual Fellowship  ( H2020-MSCA-IF-2015-704409 ), and thanks the  Danish National Research Foundation  for its support of the  Center for Macroecology, Evolution and Climate  (Grant number  DNRF96 ). We thank  Sonia Agüera-González  ( @immunosoni ) for the illustrations in Fig. 3 (https://www. behance. net/soniaguera6595).
The data were considered significant only when P ,. 05. Analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC). The study was approved by our institutional review board (CPP Est II) and the Advisory Committee for Data Processing in Health Research (Comit é Consultatif sur le Traitement de l'Information en Mati ère de Recherche dans le Domaine de la Sant é).
Discussion Currently, the treatment of BPDCN remains challenging for physicians. According to the literature, the best OS is observed in BPDCN patients who receive allo-HCT in first CR. 4, 9 We conducted this retrospective study in an attempt to identify the best first-line treatment to obtain the maximum of patients in first CR who might then be eligible for allo-HCT. Our data also explore the clinical and biological presentation of this rare disease based on the French BPDCN network data collection, which achieved almost exhaustive analysis of French cases over a 10-year period. Cases are now more easily diagnosed, taking into account the recent diagnostic criteria, including new pDC markers. 2, 8 In our series, most diagnoses were centrally reviewed (cytology and immunophenotyping), and we confirm the rarity of the disease, with 10 to 15 new cases identified per year in France.
Acknowledgments The authors thank  Fiona Ecarnot  for her help preparing the manuscript,  Louis Benazet  for collecting data,  Aurore Pugin  for statistical analysis, and  Franck Leroux  for data management, as well as the  Groupe Français d'H ématologie Cellulaire , the  Groupe d'Etude Immunologique des Leuc émies , the  Groupe Français de Cytog én étique H ématologique , and the  Soci ét é Française d'H ématologie. This study was supported by the  Institut National du Cancer  ( PRT-K-15-175 ), the  Ligue Contre le Cancer Conf érence de Coordination Interr égionale du Grand est Bourgogne Franche-Comt é, and the association Cent pour Sang la Vie.
In addition, samples were purified using a different purification line than  Caracausi et al. (2016). Taking together these considerations, we conclude that the dataset reported in the present study is more appropriate to constrain the source of volcanism in the Eifel region. Taken together, our result show the mantle reservoir sampled by the Eifel volcanism to have a geochemical signature ( 40 Ar/ 36 Ar, 129 Xe/ 136 Xe, Ne isotope composition; Fig. 1  and 4 ) that is similar to the convective upper mantle, with low Pu-Xe/(Pu+U)Xe. Despite large error bars at the 1-sigma level, heavy Xe excesses measured in popping rocks, thought to best represent the upper mantle, appear to be dominated by 244 Pu-derived Xe  (Péron and Moreira, 2018). We calculate the I-Xe/Pu-Xe and Pu-Xe/(Pu+U)Xe of the popping rock source to be 3.
Acknowledgments We gratefully thank  Rita Parai  for providing us with the MATLAB code developed by  Parai  and Mukhopadhyay (2015). Sujoy Mukhopadhyay  is thanked for his fruitful insights into how to improve our manuscript. We also thank  Manuel Moreira  for his review, and for having initiated this study by triggering insightful discussions about the origin of volcanism beneath Central Europe. This study was supported by the  European Research Council  (grant  PHOTONIS 695618  to B. M. ). This is CRPG contribution N°2693.
Funding: This research has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  804144 ). Further funding was provided by the  French government  through the  UCA JEDI Investments  in the  Future  project managed by the  National Research Agency (ANR) , with reference number  ANR-15-IDEX-01 , and by the  ANR  under grant number  ANR-18-CE29-0004-01 , as well as by the project  CALIPSOplus , under Grant Agreement 730872 from the  EU Framework Program for Research and Innovation HORIZON 2020. A. D. G. is grateful for a PhD scholarship from the  French Ministry of Science and Education.
Methods The literature search was done in Pubmed and in Embase. For additional resources we searched manually articles from the reference lists of the selected papers and from Google Scholar. We included only studies in French or in English language meeting the following eligibility criteria: design: observational and quasi-experimental; study population: taxi drivers or taxi commuters, outcome: pollutants levels in taxicabs and/or acute adverse respiratory effects. Data was extracted using tabulation according to the type of the study (exposure or epidemiological).
Search strategy A systematic literature search was performed in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  (Moher et al. , 2009). The last update of the literature searches was made on June 5, 2018. To identify relevant studies, we combined different approaches: searching electronic databases (PubMed from 1969 to June 2018; Embase from 1967 to June 2018)), relevant Internet resources (Google Scholar from 1972 to June 2018) and visually scanning articles from the reference lists of the selected papers. We began by defining the concepts of the research questions: "taxi drivers", "occupational exposure", "air pollutants" and "respiratory health". Then, for each concept, we selected both indexing terms and keywords and we compiled the terms relating to the same concept by using OR as Boolean operator, in order to make searches larger.
Data analysis and quality assessment After developing a preliminary synthesis using tabulation, we explored relationships within and between studies by qualitative descriptions. We assessed the quality of the included observational studies using an adjusted version of the Newcastle-Ottawa Scale based on three perspectives (selection, comparability and outcomes) separately for exposure and epidemiological studies. Joanna Briggs Institute Critical Appraisal Checklist was used for the quasi-experimental studies. Overall, they were based on 7 and 9 items. Each article was graded as a good (score ≥78%), as a fair (score [44-77] %) or as a poor quality study (score ≤ 43 %) (online supplementary Appendix C).
Acknowledgments Christine Azar, PhD  student in a cotutelle program between  Lebanese University  ( Ecole Doctorale en Sciences et Technologie, Hadath, Lebanon ) and  Paris-Est Créteil University (Ecole Doctorale de Santé Publique ,  EA 7379 , Créteil, F-94010, France), who helped in the literature searching. Patient consent Not required. Contributors MH wrote the manuscript as part of her PhD research under the supervision of IM, LBC and NS. MH and LBC were involved in the selection process and the data extraction. IM,  LBC ,  ACP  and  NS  revised the article critically by giving comments and feedbacks to the review write-up. All authors contributed to the manuscript development.
Funding This work was supported by  ADEME  (Contract n°  1666C0006 ), and the  Research call Programme National de Recherche Santé Environnement Travail (ANSES -French Agency for Food, Environmental and Occupational Health & Safety). MH is co-funded by the  French Environment & Energy Management Agency (ADEME)  and by the  National Council for Scientific Research -Lebanon (CNRS-L). The co-founders had no involvement in the conduct of this article.
Although the importance of multi-compartment modeling has been recognized  [3] [4] [5] [6] [7] , most of these investigations use multi-shell diffusion acquisitions, since the additional shells allow for more robust and accurate estimation of model parameters. Such acquisitions, however, remain clinically infeasible for the time being. Single-shell DTI is the predominant type of diffusion MRI acquired in a busy clinical setting. Additionally, large cancer databases, such as the Adult Brain Tumor Consortium (ABTC) (http://www. abtconsortium. org) have single-shell DTI data only. The ability to interrogate the peritumoral manifestation of free water in such large datasets, along with the wealth of clinical data available, including pharmacological and surgical treatment responses, quality of life measures, and survival rates, could be crucial in assessing neoplastic infiltration and edema, potentially providing new radiomic features for tumors.
Experiments on simulated data Creation of simulated data: To address the lack of ground truth, especially in edematous and infiltrated regions, we simulated data with varying ground truth mean diffusivities, anisotropies, and free water volume fractions. These simulated datasets follow a bi-compartment model where one of the compartments represents tissue and the other is isotropic with a fixed diffusivity (3. 0 x 10 -3 mm 2 /s). The simulation of the weighted images was obtained using the bi-tensor model available in Dipy's multi-tensor simulator  [20]. For simulating an unweighted (b0) image, S0, we calculated the transverse magnetization of a spin-echo experiment as a linear sum of the contribution of each compartment as proposed in Phantoms  [21] :  [6]  where   is the volume fraction of white matter,   is the volume fraction of CSF,  is the proton density, and TR/TE are repetition and echo times.