dataesr/scientific-paragraphs-categorization huggingface dataset:
X-ray crystallography A single colorless prism-shaped crystal (0. 20 mm × 0. 15 mm × 0. 15 mm) was selected and used for data collection using a Bruker Nonius Apex II CCD (Mo-Kα radiation, λ = 0. 71073 Å) operating at T = 115 K. The total number of runs and images was based on the strategy calculation from the program Apex2  (Bruker, 2014). Cell parameters were retrieved and refined using the Saint software  (Bruker, 2013). Data reduction was performed using the Saint software, which corrects for Lorentz polarization. The structure was solved by charge flipping algorithm methods with Superflip  (Palatinus and Chapuis, 2007; Palatinus and van der Lee, 2008; Palatinus et al. , 2012)  and refined by full matrix least squares on F 2 using ShelXL  (Sheldrick,.
Acknowledgments: The authors gratefully acknowledge the  Cheikh Anta Diop University of Dakar (Senegal) , the  Centre National de la Recherche Scientifique (France) , and the  University of Bourgogne Franche-Comté (Dijon, France). We also thank  Mr. Marcel Soustelle ,  Mrs. Marie-José Penouilh , and  Dr. Myriam Heydel  for elemental analyses, 77 Se NMR spectroscopy measurements, and  ICP-OES determination , respectively. We are also grateful to the two anonymous reviewers for their comments that helped improve the content of this article.
Acknowledgements This work was supported by the  Champagne-Ardenne Research Council  through  NANOSURF  and  NANOTRIBO  projects with grateful acknowledgement. It is also a pleasure to acknowledge the  University of Technology of Troyes ,  Grand Troyes  and  Conseil Départemental de l'Aube  for financial support through  CompNano  project. J. Zhou  acknowledges the MESR for the financial support of his PhD.
Measurement of docetaxel and trastuzumab concentrations Docetaxel concentration was measured using HPLCultraviolet (UV) method after liquid/liquid extraction using a C18 column (25 cm ×4. 6 mm, 5 µm; Waters Corporation, Milford, MA, USA). 13 The mobile phase was composed of 53% of an ammonium acetate buffer (35 nM, pH 5) and 47% of acetonitrile. Samples were eluted at a constant flow rate of 1. 8 mL/min with UV detection (227 nm). Data were acquired and analyzed using ChemStation software (Agilent Technologies, Santa Clara, CA, USA). Docetaxel and paclitaxel typical retention times were, respectively, 11 and 13. 5 minutes.
In vivo distribution studies Biodistribution studies were performed weekly, after using DiR-labeled nanoparticles with subsequent imaging in MDA-MB-453 and MDA-MB-231 bearing mice. Imaging was performed on an IVIS Spectrum imager equipped with Living Image 4. 2. 1 software. DiR-labeled nanoparticles were excited at 745 nm and emission was recorded from 780 to 840 nm. Tumor targeting was evaluated by measuring the relative amount of DiR-related fluorescence in the tumor vs whole body fluorescence. Blood vessel density was evaluated at the beginning and end of treatment on three satellite mice using the AngioSense kit (PerkinElmer Inc). AngioSense ® 680 EX (100 µL) was administered via tail vein injection and imaging was performed on an IVIS Spectrum imager equipped with Living Image 4. 2. 1 software 24 hours postinjection.
g. agricultural or forest) can occasionally be flooded. For example, the GIEMS dataset (Global Inundation Extent from Multi-Satellites) and other measurements of surface water are frequently assimilated to wetland extents  (Aires et al. , 2013; Fluet-Chouinard et al. , 2015; Papa et al. , 2006; Prigent et al. , 2001 Prigent et al. , , 2012)). These two essential metrics are in fact quite distinct, as national statistics for Mediterranean countries show (Fig. 1 ), and confusing them may lead to underestimations or overestimations of the total amount of wetland habitats. One of the main reasons why RS tools often underestimate the extent of wetland habitats is that the spatiotemporal dynamics of flooded areas are difficult to tackle, even with good time series. For instance, some ephemeral wetlands are rarely flooded and are often missed by RS datasets recording "surface waters.
" Conversely, identifying and delineating flooded areas under a dense vegetation canopy are crucial for the monitoring of several Red List habitats, but still difficult to assess using optical RS data, where the presence of water is not easily detected under dense emergent vegetation (see Section 3. 2 for some possibilities offered by new Synthetic Aperture Radar (SAR) data). As a result, what is often mapped as "flooded areas" are in fact only the "open water areas," leading to an underestimation of the real flooded areas. Fig. 2  illustrates a lake in Algeria mapped using the Global Surface Water dataset, a Landsat-based approach using optical data  (Pekel et al. , 2016)  where parts that are temporally flooded are indicated as dry for more than 30 years  (from 1984 to 2015). This is contrary to local ecological knowledge of this well-known Ramsar site  (Saifouni and Bellatreche, 2014).
Of all the species that Mediterranean wetlands host, an important part is endemic and many Red List species depend on natural wetland habitats  (MWO, 2012b; Riservato et al. , 2009). Artificial wetlands host a significantly lower biodiversity (e. g. Sebastian-Gonzalez and Green, 2016) , and it is therefore important to be able to separate them reliably from natural wetlands (e. g. distinguishing a natural lake from a reservoir; a natural lagoon from a salina). But, in practice, telling the difference based on RS data alone is not always possible. Use of object-based classifications or of a predefined matrix to discriminate natural from man-made wetlands have been proposed  (Camilleri et al. , 2017). However, natural and artificial wetlands are often similar in shapes and updating an artificial wetland matrix can pose a problem for routine monitoring over large geographical scale comprising several hundreds of sample sites.
Based on these characteristics, many of which are common to other arid and semiarid regions beyond the Mediterranean basin, we can identify three challenges for the use of RS to monitor trends in Mediterranean wetlands: (1) the delineation and separation of habitat types; (2) the mapping of the water dynamics inside wetlands; and (3) the detection of trends over time with respect to a naturally occurring variability. In the next sections, we demonstrate how these challenges have previously led to errors in the interpretation of RS data for Mediterranean wetlands monitoring, how some challenges have been recently overcome and which other ones remain to be addressed. Table1 presents a schematic guide to how these issues will be covered.
g. agricultural) classes. This was for instance the case in France where wet meadows, nationally one of the most important wetland types in terms of area, ended up being lumped with dry meadows in CLC, and therefore not identified as "wetlands" in the final maps  (Perennou et al. , 2012). From an ecological point of view, a more detailed nomenclature is appealing because its application may provide better estimates of biodiversity and ecosystem services. However, use of ancillary data is then necessary to produce reliable maps from RS data. Ancillary data may include preexisting local land cover maps, in situ data, literature, VHR images and contextual data (e. g. topography, hydrology, precipitations). Collecting this information may be realistic when working at the scale of a site level or for a limited number of habitats, but less feasible at large scales.
For instance, of the 103 different classes developed in GW-II, the 55 classes for wetlands comprised many classes that eventually proved difficult or even impossible to segregate using RS data alone. In theory, the hierarchical structure should have allowed mapping at higher level only, when the information available did not allow separating finer (lower-rank) classes. However, in practice, the lack of ancillary data often translated into detailed maps of high uncertainty although confusion matrices (see below) were developed. Unfortunately, this uncertainty is rarely estimated when mapping biodiversity and ecosystem services (e. g. Rocchini et al. , 2011) , resulting in visual representations that may be powerful, but include lots of unacknowledged errors  (Hauck et al. , 2013). This should eventually be overcome by not placing on the mapping operators any pressure for always having final results using the most precise habitat type level, rather accepting maps with less precise classes that are more accurately mapped.
Another approach that is particularly useful to document habitat transformation consists of using the Earth Observation Data for Habitat Monitoring (EODHaM) system developed by  Lucas et al. (2015). Using the hierarchical land cover classification system(LCCS)from the Food and Agriculture Organization (FAO), this approach uses a combination of pixel and object-based procedures. The first four levels of the FAO LCCS can be obtained based on simple rules that can be quantified by RS: vegetated vs non vegetated areas, herbs vs trees, terrestrial vs aquatic, cultivated vs natural, etc. Combined with expert local knowledge (e. g. available land cover land use maps), these methods are also applicable to other nomenclatures (e. g. EUNIS, Annex I) for generating habitat maps. An additional module quantifies changes in the LCCS classes and their components, being particularly useful to monitor ecosystem evolution and support decisions relating to the use and conservation of protected areas, including wetlands.
While this approach is reasonable for detecting and delineating natura habitat types that are unlikely to change much over a few years, it leads to systematic over estimations of specific habitat classes in agricultural landscapes with a highcrop rotation frequency, such as rice. Flooded fields that are used for rice production are counted as wetlands and by aggregating images of multiple years,the total surface covered with (flooded) rice fields in any year is summed up, rather than averaged. This can lead to an overestimation of rice fields up to three times their actual surface, e. g. , in CLC  (Perennou et al. , 2012). However, the availability of new optical and SAR RS data with very high temporal resolutions (Landsat-8, Sentinel-1 and Sentinel-2) allows to better capture interannual dynamics of habitats such as ricefields (Fig.
BOX 1 A Quality Control Exercise of Mediterranean Wetland Mapping Using RS Data-The GlobWetland-II Case Study The GlobWetland-II (GW-II) project (ESA DUE project, 2010-14) aimed at producing a homogeneous assessment of 284 wetlands spread all over the Mediterranean region (Fig. 5 ) and their change over time  (1975-1990-2005) , using Landsat imagery. The approach followed in the project accumulated uncertainties due to many of the challenges presented in this chapter. In this box, we present an analysis of the Mediterranean Wetlands Observatory (internal document) in which we quantified the errors of the habitats mapped in the GlobWetland-II project. The quality control consisted of the scrutiny of the database on three possible inconsistencies: (1) unrealistically large changes in habitat surface; (2) uncommon transitions in habitat types; and (3) mismatch of the habitat type identification with habitat type observations from the field, the literature and the Ramsar database.
e. a natural wetland habitat loss of 13% instead of 10% in 30 years). The errors also caused an underestimation of the overall gain in human made wetlands which turned out to be +159% instead of the originally reported +54%. A re-analysis of the whole dataset had to be undertaken, using new internal decision rules of the Mediterranean Wetlands Observatory based upon the lessons learnt from the first, flawed analysis (e. g. Beltrame et al. , 2015; MWO, 2014). The steps followed can be viewed as a quality control for end products, i. e. , both for maps and for key indicator values, such as the natural and artificial wetland surface at each date. This example of a posteriori quality control clearly demonstrates the need for wetland expertise and data to validate the remotely sensed produced maps.
In the past, due to limited availability of cloud-free optical images, "water dynamics" maps have been produced with as little as two images from a hydrological year (GlobWetland-II, 2011), which is not sufficient for monitoring short-term dynamics of wetland hydrology having a seasonal water regime. SAR satellites provide an improvement for mapping open water dynamics throughout the year due to their independence of daylight and cloud cover. Furthermore, some sensors offer the ability to detect water overgrown by vegetation. Although several SAR satellite missions have been launched in the past decades, ERS, ENVISAT ASAR, TerraSAR-X and ALOS PALSAR to name a few, water body monitoring was hindered by infrequent data acquisition schemes and acquisition cost. General overviews on the use of SAR for wetland monitoring and water mapping are provided in  Brisco (2015)  and  White et al.
(2015). A rather large and consistent historical image archive is available from the ASAR C-Band sensor aboard ESA's ENVISAT satellite, which was operated from 2002 to 2012. In particular its Wide Swath Mode (WSM), although not optimal due to its limited VV polarization and low spatial resolution (150m), has been successfully used in several water mapping studies (e. g. Bartsch et al. , 2008; Kuenzer et al. , 2013; Matgenetal. ,2011; Schlaffer et al. , 2015) and also specifically to characterize wetlands by  Schlaffer et al. (2016). With ESA'sSentinel-1mission,currently consisting of two satellites with a combined repeatrate of down to 6 days and data provided at no cost, water dynamics mapping at high resolution and temporal frequency now becomes easier and cheaper. At the time of writing still only few publications can be found on Sentinel-1 water mapping, but it has been used for flood detection by, e.
Nutrients cannot be directly estimated by RS, but high nutrient availability often leads to massive development of algae. Since the concentration of chlorophyll a, the main pigment in algae, can be estimated from RS data (e. g. Brezonik et al. , 2005; Matthews, 2011; Odermatt et al. , 2012; Ritchie et al. , 2003) , it often serves as an index for algal biomass and as a proxy for eutrophication. Turbidity is another common water quality parameter that can be estimated from RS data  (Dogliottietal. ,2015; Ritchieetal. ,2003). Turbidity is related to the concentration of SPM. At this moment, it is not possible to separate artificial from natural turbidity based on RS data alone. The suspended particles will affect the transparency of the water and therefore the transmittance of sunlight through the water, which can result in low plant productivity.
CONCLUSIONS The use of RS data for the mapping and monitoring of wetlands of arid and semi arid areas such as the Mediterranean has improved since 1980. To allow for the progress made to be included in the RStools and interpretation procedures that are being developed for new types of RS data, we here summarize the most important progress made in the form of recommendations, as well as the challenges that still need to be addressed. By identifying current best methods as well as gaps, this chapter contributes to the development of a repository for best practices of wetland RS under the framework of GEO Wetlands, and to applications beyond the Mediterranean ecoregion. In general, the uncertainties that come with any method applied to RS data can be greatly reduced by integrating ecological expertise and ground truth data in the different steps of the technical process.
This chapter uses several ways to do this. To increase accuracy of RS products, ecological understanding of the habitats and their dynamics is required. This can be achieved by developing RS products through active collaboration with ecologists and site managers. This would allow RS experts to improve monitoring tools and ecology experts to recognize both the value and the limitations of RS data, as recommended by  Skidmore et al. (2015). The development of a robust habitat nomenclature for Mediterranean wetlands that would fulfil the needs of scientists, managers and decision makers is a tricky task, especially for an application to areas where we can only rely on RS data for monitoring these habitats. Habitat maps using typologies with many classes require enhanced validation procedures with ancillary data or expert knowledge.
ACKNOWLEDGEMENTS This work was partly supported by a suite of completed and ongoing projects, namely GlobWetland2 ( GW-II ),  GlobWetland-Africa , SWOS and ECOPOTENTIAL. GW-II was a  European Space Agency (ESA-DUE ) project, in partnership with the Ramsar Convention on Wetlands and the  Mediterranean Wetlands Observatory. GlobWetland Africa  is an Earth Observation application project funded by the  European Space Agency (ESA)  in partnership with the African Team of the Ramsar convention. SWOS (Contract No. 642088 ) and ECOPOTENTIAL (Contract No. 641762 ) are ongoing projects under the  Horizon 2020 Programme  funded by the  European Commission. SWOS and  GlobWetland Africa  are partners to the  GEO-Wetlands Initiative , to which this study contributes. The authors are grateful to the Editor and two anonymous reviewers for suggesting substantial improvements.
Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications. Narcolepsy is a rare central hypersomnia (CH) with an estimated prevalence of 0. 02% in the European population  1. The diagnosis of narcolepsy is challenging for several reasons. First, excessive daytime sleepiness (EDS), the key feature of narcolepsy, is shared by many other central hypersomnias and also a prevalent life-style consequence of insufficient sleep in our modern societies. Second, various forms of narcolepsy exist and only in narcolepsy type-1 (NT1, formerly referred as narcolepsy with cataplexy) low or absent cerebrospinal fluid (CSF) hypocretin levels serve as a specific biomarker  [2] [3] [4]. In contrast, in narcolepsy type-2 (NT2) or other variants of central hypersomnias (i.
e. idiopathic hypersomnia or hypersomnias associated with psychiatric diseases), specific biomarkers are absent  5. Third, the current international diagnostic criteria are based mainly on clinical experience but large multicenter international data are still missing. Even though international diagnostic criteria have been published, they may not be used in all countries. For example, CSF-hypocretin measurement is not available in many centers. Therefore, the European Narcolepsy Network (EU-NN), an association of leading European sleep centers, launched the first prospective European web-based database for narcolepsy and related disorders which allows collection, storage and dissemination of data on narcolepsy in a comprehensive and systematic way  1. Currently this database includes 317 mixed types of variables per patient (e.
g. , categorical variables and discrete/continuous numeric variables from questionnaire data like features and history of cataplexy, laboratory data of multiple sleep latency test (MSLT) and polysomnography (PSG), biomarkers like CSF hypocretin levels and HLA DQB1*06:02) and 1380 patients from 26 centers with a number of missing data and multicollinearity, making it difficult to analyze with conventional analytics methods. One major goal of the EU-NN database is to identify distinctive phenotypes of central hypersomnias and to identify new biomarkers specific for these phenotypes. Machine learning (ML) tools are becoming increasingly popular in medicine as these methods are able to detect patterns of symptoms and unveil information that is not visible for humans. Unlike human knowledge machines will extract their own information based on an iterative information processing.
ML is specifically powerful in achieving classifiers (i. e. NT1 vs NT2) when extracting hidden information from big and disparate data sources like the EU-NN database at machine scale, i. e. , it frees from the limitations of human scale thinking. Boosting is one of the most powerful ML methods for selecting features and weight their predictive contribution to the classifier. It combines the outputs of many weak learners to produce a powerful "committee"  6, 7. The predictions from all of the weak learners are combined through a weighted "majority vote" to produce the final prediction  7. As a decision tree-based ensemble method, boosting can naturally incorporate mixtures of numeric and categorical predictor variables and missing values  7. It can reduce multicollinearity and overfitting (overfitting is a common problem in ML which means that the model can fit the training data well but cannot generalize to data outside the training set, such as the data in the testing set or data from new cases) problems through shrinkage strategy  7  , making it more resistant to multicollinearity than other ML methods like neural network  8.
The merits of boosting may render it a suitable and powerful algorithm to model the EU-NN database for empirical classification of narcolepsy. In this data-driven study we use boosting to explore the predictive features of narcolepsy NT1 and NT2 from EU-NN database. We choose Stochastic Gradient Boosting (SGB) model, an advanced supervised learning (i. e. , the instances are given with known labels which are NT1 and NT2 in this study) method in the boosting algorithm family that usually outperforms others  9, 10  as it combines boosting with bootstrap averaging  10. SGB model has good interpretability, i. e. , it can identify the variable importance  11. The resulting relative variable influences can be used for feature selection. Therefore, SGB is a model with built-in feature selection, which is more efficient than the usual feature selection procedures (i.
e. , wrapper methods and filter methods) in which the searching routine for the right predictors is external to the model 12. To do the feature selection with SGB, we first construct a SGB model for the classification of NT1 and NT2. Next we evaluate the performances of the built SGB model in the testing set. The model correctly 'learns' the clinical features of NT1 and NT2 if it gives good performances. By checking the variable importance ranked by the model, machine could feed us back with clinical features of narcolepsy on machine scale (i. e. , machine tells human how it ranks the mixed features of narcolepsy in EU-NN database).
95-5. 62, P = 0. 046). The mean REM sleep latency of MSLT. Our finding that mean REM sleep latency contributes to the classification of NT1 and NT2 is unexpected, as only the number of sleep-onset REM sleep period (SOREMP) is a well-known disgnostic marker for narcolepsy  13. We then perform logistic regression with NT1 and NT2 as dependent variable and the number of SOREMP and the mean REM sleep latency of MSLT as explanatory variables, to confirm the findings of the machine. Complete MSLT datasets are available from 321 NT1 and 83 NT2 patients to build the model. The model passes the Hosmer-Lemeshow goodness of fit test (P = 0. 796). Both the mean REM sleep latency and the number of SOREMP are strongly significant predictors (P < 0. 001 for both variables) to classify NT1 and NT2, and the ORs are 0.
874, Kappa = 0. 304, accuracy = 0. 874, sensitivity = 0. 987, specificity = 0. 231). The values of AUC and accuracy are still good, but the dramatic decrease in Kappa (0. 9778 vs. 0. 304) and specificity (1 vs. 0. 231) indicate that the high AUC and accuracy are mainly caused by the imbalanced dataset (i. e. , 149 NT1 vs. 26 NT2 patients in the testing set). So even a model predicting all the patients as NT1 can yield an accuracy of 0. 85 and a sensitivity of 1 but a specificity of 0. In this case, Kappa and the balanced accuracy (i. e. , the mean of sensitivity and specificity which is 0. 609 here) are better metrics to evaluate our new model as a poor classifier for NT1 and NT2. We propose to consider the selected features as reliable only when the models have good performances. Next we remove hypocretin level and update our model.