dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, the offset between LST and GWT is dominated by the latent heat flux cooling down the surface in the form of evapotranspiration and by snow cover insulating the subsurface during the winter period. An empirical best-fit approximation for estimating rural groundwater temperatures (eGWT rur) from satellite-derived data on a global scale is given by equation (  1 )  [2]. The second step is estimating groundwater temperatures in urban environments (eGWT urb ) by using equation  (2) , which takes into account building density and basement temperatures  [38]. The third step combines rural and urban estimations by replacing LST by eGWT rur in equation  (2). The resulting equation (  3 ) is intended to estimate groundwater temperatures in rural, urban, and mixed settings (eGWT c ). Building densities were calculated at a resolution of 1 km × 1 km from OpenStreetMap [61] building polygons.
9 °C within the city center (Figure  3(a) ). This dataset exhibits two central LST cold spots (12. 7 and 12. 5 °C), caused by the two large forests in the eastern and western parts of the city center. The annual percentage of snow days (SD) ranges from 0 to 7. 3% and as expected SDs are more frequent in the outer suburbs than in the city center. Again, the two forests manifest as anomalies in the spatial SD distribution (Figure  3(b) ). In contrast to LST and SD, the data on ET has a lower resolution of 0 25 °× 0 25 °(approx. 28 km × 28 km). The city center is almost fully covered by one cell of low ET with a value of 10 mg m -2 s -1. Towards the rural areas, ET increases up to 17 mg m -2 s -1 (Figure  3 (c)). Finally, building density (BD) is calculated from building polygons of OpenStreetMap at a resolution of 1 km × 1 km.
Results and Discussion 3. 1. Spatial and Vertical Distribution of GWT. For the first step, we characterize the spatial and vertical distribution of GWTs based on the measured data (Figure  4 ). For this, the available early measurements from 1977, which focused on the city center, and averages from the years 1990 to 2015 for the greater Paris region were spatially combined. In February 1977, the reported GWT beneath the city center ranged between 14. 1 °C and 18. 3 °C. Towards the suburbs, the measured temperatures decreased yielding a temperature range from 12. 2 °C to 16. 6 °C. The measurement depths range from 17 m to 100 m below ground level. For the shallower measurements, a slight seasonal bias can be 5 Geofluids expected. For the 87 observation wells measured in February 1977, no repeated or more recent groundwater temperature measurements are available.
Comparison of Measured and Estimated GWT. To assess the quality of predicted groundwater temperatures from satellite imagery, eGWT c , they are compared with well measurements in the field. It is important to note the time difference: data in the city center was only observed in 1977, about three decades earlier than the satellite data considered for calculating eGWT c. However, as GWTs are expected to have increased during that time  [30, 66, 67] , these measurements are included in the following comparison viewing them as minimum values. In order to inspect the role of temporal variability and trends, we separately compare measured GWTs from (i) 1977, (ii) 1990-2015, and (iii) 2005-2015 with satellite-derived estimates. The latter GWT subset is extracted as it covers roughly the same time period as the satellite-based product.
Since satellite data covers the time period from 2005/01 till 2014/12 and groundwater temperatures are recorded till 2015/11, the data contains a negligibly small amount of 85 unsynchronized single measurements (4. 4% of the single measurements between 2005 and 2015). Figure  6  shows the measured GWTs versus LST and eGWT c along with misfit and Spearman correlation indices. The city center wells measured in 1977 (Figure  6 (a)) yield misfits (RMSE, MAE) between eGWT c and measured GWTs around 0. 5 K higher than the error observed between LST and GWT. Additionally, ME is higher (Figures  6(a ) and 6(d)) when comparing measured groundwater data with eGWT c (1. 27 K) than with just LST (0. 09 K). This means that subsurface temperatures are overestimated by eGWT c when using recent values of LST and old well temperatures in equation (  3 ).
This is because LST is expected to have increased by around 1 K from the 1970s to the period between 2005 and 2015 due to climate change and atmospheric urban heating, which is not surprising. At the same time, GWTs in 1977 were as high as 18. 3 °C, which is warmer than the assumed basement temperature of 17. 5 °C in equations (  2 ) and (3). Therefore, other heat sources such as heat release from subsurface infrastructures may play a significant role but cannot be reproduced by this method. This is supported by the low sensitivity of the measured GWT to LST as revealed by Figure  6(a). 7 Geofluids being slightly lower compared to the entire dataset from 1990 to 2015. We can therefore conclude that the estimation approach is most applicable for overlapping time periods between subsurface and satellite measurements.
For the given groundwater data in the outskirts of Paris, eGWT c is only slightly favorable over the use of eGWT rur. Although the comparison between the results of the individual equations correlates well with measured groundwater, the used approach also has some limitations. Particularly, local thermal disturbances caused by underground anthropogenic heat sources (e. g. , geothermal devices, sewer networks, and underground constructions) cannot be detected from above-ground data or their thermal footprints are too small to be resolved by the resolution of 1 km × 1 km of eGWT c. Moreover, the resolution of the ET dataset is very coarse (about 28 km) and cannot detect small-scale or even city-scale features. Applying the combined method 8 Geofluids to Karlsruhe and Cologne in Germany, the ET dataset did not display the reduction of ET commonly associated with urban areas, which added to an overestimation of GWTs in those areas  [60].
Based on the examined data, the maximum GWT anomaly reaches 6. 9 K in Paris. In the present study, an approach is applied that estimates groundwater temperatures (eGWT c ) from satellite-derived data and building footprints combining existing methods for estimating rural (eGWT rur ) and urban (eGWT urb ) groundwater temperatures. Estimates are validated with temperatures measured in the wells of Paris. Predicted GWTs have a very good fit (RMSE < 1 K) and only slightly underestimate measured GWT with a mean error of -0. 23 K. In addition to that, a coherent spatial estimation of the SUHI of Paris is obtained. Even so, measured extreme GWTs that cannot be reproduced as subsurface heat sources other than in basements are not considered. In an ideal case, the estimation techniques displayed here would supplement existing temperature measurements in wells, which would allow the use of this method as a tool to spatially connect point information obtained from wells.
The samples were mineralized by the addition of one volume of suprapure 65% HNO 3 and incubation on a rotating wheel at room temperature for 18 h. Mineralized samples were diluted in 0. 5% (v/v) HNO 3 and analysed using an iCAP RQ quadrupole mass instrument (Thermo Fisher Scientific GmbH, Germany) equipped with an ASX-560 auto-sampler (Teledyne CETAC Technologies, Omaha, USA). The instrument was used with a MicroMist U-Series glass concentric nebulizer, a quartz spray chamber cooled at 3 °C, a Qnova quartz torch, a nickel sample cone, and a nickel skimmer cone equipped with a high-sensitivity insert. 24 Mg,  25  Mg, 107 Ag and 109 Ag concentrations were determined using standard curves and corrected using an internal standard solution of 103Rh added online. Data integration was done using the Qtegra software (version 2.
5 W was applied per gel until the dye front reached the bottom of the gel. Detection was carried out by tetrathionate silver staining  59. 2. 10. 4 Image analysis and global analysis of the spot abundance data. The gels were scanned after silver staining on a flatbed scanner (Epson perfection V750), using 16 bit grayscale image acquisition. The gel images were then analyzed using the Delta 2D software (v 3. 6). The spots that were never expressed above 100 ppm of the total spots were first filtered out. Then, significantly-varying spots were selected on the basis of their Student's T-test p-value between the treated and the control groups. Spots showing a p-value lower than 0. 05 Environ. Sci. : Nano, 2019, 6, 3133-3157 | 3137 This journal is © The Royal Society of Chemistry 2019 were selected.
This strategy is used to avoid the use of arbitrary thresholds, which can result in discarding statisticallyvalid relevant changes and including non-valid changes. 60 he false positive concern was taken into account using several approaches such as the Benjamini Hochberg approach,  61  the sequential goodness of fit approach  62  and the sequential Fisher test approach. 63 Furthermore, we checked that all the spots that we found through the T-test also had a p < 0. 05 in a non-parametric Mann-Whitney U-test. For the global analysis of the spot abundance data, we used directly the spot abundance data as provided by the gel analysis software. The software directly normalizes each spot abundance by the sum of all spot abundances detected on the gel. These relative abundance data were used directly for global analysis using the PAST software suite  64  without any transformation.
The ion (M + 2H) 2+ at m/z 785. 8426 was used to calibrate MS data and the fragment ion (M + H) + at m/z 684. 3469 was used to calibrate MS/MS data during the analysis. For tandem MS experiments, the system was operated with automatic switching between the MS (0. 5 s per scan in the m/z range [150;1700]) and MS/MS mode (0. 5 s per scan in the m/z range [50;2000]). The two most abundant peptides (intensity threshold 20 counts per s), preferably doubly and triply charged ions, were selected in each MS spectrum for further isolation and CID fragmentation using a collision energy profile. Fragmentation was performed using argon as the collision gas. Mass data collected during analysis were processed and converted into. pkl files using ProteinLynx Global Server 2. 3 (Waters Corporation, Milford, USA).
Switching criteria were set to ions with a charge state of 2-4 and an abundance threshold of more than 500 counts; the exclusion time was set at 4 s. The IDA rolling collision energy script was used for automatically adapting the CE. Mass calibration of the analyser was achieved using peptides from digested BSA. The complete system was fully controlled by AnalystTF 1. 7 (Sciex). Raw data collected were processed and converted with MSDataConverter in the. mgf peak list format. For protein identification, the MS/MS data were interpreted using a local Mascot server with the MASCOT 2. 4. 1 algorithm (Matrix Science, London, UK) against UniProtKB/SwissProt (version 2016_01, 550 299 sequences). The research was carried out in all species. Spectra were searched with a mass tolerance of 15 ppm for MS and 0.
Global analysis of the proteomic results From the total protein expression data obtained from the gel analysis software, a subset of variable proteins (p < 0. 25 in either the acute vs. control or the recovery vs. control comparisons) was extracted. This allowed the noise brought by proteins that do not show a consistent variation in the biological phenomena under investigation to be decreased. This subset was then tested using the PAST statistical suite. 64 As a first test, a hierarchical clustering was performed, and the results are shown in Fig. 3A. Clustering clearly separates the control group from the two silver-treated ones, but gives no information regarding which silver treatment condition is closer to the control. Principal component analysis gave a more valuable indication, as shown in Fig.
05) between the control group and at least one of the silver-treated groups. In order  to cope with the multiple testing issue, we used several approaches such as the Benjamini Hochberg approach,  61  the sequential goodness of fit approach  62  and the sequential Fisher test approach. 63 All the selected spots showed a very low q-value for the condition for which they were variable compared to the control (Table  S1  †). We then divided this dataset of 239 spots into lists (Table  S2  †). The first list, labelled as "returning spots", contained the spots for which the amplitude of the change in abundance between the recovery and control stages is lower than the amplitude of the change in abundance between the acute exposure and control stages. 135 spots belonged to this list. It was then divided into subsets.
Funding This work was funded by  ANSES (Agence Nationale de Sécurité Alimentaire, Environementale et du Travail)  ( PNREST 2011/25 ,  Innimmunotox  project) and by the  CEA toxicology program  ( Nanostress grant ). It is a contribution to the  Labex Serenade  (no. ANR-11-LABX-0064 ) funded by the " Investissements d'Avenir" French Government program  of the  French National Research Agency (ANR)  through the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ). STEM-EDS analyses were conducted with the  TEM OSIRIS, Nano-Safety Platform, CEA-Grenoble , operated by  P. H. Jouneau. This project was managed by  Agence National de la Recherche (ANR) ,  program 'Investissements d'Avenir' , reference  ANR-10-EQPX-39.
The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track. The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.
Setup To run a machine learning challenge a few components are required: a well-defined problem, a dataset that contains the relevant features, a scoring metric to rank different solutions, and a platform to run the challenge. The problem design has to be reduced as much as possible to be as accessible as possible to a lay-person. At the same time it also has to retain the necessary complexity inherent in the tracking challenge at HL-LHC experiments. In this section, these components will be presented and the chosen configuration and the motivation for particular choices are discussed.
Dataset format Within the HEP community ROOT  [12]  is the default data format of choice for a large variety of applications. While established and widely used within HEP it is virtually unknown in the wider data science field. To reduce the entrance barrier to the challenge and allow a wide acceptance a different format had to be used. Text files based on comma-separated values (CSV) were chosen as it is a well-established data exchange format within in the larger data science community. Particle physics events contain a multitude of different types of information that are usually represented in a nested structures of variable length. Here, we aim to provide the data in a flattened structure to avoid the necessity for specialised tools or formats. Since events are statistically independent, data is stored separately for each event, organized by a numerical event identifier.
For each event in the training dataset the following four files are provided: Hits: The hits file provides the simulated hit information that are the core input for the challenge. Each entry corresponds to one hit generated by one particle in a detector module. Hits are identified by an event-unique numerical identifier. The position of the hit in the global coordinate system (x, y, z) as well as its origin in the detector hierarchy, i. e. volume, layer, and module identifier, is given. Cells: Each hit originates from one or more active detector cells, e. g. clusters of active pixels in the innermost sub-detectors. The cells file contains this information to enable participants to extract additional information, e. g. directional information from the cluster shape. Each entry contains the hit identifier to which the cell contributes too, information on the active channels, and possible charge measurement.
Depending on the subdetector the true particle state is given either before or after the interaction with the detector material. Training and test data-sets only differ by the type of files made available. In the test data set only the hits and cells files are available, as would be the case in the real detector reconstruction. Internally, all events were generated in exactly the same way. Solutions provided by the participants were also provided in CSV format. Due to technical requirements by the Kaggle platform a single file had to be provided for all events in the test dataset. Each entry therefore had to comprise three values: the event identifier as provided, the event-unique hit identifier as provided, and a track identifier generated by the participant. Hits reconstructed to belong to the same track must have the same track identifier.
The Kaggle platform The accuracy phase uses the Kaggle platform  [4, 6]. This platform hosts the dataset and provides the scoring and the leader-board for the participants. Participants can download the training and test dataset, train on the former and prepare a solution for the latter, and upload the solution. While a variety of solution metrics already existed, the scoring metric discussed in section 2. 4 was not one of them. It was implemented by Kaggle on their platform specifically for this challenge. For the test dataset, 125 simulated events were selected. Participants had to reconstruct all events on their own machines and upload their solution to Kaggle where the score was then computed.
Outlook With the accuracy phase closed, we saw a successful first phase of this challenge. Thanks to the great number of participants a set of new interesting solutions is now available. We are currently conducting a detailed analysis of the best-performing solutions to see if and how they can be applied to existing and future experiments. For the throughput phase both the accuracy and the run time need to be measured. For this the throughput phase is hosted on the Codalab platform  [5]. Here, participants publish not the output of their algorithm but the software itself. It is then run in a consistent environment to measure its runtime in addition to its accuracy. In the future, the challenge dataset is expected to be published as open data, e. g. on  [15]. We expect such a dataset to be useful as a common reference for algorithmic research, software validation, and to allow future work after the challenge has finished.
Acknowledgement The team would like to thank  CERN  for allowing the use of the dataset, and  Kaggle  for hosting it. We are very grateful to our generous sponsors without which the challenges would not have been possible. Platinum sponsors:  Kaggle ,  Nvidia , and  Université de Genève. Gold sponsors: Chalearn and DataIA. Silver sponsors:  CERN Openlab, Paris-Saclay CDS ,  INRIA ,  ERC mPP ,  ERC RECEPT ,  Common Ground ,  Université Paris-Sud ,  INQNET ,  Fermilab , and pyTorch. TG acknowledges the support of the  Swiss National Science Foundation  under the grant  200020_181984. This project has received funding from the  European Union   Horizon 2020 research and innovation programme under grant agreement No 724777 "RECEPT", No 772369 "mPP" and No 654168 "AIDA-2020 ".
Blast cells were gated as CD45 dim and SSC low. Two ratios of mean fluorescence intensities were calculated: E/A (rhodamine 123 efflux) and V/E (verapamil reversal of the efflux). Experiments were performed on a Navios flow cytometer and analyzed with Kaluza software (Beckman-Coulter). The cytometer settings were tested daily for optical alignment, fluidic stability, and optical detector sensitivity using standard fluorospheres (Flowset targets™ and Flowcheck™, Beckman Coulter, Villlepinte, France). Five patients were analyzed with an anti-CD34-AA700 (clone 581, Iotest, Beckman Coulter) and an anti-CD38-PB (clone LS-198-4-3, Iotest, Beckman Coulter, Villepinte, France) simultaneously with the measurement of ABCB1 activity to evaluate the CD34+CD38-population known to be enriched for leukemia stem cells.
Gene Expression Analysis Gene expression analysis was performed on Affymetrix GeneChip™ U133 Plus 2. 0 array according to the manufacturer's directions (Thermo Fisher), using total RNA extracted from cryopreserved AML samples and log2 gene expression values were obtained as described previously  [30]. We filtered out probe sets if they were present in less than 10% of samples (absence filtering) and if standard deviation was less than σ <1. 1 (non-variance filtering). All gene expression data will be made available via GEO at https://www. ncbi. nlm. nih. gov/geo/.
Methods species occurrence data. Occurrences of P. lotor were compiled from the online databases VertNet and GBIF, in addition to the databases of the Guadeloupe National Park, Martinique Regional Park, Saint-Martin Natural Reserve, French national wildlife organizations, recent literature, and personal observations. Only recent observations (from 1950 to the present day) with complete and precise locations were selected, giving a total of 20425 occurrences (Supplementary Table  S1 ). All occurrence records were aggregated into 0. 08° cells corresponding to the resolution of environmental variables, resulting in a total number of 5922 records (Supplementary Fig. S4 ). Unfavourable: areas that are currently unfavourable remain unfavourable in the future; Lost: areas currently favourable that will lose their favourable nature in the future; Maintained: areas that are currently favourable and will still be favourable in the future; New: areas that are currently not favourable but would become favourable in the future.
environmental data. Current and future favourable environmental envelopes for P. lotor were calculated using 19 bioclimatic variables (derived from temperature and precipitation measures; Table  2 ), averaged for the period 1950-2000 from the WorldClim 1. 4 database  119. A 5 arc-minute spatial resolution (approximately 9 kilometers at the equator) was selected for all bioclimatic variables. Future favourable envelopes for P. lotor were modeled using climate projections from global climate models (GCM) based on the Coupled Model Inter comparison Project Phase 5 (CMIP5) averaged for the period 2041-2060. Three GCMs were used (CNRM-CM5; GISS-E2-R; MIROC-ESM-CHEM; Table  2 ), for two Representative greenhouse gas concentration pathways scenarios (RCP): the most optimistic RCP2. 6 (with a radiative forcing of +2.
Niche overlap was calculated using a PCA approach and calibrated with the four selected environmental variables for each area of interest. PCA scores of the species occurrences on the first two axes were projected onto a grid of cells delineated by the minimum and maximum PCA scores of the environmental variables. Next, a kernel density function was applied to estimate a density of occurrence for each cell of the grid (see Di Cola et al. 120  for methodological details). Thereafter, niche overlap was estimated using Schoener's D  121. This index varies between 0 and 1; 0 meaning no overlap, while 1 meaning identical niches. This index was then used to assess niche equivalence and similarity. Occurrences in each compared range were pooled and randomly split into two datasets with equal size as the original dataset.