dataesr/scientific-paragraphs-categorization huggingface dataset:
The image analysis software was used to automatically analyze the acquired cell images from the sample to give cell count and viability. Data were plotted either as the number of viable cells for cell proliferation or as the percent of dead cells for viability assessment. For cell proliferation, cells were seeded at 100. 000 cells and counted every 3 to 4 days. For the clonogenicity assay, 500 cells were seeded on a 6-well plate. After 1 week, cells were fixed and stained with a solution of violet crystal 0. 05% in ethanol 50%. The number of colonies is counted.
Of course it is important to check that this autocorrelation is realistic in each specific application. Some references particularly favor the detection of spatial anomalous observations. Cerioli and Riani (1999) , define a procedure based on kriging schemes and dedicated to multiple outliers while  Shekhar et al. (2003) ,  Lu et al. (2003)  and  Kou et al. (2006) , develop some simple, intuitive and robust ways to detect spatial outliers. Let us finally mention the very recent paper of  Li et al. (2013)  about outlier hypothesis testing studied in a universal setting. The basic idea of the detection algorithm we propose consists in comparing the measured concentration to some spatial prediction, following a classical jackknife type approach (i. e. leaving out the observation at the considered location from the dataset used to calculate the estimate, see  Efron, 1982).
Measurements quality control process Air Normand and Air COM make measurements freely available and update it at a rate depending on the communication media that is going to be used (i. e. , paper report, web). Of course, the most common one is the Internet: measurements are collected at various monitoring stations, stored in the database and auto matically published every hour. To ensure a high quality of service, Air Normand and Air COM have developed several procedures for data validation. First, the maintenance of the analyzers is the primary task of any air quality monitoring association. Technicians regularly calibrate the analyzers, according to the manufacturers' recommendations and the references prescribed by CEN standards (see www. cen. eu). Then technicians check measurements twice a day (morning and evening).
It is a first validation level performed on a strictly technical basis. At this stage, the physical state of measuring equipment is controlled, leading to three decisions: validation, invalidation or sometimes correction of the data in the database. The website is then updated accordingly. A second level of valida tion is performed daily by a single expert on a different temporal scale examining the sequence of measurements on each site. Finally, an environmental validation of the measurements is performed every month during a meeting of experts. At this stage, the measurement network is considered as a whole: rather than reviewing site by site, from a metrological point of view, it is examined spatially. Of course, the notion of outlier is not necessary the same as invalidated data. For example, in case of a smooth drift, an expert will invalidate the data even if it is not an outlier.
Data For each station, we have hourly or bi-hourly average PM10 concentrations. Each value can be a NaN (Not A Number) when the concentration is not available or a value with a tag: validated or invalidated. For stations IFS, CHD, MRA and LIS, where average PM10 concentrations are measured every two hours, we have duplicated measurements to obtain sets of data compatible with those of other stations. Note that for stations outside Normandy, every available data is validated. PM10 concentrations were collected over the period from January 1, 2013 to May 31, 2013 (set of 3 624 hours). We split this set in two parts: learning and test sets. An hour h belongs to the learning set if h and h-1 are validated data and without missing value (across stations). The test set is defined as the remaining hours.
For each algorithm and for each station, we calculate the residuals and then derive the empirical quantiles of level 0. 25% and 99. 75%. The parameters of the Kx-algorithms are as follows: The theoretical variogram is the exponential one with a range and a sill estimated from the data at each time period. The R package geoR  (Ribeiro and Diggle, 2001 ) is used to estimate the parameters of the exponential variogram and the R package gstat  (Pebesma, 2004 ) is used to predict the value at a given station. As previously mentioned, two variants of the kriging algorithm are used. They differ from the choice of the network considered for the computation of the pseudo-innovations. More precisely: KK-algorithm uses a kriged version of the network at time (t-1) if necessary, i. e. unavailable or invalidated data are replaced by the predictions coming from the kriging algorithm; KM-algorithm uses the predicted values at time t coming from the most recent ESMERALDA model output.
These aspects, which are typically operational ones, open up some prospects: more generally we could also think to generate maps by bootstrap (instead of jackknife) or by conditional simulation techniques in order to obtain maps free of outliers. In our case, however, and at this scale, with a small number of sites, we prefer restricting our strategy to jackknife for the time being. In addition, mention that in the future we hope to have at our disposal historical data covering a longer period and conveniently tagged concerning the outlying status. We could then divide the dataset in three subsets, as it is classical, in machine learning. The first one, the learning set, is used to compute the weights of the neighbors in the NN-method (see Section 3. 2) and the upper and lower limits of the detection procedure (see Section 3.
Acknowledgments This work comes from a scientific collaboration between  Air Normand  (see the website http://www. airnormand. fr)from the applied side and  Orsay University  and  INSA Rouen  from the academic side. We would like to thank  Veronique Delmas , from  Air Normand , for providing the problem, the data as well as for supporting the statistical study. In addition, we would also like to thank the three anonymous referees for their thorough comments and suggestions, which really helped to improve the clarity of the paper.
4 ). Thus, while there are uncertainties in the model, we believe comparison of these calculation results with data from modern volcanoes  (Mastin et al. , 2009)  suggests that T 2. 36 was deposited from an ~VEI 6 Plinian eruption. By comparison, one of the largest eruptions from the 1995-2012 Montserrat activity was the VEI 3, 20 May 2006 dome collapse  (Wadge et al. , 2014). There are no reported Holocene eruptions in the Lesser Antilles with a VEI >4  (Venzke, 2013) , and the LaMEVE (Large Magnitude Explosive Volcanic Eruptions) database  (Crosweller et al. , 2012)  lists only two Quaternary Lesser Antilles eruptions with VEI of 6 or greater: those that deposited the ca. 27 ka Grande Savanne Ignimbrite and the ca. 36 ka Roseau Tuff. Recent studies suggest, however, that both of these deposits are made up of a series of smaller eruptions  (Howe et al.
Mutations validation The ability to interpret sequence variants in a gene is linked to several considerations and validation steps  [40, 41] : One important point for assessing the impact of a variant is to access to sequence databases of patients and controls. Variants classified as pathogenic, of uncertain significance or as polymorphisms are available online in numerous databases such as the Human Gene Mutation Database (HGMD, http:// www. hgmd. cf. ac. uk/ac/index. php), Universal Mutation databases (UMDs, http://www. umd. be/), Leiden Open Variation Databases (LOVDs, http://www. lovd. nl/3. 0/home), the Single Nucleotide Polymorphism database (dbSNP, http://www. ncbi. nlm. nih. gov/SNP/), the 1000 Genome Browser (http://www. 1000genomes. org/1000-genomes-browsers), the Exome Variant Server (http://evs.
gs. washington. edu/EVS/), the Exome Aggregation Consortium (ExAC, http://exac. broadinstitute. org/). There is now a trend to make publicly accessible variants list through the development of computing tools, such as the EGL's online tool or EmVClass  [42]  used by Ankala and colleagues  [32]. Nevertheless, not all variants lists produced through MPS projects are available online and are often maintained as in-house variants databases until the data are published. International efforts, as PhenomeCentral (https://phenomecentral. org/), aim to provide a platform for secure data sharing. Another important step is the study of the variant segregation among relatives (if it has not been performed using MPS), and eventually complementary analysis such as RNA or protein studies. Such complementary analyses were required to reach a diagnostic conclusion for 115 out of 442 patients (26%) enrolled in 7 studies presented in Table  1.
More detailed functional analyses may be required for potential mutations that were not yet reported as pathogenic and can benefit from high throughput wet bench pipelines, for example to test the impact of variants on protein stability, localisation and interaction  [43] , or to correlate with phenotypes in animal models as zebrafish  [44]. Single gene testing needs to be available for all genes included in a panel, either to investigate additional relatives for assessing the pathogenicity of a variant through segregation analysis, or for further genetic counselling in the family when the causal effect of the mutation is established. It is advantageous to rely on laboratories expert in a subset of genes to validate the impact of mutations found by MPS. These laboratories are usually linked to research programs through patient's clinical and molecular databases (UMD, LOVD, Treat-NMD http://www.
Increase in technologies performance foresees GS as the main future strategy for genetic diagnosis. To increase the benefit of MPS in a diagnosis setting, improvement in bioinformatics, storage of data and interpretation of variants are areas that require further resource development, as well as expanded and multidisciplinary databases and analysis pipelines and tools, and increased training for biologists and clinicians. High throughput assay to assess RNA integrity  [55]  and protein function have been described  [43]  and should be further developed for functional validation of genetic variants. Other potential applications than diagnosis of monogenic diseases are under discussion or proposal, in particular with the idea of early diagnosis for optimal clinical management or disease prevention: preconception screening  [56] , non-invasive prenatal diagnosis using maternal circulating nucleic acids  [57] , new born screening  [58]  and predictive testing.
Up to now, high-resolution mapping of surface water extent from satellites has only been available for a few regions, over limited time periods. The extension of the temporal and spatial coverage was difficult, due to the limitation of the remote sensing technique [e. g. , the interaction of the radiation with vegetation or cloud for visible observations or the temporal sampling with the synthetic aperture radar (SAR)]. The advantages and the limitations of the various satellite techniques are reviewed. The need to have a global and consistent estimate of the water surfaces over long time periods triggered the development of a multi-satellite methodology to obtain consistent surface water all over the globe, regardless of the environments. The Global Inundation Extent from Multisatellites (GIEMS) combines the complementary strengths of satellite observations from the visible to the microwave, to produce a low-resolution monthly dataset (0:25 Â 0:25 ) of surface water extent and dynamics.
Xiao et al. (2005 Xiao et al. ( , 2006) )  analyzed paddy fields in China by identifying the flooded pixels from the difference between the Land Surface Water Index (LSWI) and Vegetation Indexes (NDVI or EVI). Sakamoto et al. (2007)  described the multi-year surface hydrology in the Mekong Delta using the MODIS reflectances. They develop a wavelet-based filter to limit the impact of the cloud cover during the monsoon season. Nakaegawa (2012) compared water-related cover types in six 1-km global land cover datasets. They include the Global Lakes and Wetlands Database (GLWD)  (Lehner and Doll 2004 ) based essentially on inventories and five estimates from visible imagery such as a MODIS classification and a SPOT-derived estimate. The global total open water areas are similar for all datasets.
However, the agreement is mediocre for wetlands, with the visible and near-infrared satellite estimate of the total wetland surface of the order of 10 times lower than the GLWD surface. There is only a 5 % chance that a wetland pixel in GLWD corresponds to one in the other five datasets. This is partly due to the difficulty of detecting water under a vegetation canopy and to the period of reference selected in the different datasets. It is noted that the GLWD can be considered to be the best 1-km global static water-related land cover dataset currently available. We tested two MODIS-derived wetland detection methods over the Mekong Delta region, based on the work of  Sakamoto et al. (2007)  and Berge-Nguyen and Cretaux (2015) (Fig. 1 ). With the Sakamoto et al. (  2007 ) method, we tested two cloud filters.
GIEMS, with its long time series, provides key information on the seasonal and interannual variability of the inundation, even at regional scale. As an example, a major flood occurred in the American Midwest, along the Mississippi and Missouri rivers and their tributaries, from April to October 1993, causing terrible devastation and flooding up to 80;000 km 2. Figure  3  presents the mean surface water extent in August over the Midwest in the USA between 1993 and 2007 (top), as compared to its value in August 1993 (bottom). The maximum inundated surface in the region calculated by GIEMS is very close to the official number, and its spatial pattern follows closely the expected hydrological structures. Regional assessment of this database using SAR data indicates that the approach realistically captures wetland complexes but can underestimate small wetlands comprising less than 10 % fractional coverage of a grid cell (\80 km 2 ).
The dataset has been extensively evaluated at the global scale  (Prigent et al. 2007; Papa et al. 2010 ) and for a wide range of environments, including boreal  (Papa et al. 2007 (Papa et al. , 2008) )  and tropical regions  (Papa et al. 2006; Frappart et al. 2008). The availability of this consistent and carefully evaluated dataset at a global scale and over a long period of time makes it possible to perform climatological analysis. We observed a decline of the wetland extent of $ 6 % over the globe between 1993 and 2008. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater  (Prigent et al. 2012). GIEMS has also been used for biogeochemical and hydrological analyses, such as the evaluation of methane surface emissions models  (Bousquet et al.
Downscaling of GIEMS With a 0:25 Â 0:25 spatial resolution, the initial GIEMS dataset is clearly not adequate for all applications. Would it be possible to develop downscaling methodologies to derive high-resolution surface water extent from the existing GIEMS low-resolution dataset? Since GIEMS has global coverage, the ideal situation would be to develop a downscaling technique general enough to work in all environments. However, each hydrological basin has its own characteristics such as its topography, vegetation, or space-time variability. The downscaling algorithm needs to take into account these specificities and the availability of the a priori high-spatial-resolution information for a particular basin. The highspatial-resolution information can come from satellite observations such as visible /nearinfrared images or SAR measurements, or it can be extracted from static topography information.
Downscaling Based on High-Resolution Satellite Observations Two downscaling methodologies based on high-resolution satellite observations have been developed and evaluated, with very encouraging results. These two methods require inundation information at high spatial resolution, at least for some time steps, in coincidence with the coarse spatial resolution dataset. The first methodology is based on image processing techniques and can be applied with a limited number of high-spatial-resolution information  (Aires et al. 2013). It has been applied and evaluated over the Amazon basin, using the SAR-derived observations of  Hess et al. (2003)  to calibrate the method, at only two time steps, for low and high waters. The probability of a high-spatial-resolution pixel to be inundated depends on the status of its neighboring pixels, and this probability is derived from the SAR images.
The inundation then fluctuates between the low and high water stages for each month, based on the GIEMS monthly dynamics, using a dilatation factor driven by the inundation probability. Following this methodology, 15 years of monthly mean inundation extent for the Amazon basin have been produced at 500-m spatial resolution  (Aires et al. 2013). Figure  4  shows an example of the downscaling of GIEMS with the SAR observations, over the Amazon The results benefit from the spatial structure of the SAR data coupled to the temporal dynamics of GIEMS. With the advent of the recently available Sentinel 1 SAR data, it is expected to have soon a global coverage of the inundated surfaces derived from the highresolution observations, at different periods of the year at global scale. The downscaling method will be trained on this SAR-derived dataset and propagated backward in time, as for the Amazon case.
The second solution requires the availability of more information on the dynamics of the inundation at high resolution. It uses the empirical orthogonal function (EOF) representation of the space-time variability of the high-resolution inundation extent, and to be efficient, a reasonable representation of the inundation dynamics has to be provided over a year. It has been applied to the Inner Niger Delta, using MODIS-derived inundation extent (Berge-Nguyen and Cretaux 2015), to produce a long time series  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011)  of highresolution inundation extent. This technique makes possible not only the downscaling of low-spatial-resolution information but also the temporal and spatial interpolation of highresolution dataset  (Aires et al.
The technique is applied to GIEMS to produce a 15 arc-s ( $500 m) map of the mean annual minimum, mean annual maximum, and long-term maximum (GIEMS-D15). Comparison with independent regional maps shows reasonable agreement, with some variation depending on the environments. This database is available under request at http://www. estellus. fr/index. php?static13/giems-d15. Work is underway to produce a monthly mean dynamic dataset at 90-m resolution from GIEMS.
However, these estimates only cover open water bodies and exclude the wetlands, despite their interest for a large range of environmental applications. GIEMS provides the only global monthly surface water extent database, thoroughly evaluated and available over a long period of time (15 years). Its extension in time up to the present is underway. GIEMS comprises all surface waters, including open water, wetlands, or rice paddies. Its original spatial resolution is $25 km, but downscaling methodologies have been developed to provide the user community with down to 100-m resolution estimates. Some methods involve the use of high-spatial-resolution satellite data from visible/infrared observations or from SAR measurements, to train the downscaling algorithms. Other methods require topography information from digital elevation models.
A first version of GIEMS at 500-m spatial resolution is available, based on this methodology (GIEMS-D15). Work is underway to produce a 90-m spatial resolution data base, with a monthly mean temporal resolution. The downscaling of GIEMS provides a global consistent reference dataset on water surfaces, at high spatial resolution. It can complement the local independent current estimates, with a reference that is suitable at large scale, for applications such as risk management or pricing of insurance policies. The advent of the SWOT mission will be transformational for the hydrology field, with its capacity to delineate surface water extent (lakes, rivers, wetlands during seasons of inundation, and reservoirs) and to estimate their water storage with an unprecedented spatial resolution and accuracy.
To provide the scientific community with a very long time record of surface water extent with a high spatial resolution and accuracy, the SWOT data, when available, can be adopted to downscale GIEMS over a common period of observations. The downscaling methodology trained on the SWOT dataset can then be applied back in time to produce a long time series of water surfaces at global scale, with a high spatial resolution, and consistent with the SWOT database. This will provide the hydrology and climate communities with an exceptional database that will be continuously extended in time during the SWOT lifetime.
Acknowledgments We are grateful to  Bill Rossow  and  Elaine Matthews  for their involvement at the origin of the work on GIEMS. We like to thank  Etienne Fluet-Chouinard  and  Bernhard Lehner  for their contribution to the GIEMS downscaling. We are also grateful to  Sylvain Biancamaria  for providing details for the SWOT description and to  Pham Duc Binh  for the comparisons of the surface water extents over the  Mekong Delta. The downscaling of GIEMS has been partly financed by  CNES , in the framework of the SWOT mission preparation, with the support of  Selma Cherchali  and  Jean-Francois Cre  ´taux. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution,.
For each library, amplicons were sequenced on an ABI 3130 POP7 sequencer (Applied Biosystems) at the Biogenouest Sequencing-Genotyping Platform (Roscoff site) after being ligated to the TOPO-TA vector (Invitrogen) and cloned into Escherichia coli following the manufacturer's recommended protocol (Invitrogen Corporation, Carlsbad, CA). Sequences were manually curated, subjected to BLAST search against publicly available sequences, grouped into Operational Taxonomic Units (OTUs) according to a cut-off value of 94% using BOSQUE (available at http://bosque. udec. cl, Ramírez-Flandes and Ulloa, 2008). Sequence data were analyzed with the ARB software package  (Ludwig et al. , 2004)  and phylogenetic reconstruction was performed as described previously  (Lehours et al. , 2010). Phylogenetic tree display and annotation were performed with iTOL software  (Letunic and Bork, 2006).
Representative sequences (one for each out) are deposited in the GenBank database under accession numbers KM654564-KM654598. pufM Gene Profiling Profiling of puf M genes was performed by Temporal Temperature gel Gradient Electrophoresis (TTGE) after PCR amplification using a Cy5-labeled pufM_forward primer and a primer pufM_WAW with a 40-bp GC-clamp (CGC CCGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCCCG) added at the 5 ′ -end. The electrophoresis conditions were similar as described by  Lehours et al. (2010). Gel images were obtained at a 100 µm resolution using a Typhoon Trio variable mode imager (Amersham Biosciences, Piscataway, NJ). Typhoon scans were acquired using the 633 nm excitation laser and the 670 BP 30 emission filter as recommended by the manufacturer for the detection of Cy5-labeled molecules.