dataesr/scientific-paragraphs-categorization huggingface dataset:
E-Health testbed The experimental testbed is based on an infrastructure deployed within Dell premises in Ovens, Ireland, which replicates the capabilities of the presented e-Health scenario. Figure  8  depicts a schematic of the tesbted, highlighting the most important components. To have access to this eNBs, open-cells Subscriber Identity Modules (SIMs) are reprogrammed with uicc/sim programming software provided by opencells. These SIMs are the ones that emulate the UEs associated to e-Health service, such as wearables inside a roaming ambulance. Given this testbed, the following sub-sections details the specific experiments done to test both the ML model and the Actuation Framework.
QoP prediction model evaluation The QoP prediction model aims to train from data that are collected from the vertical service perception. Several real traces are captured having between 2 to 4 hours of length. The total number of instances is 11820. For each instance, seven QoP metrics are considered for determining the quality of the network signal perceived by the ambulance as described in Table  1. Among the real traces, 75% have been used for the training phase and the rest is kept for the testing phase. As detailed in Section 5, the first step aims to apply a sliding window that will transform the data into a 3D matrix. Each window will observe the KPIs for the last five minutes and it will label the data by observing the future five minutes. The window will advance each 5 seconds. The labeling aims to define the perceived signal strength label for each instance by according thresholds to RSRP, RSRQ and SNR metrics.
Following the depicted set-up, the PoC consisted on simulating a signal quality degradation to the RAN slice in which the UEs of the ambulance are connected. To this end, as a first step, a RAN slice has been deployed employing the operations exposed through FlexRAN. The slice has been assigned with a 10% of the RRBs of the total RAN spectrum. Figure  10  depicts in a Java Script Object Notation (JSON) format the details of both the down-and up-link of the slice as exposed through the monitoring API of FlexRAN and captured through wireshark. The highlighted fields are both the percentage of bandwidth occupied by the slice as well as the positioning of the RRBs. Then, the UE is connected to the deployed slice. Given the configuration of resources and the monitored QoP metrics, the Anomaly Detection ML model detects that a potential anomaly may happen due to the insufficient bit-rate that the UE is experiencing, as a result of the low number of RRBs that have been provisioned.
Once this is detected, the full actuation workflow is triggered, which results in a request of increasing the amount of assigned bandwidth of the deployed RAN slice. This is ultimately enforced by the QoS Control module, which contacts the FlexRAN SDN controller specifying the new configuration following the format of the exposed operation. Figure  11  depicts the JSON of the modification operation, stating that the slice needs to be provisioned with 25% of the available RAN spectrum instead of the initial 10%. Once the FlexRAN controller is contacted, it then takes care of applying the necessary re-configurations at the RAN segment so as the indicated slice is provisioned with the new specified bandwidth. Figure  12  depicts the characteristics of the slice after the re-configuration process.
Characterization of Root Extracts from Wheat Genotypes To examine the impact of genotypes on the wheat root metabolome, we profiled methanol root extracts by UHPLC-Q-TOF mass spectrometry. This untargeted analysis allowed the identification, after data filtration, of a total of 781 metabolite ions for all genotypes. An unsupervised principal component analysis of all metabolite ions revealed that wheat genotypes had major impacts on the root metabolome. The three wheat genotypes can be separated by the first two axes of the principal component analysis (Figure  1a ), showing that they are able to produce different secondary metabolites or identical metabolites in different proportions at a sampling time of 21 days. Component 1, explaining 21. 1% of the variation in the dataset, separated the Adular (A) and Soissons (S) genotypes.
Component 2, which explains 16. 3% of the variation in the dataset, separated the Bordeaux (B) samples from those of the other two genotypes. Univariate statistical analysis on ion abundances was used to identify metabolite ions that significantly differed between samples (Figure  1b ). Among the 781 metabolite ions detected in the wheat genotypes extracts, 479 were produced in the same proportions in all wheat genotypes, while 302 were differentially produced between genotypes (Figure  1b ). Most of these metabolite ions were produced by all genotypes, but in different proportions. The diversity of metabolites was approximately the same for all genotypes, and each genotype overproduced at least 50 specific metabolite ions. Nevertheless, the extracts from the Adular and Bordeaux samples shared the greatest number of metabolites, showing that the metabolomes of these two genotypes were close to each other.
The soil was watered at a water content of 20% (w/w). At 21 d after planting, the plants were dug up and soil that did not adhere to roots was discarded. Each plant root system and adhering soil were dipped into liquid nitrogen to avoid enzymatic reactions and freeze-dried by lyophilisation (Alpha 1-4 LSC Christ, Osterode, Germany). Preliminary experiments under the same conditions were carried out in order to determine the conditions of wheat growth and to assess the reproducibility of the data (data not shown). For the extraction of metabolites, three freeze-dried root systems of the same condition were pooled (leading to seven replicates per condition) in an Eppendorf tube, which was immediately soaked into liquid nitrogen. Roots were crushed using a ball mill (TissueLyser II; Qiagen, Courtaboeuf, France).
Extraction was performed using 1. 5 mL methanol for 150 mg of dry sample by shaking 10 min at 150 g and sonicating 10 min; samples were then subjected to centrifugation in order to recover the supernatant. The extraction was performed twice, and extracts were pooled and dried using a Speedvac-assisted evaporation (Labconco, Kansas City, MO, USA). Each sample was then re-suspended in methanol in order to obtain a concentration of 10 mg/mL. Preliminary experiments under the same conditions were made in order to develop analytical methods and to assess the reproducibility of the data (data not shown).
Data Processing and Statistical Analysis of Secondary Metabolites of Wheat and Pseudomonas The metabolomic analysis workflow was focused on small molecules (<1000 Da) for both wheat root extracts and Pseudomonas metabolomes. LC-MS raw files were converted to mzXML format using MS convert "ProteoWizard 2. 1" with filtering of m/z and eliminating those that were outside the range of 50-450 seconds for Pseudomonas extracts and 80-880 s for wheat root extracts  [91]. The data were then processed using both the R software and the collaborative Galaxy platform "Workflow4metabolomics" version 3. 3  [92]. Then the overall workflow of data processing was as follows: (1) peak extraction (xcms R package 3. 6. 1) [93] ; (2) peak alignment (xcms R package 3. 6. 1); (3) elimination of ions also found in blank samples; (4) elimination of ions having a coefficient of variation (CV) of signal intensity higher than 25% in the QC pool sample; (5) elimination of the isotopic ions or adducts that represent the same molecule (CAMERA R-package and manual control validation)  [94] ; and finally (6) a last step of normalization was performed according to the total chromatogram intensity given the relative intensity for each pre-processed ion.
Molecular Network Analysis and Identification of Metabolite The identification of wheat secondary metabolites was carried out using a molecular networking (MN) approach. MN was realized from MS/MS data after analyses of wheat root extract according to the protocol described by  [96]. MS/MS data were converted to. mzXML format using MS convert "ProteoWizard 2. 1" and were processed using MZmine 2 v2. 53. All parameters are presented in supplementary data (Table  S3 ). Molecular networking was realized thanks to the Metgem software with cosine score = 0. 6  [35]. The molecular-networking approach allowed us to cluster compounds belonging to the same chemical family. Thus, the identification of tryptophan, phenylalanine and the benzoxazinoids DIBOA and MBOA was confirmed by comparison of the retention time and accurate mass with those of pure chemical standards (Table  S4 ); other benzoxazinoid derivatives were characterized by comparing their UV spectra, accurate masses and MS/MS fragmentations to those of standards of DIBOA and MBOA.
5 MBq/50 µL of 18 F-FDG on days 16 and 30 post-ischemia. MicroPET images were acquired 1 h after injection. Seven myocardial infarction mice and 4 sham mice were IV injected with 5. 0 ± 2. 4 MBq/100 µL of 68 Ga-sCD146 on days 15 and 22 post-MI. MicroPET images were acquired 1 h after injection. Seven myocardial infarction mice and 4 sham mice were IV injected with 5. 0 ± 3. 2 MBq/100 µL of 68 Ga-RGD 2 on days 14 and 23 post-MI. MicroPET images were acquired 1 h after injection. Semi-quantitative region-of-interest (ROI) analysis of the PET signal was performed on attenuation-and decay-corrected PET images using InterviewFusion software (Mediso) and tissue uptake values were expressed as a mean percentage of the injected dose per gram of tissue (%ID/g) ±SD for 18 F-FDG and as a mean heart-to-muscle signal ratio (H/M ischemic ) ± SD for 68 Ga-sCD146 and 68 Ga-RGD 2.
Statistical Analysis Biodistribution data were analyzed using Prism software v9 (GraphPad, San Diego, CA, USA). Data were expressed as mean values ± SD. Earth to muscle ratio differences were analyzed using to Mann-Whitney test. Differences between sham and infarct hearts evaluated by immunohistochemistry and by ultrasound imaging were analyzed using parametric unpaired Student t-test. Gaussian distribution was assumed by a Shapiro-Wilk normality test. Analysis of correlation was realized with a Pearson two-tailed test. Differences were considered statistically significant when p < 0. 05.
Numerical procedures We use the Contact Dynamics (CD)  [12, 13]  to simulate assemblies of angular rigid grains in interaction. The CD method is based on implicit time integration of the equations of motion and a nonsmooth formulation of mutual exclusion and dry friction between particles. The contact forces and body velocities are found by means of an iterative nonlinear Gauss-Seidel algorithm. This method requires no elastic repulsive potential and no smoothing of the Coulomb friction law to determine interaction forces. For this reason, simulations can be performed with larger time steps compared to molecular dynamics simulations. We used LMGC90  [14] , a multipurpose software developed in Montpellier, capable of modeling a collection of deformable or rigid particles of various shapes by di↵erent algorithms.
Between December 2005 and 2013, the In-service Aircraft for a Global Observing System (IAGOS) program produced almost daily in situ measurements of CO and O 3 between Europe and southern Africa. IAGOS data combined with measurements from the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the Metop-A satellite (2008)(2009)(2010)(2011)(2012)(2013) are used to characterize meridional distributions and seasonality of CO and O 3 in the African upper troposphere (UT). The FLEXPART particle dispersion model and the SOFT-IO model which combines the FLEXPART model with CO emission inventories are used to explore the sources and origins of the observed transects of CO and O 3. We focus our analysis on two main seasons: December to March (DJFM) and June to October (JJASO). These seasons have been defined according to the position of Intertropical Convergence Zone (ITCZ), determined using in situ measurements from IAGOS.
, 1998; Petzold et al. , 2015 ; http://www. iagos. org, last access: 20 September 2020), airborne measurements of chemical compounds have been made over Africa. Using upper-tropospheric measurements of O 3 and relative humidity (RH) from 1994 to 2004,  Sauvage et al. (2007d)  showed an ozone minimum collocated with a RH maximum at the ITCZ. Both vertical transport in the ITCZ followed by photochemical production in the upper branches of the Hadley cells were found to contribute to meridional O 3 gradient creation. Few CO measurements were available at this time and none south of the ITCZ. Between  December 2005 December and 2013 December (2006 December -2013 hereafter) hereafter) , there were almost daily flights by Air Namibia equipped with IAGOS instruments between Europe and southern Africa, thus providing a unique and highly representative dataset of in situ O 3 and CO on both sides of the ITCZ.
The SOFT-IO model  (Sauvage et al. , 2017b)  has been developed to complement the IAGOS database to estimate anthropogenic and fire contributions to CO for each IAGOS measurement. These measurements, together with the SOFT-IO model outputs, allow us to trace the distributions, variability, and origins of O 3 and CO in the African UT. Since 2008, the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the Metop-A satellite has also documented UT CO and O 3 global distributions  (Barret et al. , 2016; Tocquer et al. , 2015)  and therefore complements the African transects provided by IAGOS. The objectives of this study are to (i) analyze the meridional distribution of O 3 and CO mixing ratios observed by IAGOS between 2006 and 2013, (ii) explore sources and geographical origins of observed CO anomalies (calculated with the SOFT-IO model), and (iii) give first track on which transport processes drive the CO and O 3 meridional transects observed especially using retro plumes (calculated with the Lagrangian particle dispersion model FLEXPART) of air masses measured by IAGOS.
Observational (IAGOS, IASI) and model-based (SOFT-IO) datasets and methods are introduced in Sect. 2. In Sect. 3, the method to locate the ITCZ and establish the seasonality with IAGOS observations is described and validated. In Sect. 4, O 3 and CO seasonal meridional transects observed by IAGOS are analyzed and compared to IASI observations. In Sect. 5, origins and sources of observed CO are explored with SOFT-IO, and, finally, characterization of O 3 transects is discussed in Sect. 6.
IAGOS-MOZAIC observations This study is based on the observations made in the framework of the Measurements of ozone, water vapor, carbon monoxide and nitrogen oxides by Airbus in-service aircraft (MOZAIC) program  (Marenco et al. , 1998) , now integrated into its successor program: IAGOS (https://www. iagos. org, last access: 28 April 2020)  (Petzold et al. , 2015). In MOZAIC, O 3 and RH has been measured since 1994 and CO since 2002 by airliners of several companies. In MOZAIC and IAGOS, a dual-beam UV absorption monitor is used to measure O 3 with a time resolution of 4 s and accuracy estimated at ±2 ppbv/±2 %  (Thouret et al. , 1998). An improved infrared filter correlation instrument measures CO with a time resolution of 30 s and accuracy estimated at ±5 ppbv/±5 %  (Nédélec et al. , 2003).
Since the setting up of the IAGOS program, a single analyzer integrates O 3 and CO measurements  (Nédélec et al. , 2015). In MOZAIC, capacitive relative humidity sensors were used to measure RH (MOZAIC Capacitive Hygrometer MCH)  (Helten et al. , 1998; Neis et al. , 2015a, b; Smit et al. , 2014). For the IAGOS program, MCHs were slightly modified and RH is measured by the IAGOS Capacitive Hygrometer (ICH)  (Neis et al. , 2015b). Both MCH and ICH instruments measure RH with a time resolution from 1 s at 300 K to 120 s at 200 K and accuracy estimated at ±5 %  (Helten et al. , 1998; Neis et al. , 2015b). During the 2011-2012 overlapping years of the MOZAIC and IAGOS programs, systematic intercomparisons were performed and have demonstrated a good consistency in the both dataset  (Nédélec et al.
, 2015). For convenience and as the IAGOS program database integrates MOZAIC program data, the MOZAIC and IAGOS programs are hereafter commonly referred to as the IAGOS program. Between December 2005 and November 2013, one Air Namibia aircraft equipped with IAGOS instruments (except in 2010 for technical reasons) operated daily flights between Europe (London or Frankfurt) and Windhoek in Namibia. It represents approximately 2200 flights in the African upper troposphere (between 9 and 12 km altitude) with observations of O 3 , CO and RH but also measurements of instant winds (zonal and meridional components, speeds and directions) from the aircraft navigation instruments. Given its large spatial and temporal coverage, this dataset is unique and offers outstanding opportunities for studying the O3 and CO budget in this region.
Model-calculated parameters for the IAGOS data points: potential vorticity and SOFT-IO CO contributions In order to explore origins and sources of CO mixing ratios observed by IAGOS and to characterize the meridional transects, we use the SOFT-IO v1. 0 model  (Sauvage et al. , 2017a , https://doi. org/10. 25326/2). SOFT-IO v1. 0 development and operation are described in detail in the reference paper by  Sauvage et al. (2017b) , and SOFT-IO data are freely available in the IAGOS database (https://doi. org/10. 25326/3). Briefly, SOFT-IO is a scientific tool which couples FLEXPART 20 d backward plumes of air masses for all the aircraft tracks with global anthropogenic and fire emission inventories for CO and has been used in various studies (e. g. ,  Cussac et al. , 2020; Petetin et al. , 2018)  to investigate and quantify anthropogenic and biomass burning origin in CO measurements.
For the entire IAGOS flight track, SOFT-IO v1. 0 estimates the CO contribution (in ppb) of recent (≤ 20 d before) worldwide emissions. Estimated contributions are discriminated on the one hand between anthropogenic and fire origins and on the other hand between the 14 different world regions of emissions as defined in Global Fire Emissions Database (GFED; see Fig. 1 ). In SOFT-IO v1. 0, the version of FLEXPART  (Seibert and Frank, 2004; Stohl et al. , 2005)  is the v9. 1 used in backward mode. The estimations of worldwide ground emission contributions to upper-tropospheric measurements depend strongly on the representation adopted for the atmospheric transport and especially for the vertical transport. In our version of FLEXPART, vertical convection is represented using the convective parameterization scheme by  Emanuel and Živković-Rothman (1999)  as described by  Forster et al.
Recently IAGOS database also provides additional ECMWF analysis parameters interpolated along each flight track (http://iagos-data. fr/#CMSConsultPlace: ANCILLARY_DATA, last access: 20 September 2020; http://www. iagos-data. fr/#L4Place, last access: 20 September 2020). In this study we use potential vorticity to determine stratospheric observations (see Sect. 2. 3). The potential vorticity (PV) values are calculated with the FLEX-PART Lagrangian model using meteorological fields from ECMWF (European Centre for Medium-Range Weather Forecasts) operational analysis, with a 1 • × 1 • resolution, and interpolated along the aircraft flight path.
IASI-IAGOS transect comparison In a similar way to the previous section where we used the NCEP reanalysis of wind and humidity to put the IAGOS flight tracks in a meteorological context, here we use IASI satellite observations of O 3 and CO to help put the IAGOS observations in a chemical context. We compare O 3 and CO transects obtained with IAGOS with the two-dimensional fields of O 3 and CO from IASI, which give a wider view over Africa and the adjacent oceans, allowing us to see the influence of regional circulation patterns on the distribution of CO and O 3. The IASI data were processed to take into account only a linear corridor of 5 • width (longitude) at a pres-sure level of 253 hPa, including all the trajectories of IAGOS flights (the central trajectory of this corridor follow the equation: long = 0.
Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus  and the airlines (Lufthansa,  Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific ,  Iberia  and  China Airlines , so far) who have carried the MOZAIC or IAGOS equipment and performed the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and the  Jülich Research Center (FZJ, Jülich, Germany ). IAGOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS database is supported by  AERIS , the  French portal  for data and service for the atmosphere (see http: //aeris-data. fr,last access: 20 September 2021). Financial support. This research has been supported by the  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie  grant agreement  H2020-MSCA-COFUND-2016-754433.
Data availability. The IAGOS data (Bundke et al. , 2018, https://doi. org/10. 25326/06) are available on the IAGOS data portal  (Boulanger et al. , 2019 , https://doi. org/10. 25326/20). The SOFT-IO v1. 0 products will be made available through the IAGOS central database and are part of the ancillary products (Sauvage et al. , 2017a, https://doi. org/10. 25326/2; Sauvage et al. , 2018, https://doi. org/10. 25326/3; Sauvage et al. , 2017b, https://doi. org/10. 5194/acp-17-15271-2017).
Variables and Data Sources The electronic medical records and MR images were sought to obtain the following data: • Demographics • Preoperative functional status according to the modified McCormick Scale (mMCS)  [25]  and the presence of any neurogenic bladder or bowel dysfunction • Tumor location (cervical, thoracic, and lumbar), its attachment (anterior, anterolateral, and posterior), and craniocaudal extension (number of spinal levels).
• Ratio (%) between tumor and spinal canal volume, identified as tumor canal ratio (TCR) = tumor volume spinal canal volume × 100 • Areas (mm 2 ) related to tumor and cord occupancy at maximum compression level and the degree of spinal cord compression, applying the formula reported by Davies et al. [19]  and illustrated in Table  1 , with the other previous studies reporting SMs volume analysis. The results are reported as % according to the author. This was calculated using OsiriX Lite ® software, based on axial T1-weighted images with contrast and axial T2-weighted images (Figure  2 ).
Statistical Analyses All statistical data were analyzed using TIBCO Statistica ® 13. 3. 0. Results are presented as median and range or as number of cases and percentage. Spearman Rank-Order Correlations were applied. The following correlations were studied: 1. tumor and cord dimensions with preoperative mMCS and duration of symptoms; 2. tumor and cord dimensions with postoperative mMCS and relative outcome; Statistical significance was set at p < 0. 05. This study was conducted in accordance with the ethical standards of the institutional research committees, ethic committees (Ethics Committee CCER Genève protocol code 2018-02254), and with the 1964 Declaration of Helsinki plus later amendments.
Image Analysis Pre-Processing MR image preprocessing included bias field correction using the N4ITK algorithm  (30)  from the Advanced Normalization Tools (ANTs) library  (31) , skull-stripping with the Brain Extraction Tool (BET) of the FSL software (FMRIB's Software Library)  (32)  and Z-score normalization with a scaling factor of 100. No spatial resampling was performed due to data homogeneity. As well, no noise filtering was applied.