dataesr/scientific-paragraphs-categorization huggingface dataset:
Parametrization of dissolved and particulate organic carbon and nitrate concentrations The model's parameters for the denitrification are taken from reference studies and in situ measurements. The sediment porosity φ was set to 25 %. It is computed based on the soil texture from the Food and Agriculture Organization (FAO) database at 11 km resolution. The porosity is averaged over the computation nodes (25 km × 25 km) using a bilinear interpolation. k POC , k DOC and k NO 3 were set to 1. 6×10 -7 d -1 , 8. 0×10 -3 d -1 and 30 µmol L -1 respectively. They are adapted from  Sun et al. (2017) , who performed a study of denitrification over the Garonne catchment (temperate anthropogenic watershed). To our knowledge, these parameters were never measured over the Amazon basin and the values we used are the only published estimates that we have.
According to the studies performed by Moreira-Turcq et al. (  2013 ), the POC concentration was considered constant over the whole watershed and for the entire period of the simulation (2011-2015) at 10 %. The daily discharge was extracted from the gauging stations used in the study (Fig. 3 ) from the HYBAM database. For each station, we calculated the mean monthly discharge from the daily observations. In terms of discharge, the marked seasonality of the Amazonian streams was demonstrated by prior studies  (Paiva et al. , 2013). For the DOC concentrations, we extracted the monthly measurements for the same stations over the same period. As the SWAF's period  (2011) (2012) (2013) (2014) (2015)  and the DOC measurements are not concomitant, we calculated a mean average monthly DOC concentration for each station.
When the information of DOC concentration was not available, our dataset was gapfilled using a linear relationship between DOC concentration and discharge  (Ludwig et al. , 1996) , based on the discharge marked seasonality of the Amazonian streams. Finally, we extended the calculated values to the associated main subbasin of the gauging station. NO - 3 concentrations were calculated for every type of soil given by the FAO's classification in the upper 30 cm layer (Fig. 3 ). Batjes and Dijkshoorn (1999) drew a complete description of the total nitrogen content of the soils of the Amazon region. Evaluating NO - 3 in the upper layer of the soils was executed adapting the mineralization rate which is based on the average temperature of the region and the proportion of both clay and limestone. For the most biologically active soils, such as Gleysols and Fluvisols, the mineralization rate was set at up to 7 % of the organic nitrogen amount, which is the maximum observed value in the region.
On the contrary, Regosols are biologically less active soils with mineralization rates hardly reaching 2 %  (Legros, 2007; Sumner, 1999). Finally, we determined the NO - 3 concentrations by combining the NO - 3 content in each type of soil with the water storage capacity for each type of soil, retrieved from the FAO soil database. NO - 3 concentrations were considered constant over the period. On the one hand, as the Amazon is one of the most active regions of the world  (Legros, 2007)  in terms of the microbial soil dynamic, during non-flooding periods, mineralization of nitrogen was sufficient to compensate for NO - 3 loss by plant assimilation and leaching. On the other hand, Sánchez-Perez et al. (1999)  showed that when denitrification is active during flood events, the NO - 3 pool of wetlands is provided and sustained by NO - 3 content coming from streams, in the case of the forested Rhine floodplain.
Because of the supersaturation of p CO 2 in groundwater  (Davidson et al. , 2010) , we consider that the gases produced during denitrification are entirely emitted to the atmosphere. Overall, denitrification was calculated as D NO 3 = R NO 3 • SWAF • Q wa , (2) where D NO 3 is the net denitrification in moles per month, R NO 3 is the denitrification rate in moles per month per litre, SWAF is the fraction of land covered with open waters and Q wa is the water storage capacity for each type of soil (L) retrieved from the FAO soil database. In summary, the model requires the inputs and parameters for (1) the NO - 3 concentration for each type of soil (mol L -1 ); (2) the DOC concentrations of the streams that overflow, extended to the associated sub-basin; and (3) the extent of inundated surfaces.
Finally, in our study we focused solely on denitrification. In order to provide a complete nitrogen budget for the whole Amazon basin, future studies https://doi. org/10. 5194/bg-17-4297-2020 Biogeosciences, 17, 4297-4311, 2020 will need to complexify the proposed methodology by integrating additional biogeochemical processes (DNRA, nitrification, etc. ) and physically relative datasets (soil temperature, soil moisture, etc. ) in order to extend the approach to non-flooded periods and other ecosystems.
From our model design perspective, we find that the denitrification for the Amazon wetlands is driven first by the extent of the flooded areas, which constrain the process, and second by the DOC content in the soil solution, which determines the maximum denitrification potential. Future studies will concentrate on extending the current approach to other tropical basins -it is needless to say that local observations will be essential for the validation of such exercises -preferably over the same period of analysis. Data from future missions like Surface Water and Ocean Topography (SWOT) will deliver water heights over 21 d of global coverage, which will improve the results of such studies through the integration of surfaces and volume information.
Acknowledgements. The SWAF product was developed in the framework of the TOSCA SOLE and SWOT downstream programmes from  CNES. We thank the  HYBAM Observatory network  for providing the data needed for this study. We thank the  FUI (Fonds Unique Interministériel) HydroSIM  project (2018)(2019)(2020)(2021). We thank the reviewers for their comments and their help in improving our manuscript. Financial support. This work was funded by l' Observatoire Midi-Pyrénées (Axe transversal cycle du Carbone, de l'Azote et gaz à effet de serre ). Review statement. This paper was edited by  Carolin Löscher  and reviewed by two anonymous referees.
The postprocessing analysis was performed on AZtec HKL Channel 5 software. The mechanical properties of the plates obtained after hot rolling were characterized by tensile tests and hole expansion tests. The tensile tests were performed following the DIN EN ISO 6892-1 standard. The gauge length of the tensile samples are 55 × 20× 3. 5 mm 3. The tensile properties were evaluated according to three different orientations with respect to the rolling direction: at 0 • , 45 • , and 90 •. As no clear anisotropy was observed in the sample, only the measurements made along the rolling directions are presented here. Two tensile samples were tested for each orientation, giving a total of 6 tensile samples per hot-rolling scheme. The values reported in the following are the average values (out of the six samples) for yield strength (YS), ultimate tensile strength (UTS), and total elongation.
Furthermore, the q cal values were remarkably close to the experimental values q e exp and matched well with the results of R 2 (Table  3 ). The normalized standard deviation Δq values (Table  3 ) determined from PFO and PSO data were in accordance with the results of R 2 ; they ranged from 0. 02 to 2. 24% for PFO data versus 1. 62-4. 73% for PSO data for AG biochars (raw and activated) and BCOP-NaOH. However, the Δq values were 3. 03 and 0. 26% based on PSO data of BCOP and BCOP-BM, respectively, compared to 4. 21 and 1. 99% based on PFO data, respectively. These results are consistent with previously reported studies in the literature. 43, 44 To analyze in depth the adsorption kinetics data, the intraparticle diffusion model was applied (Figure  S3c ) according to Zhu et al. and the fitted parameters are summarized in Table  3.
Median Age and Spread Rates First wave virus strains mainly hit the elderly. Hence, the positive correlation between slope and median population age in Figure  6A  (r = 0. 414, one tailed p = 0. 0011) fits the expected higher contagiousness in ageing populations. Note outliers as indicated in Figure  6A. For the second wave, the opposite association occurs, i. e. , slopes are the highest for countries with low median age (r = -0. 418, two tailed p = 0. 0023, Figure  6B ). This new information is crucial for future management of the pandemic. Second wave viruses apparently adapted to infect the larger reservoir of potential younger hosts, in addition to adapting to spread at higher temperatures. Data gathered until mid-June find second waves in additional countries. For countries with median ages above 36 years (Bulgaria, Japan, Moldova, Serbia, Sweden, and Ukraine), the trend for these second wave slopes fits that observed for the first wave slopes as a function of median age in Figure  6A.
Substituted products Valorisation, recycling and recovery options typically produce a valuable product; a secondary good (e. g. pectin, animal feed, electricity, heat), which can be further used as a food ingredient or as an energy source. This secondary good can replace/substitute another product on the market, this is called substituted product. The actual superseded product is based on plausible scenarios. The data collected refer to the average GHG emissions generated in the EU, or in a specific country within Europe for the comparison products, considering current knowledge, infrastructure, and market conditions in the year 2017. The substituted products represent a combination of market alternative products providing the same specific function (functional equivalence) as well as high and low impact alternatives (Alternative max, Alternative min).
In the case of anaerobic digestion, alternative scenarios were built for different combinations of output products (electricity, heat, digestate) as well as country electricity mixes (e. g. Norway as the greenest electricity mix in Europe and Estonia as the least green electricity mix in Europe). The functional equivalence is determined case by case either on the protein or energy content, or in the case of equal nutrient equivalent on mass basis. The selection is however limited to the commercial production of a comparison product, and to a sufficiently good standard of data quality. The identified substituted products for each food side-flow is shown in Table  1. The GHG data, data sources and equivalent amounts are described in the supplementary materials (Table  S11  -S22). For the land spread option, no comparison products were considered.
Impact assessment Global warming potential (GWP 100) is assessed as a proxy of environmental impacts from the management of food side-flows of manufacturing processes. The IPCC 2014 characterisation factors from the fifth assessment report are applied (e. g. CH 4 : 28 times CO 2 , N 2 O: 265 times CO 2 global warming potentials over a 100-year period). Emission data (Carbon dioxide equivalents CO 2 e) for energy and resources were primarily sourced from scientific literature, which are cited in the supplementary materials. Current electricity mixes were sourced from GaBi database. Transport was sourced with permission from the Network of Transport Emissions (NTME). Additionally, emission data of some specific comparison products such as hay or modified starch were sourced from Ecoinvent 3.
Integration of LCC The integration of LCC with LCA is recognized in De Menna et al. (  2018 ) as a possible strategy to identify and avoid trade-offs between environmental and economic impacts. However, the availability of published data is often limited which hinders the implementation of LCC. While some data, e. g. for energy and fuels, can be sourced from open access databases, others such as specific investment costs and labour requirements are usually protected, due to their commercial sensitivity, or when they are available, they refer to rather different technologies or countries from those used in the LCI. As far as the economic cost of animal blood treatment is regarded, a study on plasma and haemoglobin production reported an investment cost of EUR 900,000 for a plant treating 2,322 tonnes of animal blood per year, and a labour cost of more than 12,000 person hours  (Kowalski et al.
Acknowledgements The work is based on the  EU  project  REFRESH (Resource Efficient Food and dRink for the Entire Supply chain ) which was financially supported by the  Horizon 2020 Framework Programme  of the  European Union  under Grant Agreement no. 641933  which ran from July 2015 to June 2019. All partners within the REFRESH consortium providing their reviews in the course of the work are gratefully acknowledged. PM also acknowledges industry contacts for delivering information providing valuable input to this research.
To identify relevant papers, a bibliographic search was conducted accessing the following databases: Pub-Med, Cochrane Library, Scopus, Web of Science, from inception to the cutoff date of February 29th, 2020. To supplement the search, the reference list of every paper was also manually screened to identify additional potentially eligible studies. The following key-words were used, alone or combined with appropriate Boolean operators, to search the different databases: "intercostal muscle", "extra diaphragmatic muscle", "mechanical ventilation weaning", "respiratory muscle ultrasound", "intercostal muscle ultrasound", "parasternal intercostal muscle ultrasound", "critically ill patients", "critical illness", "intensive care". A similar search was also performed using the Pub-Med MeSH thesaurus.
Materials The potentiostat (STAT300) supplied by Methrom (Switzerland) was connected via USB to a computer installed with Dropview 8400 software (Methrom, Switzerland). Magnetic rack magnetic support (reference MAGNET16TUBE05) was purchased from DropSens (Spain). The thermoshaker (reference TS-100) was commercialised by BioSan (France). Non modified Screen Printed Carbon Electrodes (SPCE) (reference DRP-110) and SPCE modified with Carboxyl functionalised Multi-Walled Carbon Nanotubes (MWCNT-COOH) (110CNT) were purchased from Methrom, Switzerland. An SPCE consists of a ceramic substrate incorporating the electrochemical cell. DRP-110 electrodes consisted in a working (4 mm diameter) and counter electrodes made of carbon, reference electrode is in silver. 110CNT consisted in a working electrode (4 mm diameter) made of Multi-Walled Carbon Nanotubes (MWCNT), counter electrode is made of carbon, whereas reference electrode and electric contacts are made of silver.
Data Analysis The intensity of the current was measured. Each day of analysis, mean, standard deviation (SD) and coefficient of variation (CV%) were calculated for a set of identical samples (blank or spiked samples). Then a statistical approach which took into account the β error of 5% was chosen as it was recommended in the European guideline for the validation of screening methods for veterinary drug residues  [26]. The positivity threshold T (1) and the cut-off value Fm (2) were calculated. T = B -1. 64 × SDB (1) where: B is the mean and SDB the standard deviation of the signal (RLU) of the blank samples. Fm = M + 1. 64 × SD (2) where: M is the mean and SD the standard deviation of the signal (RLU) of the spiked samples. The assay was considered valid only if T was higher than Fm.
Dilution in PBS and Ethyl Acetate Extraction The extraction protocol develop by Yan et al. [27]  was tested with slight modifications to solve this issue. Blank honey samples from different botanical origins (i. e. , acacia, chestnut, forest, mountain, spring, lime tree) and the same samples spiked with CAP to 0. 3 ng/g were analysed during 2 days: One g of honey was mixed with 3 mL of PBS. A 4 mL of ethyl acetate was added and the samples were vortexed and centrifuged for 10 min at 4000 rpm. The supernatant was transferred to another tube. To enhance the extract efficiency, the extracting step for CAP was repeated three times. The collected supernatant was dried under nitrogen flow and then resuspended in 0. 5 mL of phosphate buffer the first day and 1 mL of PBS the second day. In both cases, the threshold value T was lower than the cut-off Fm (data not shown).
II. A Hybrid System Model for IPS The localization methods in an IPS are classified into two groups as noted in  [1] : (1) based on distance estimation; and (2) mapping-based localization. In the first group, the distance estimation process employs techniques based on the signal strength and/or the elapsed time between two signals. In our work, we opt for the second group where the mapping-based localization works with pre-stored signals (tags) values in a database. We apply the mapping localization approach in a Li-Fi based positioning system that uses a signal emitted from a LED (light source) to determine the position of the user's device (receiving device). The user's device, which is equipped with a receptacle (e. g. , photodiode-dongle), receives the signal from the LED i. e. , its identifier.
So, we use the ID as a positioning tag associated with a LED lamp installed in a known location, both data stored in a database. Nkengue M. J. P, Iguernaissi R. , Madjarov I. , Damoiseaux J. L. We also use a vision-based positioning system to estimate the position and the orientation of a person indoor by identifying an image that is within a view. In  [2]  authors note that the commonly used methods for image-based indoor positioning are focused on calculating the Euclidean distance between the feature points of an image. For smartphone-based indoor localization as a compliment, we opt for a Pedestrian Dead Reckoning (PDR) technique to give the position of a mobile user relative to a reference, as presented in  [3]. We use the step detection technique (accelerometer) and heading estimation (gyroscope) to reassure the guided person between two identified positions in case of contact losses from other technics.
III. Implementation and Evaluation In the proposed system model, the source of data dissemination is a Li-Fi lamp and a processed image from an OC, whereas the source data collection is a user device with a photoreceptor. The collected data are analyzed and processed, and the localization is performed via a Web service. When the user passes under a LED his smartphone can receive the tag associated with this LED lamp. Its path is followed by an optical camera to confirm the user's position. An alert message will be sent in case of remoteness from the prescribed path or in case of unexpected barriers. A reconfigured path will be then sent to the user. A graph-path algorithm determines all intermediary points to be followed to guide the user to the destination. These intermediary points represent the graph-vertices where the Li-Fi lamps are positioned.
From the patient's point of view, the inclusion of numerical assessment in the process of data collection has shown benefits in disease management and symptom control  [24] , improving survival  [20]  and patient-clinician communication  [19]. Moreover, it potentially facilitates clinical decision making through the systematic and continuous collection of symptom data  [25]. Since compliance is a multidimensional phenomenon, any attempt to improve it should encompass several potential contributors. Thus, we hypothesize that electronic measurement of HRQoL (before each consultation with scores communicated immediately to clinicians who can discuss it with patients), coupled with therapeutic information, will improve 12-month compliance rate with ET in patients with non-metastatic BC. At a later step, we will assess the role of social support and psychological distress as potential modulators of compliance with ET, the capacity of HRQoL to predict compliance with ET, patient satisfaction with care, and physician perception regarding the clinical utility of routine HRQoL evaluation.
Inclusion procedure The study will be proposed to eligible patients by their doctors (oncologists, surgeons, and radiation oncologists). Patients will be included at the time of the first prescription of ET (at the end of the treatments by surgery with or without chemotherapy with or without radiotherapy). Once included, patients will be randomly assigned to one of the two study arms (interventional arm or control arm). Patients then will be referred to the clinical research associates, whose role will be to collect patient data (clinical and sociodemographic) in both study arms and to instruct patients in the interventional arm on how to use the CHES (Computer-based Health Evaluation System) software  [26]. For patients who wish to access the platform from home, a username and password will be provided.
Intervention arm 2. 3. 1. Assessment of HRQoL The intervention will consist of an electronic measurement of patient HRQoL before each consultation with delivery scores to clinicians, who can discuss it with patients and couple it with therapeutic information. Before their consultation, patients will complete the EORTC-QLQ-C30 and the EORTC-QLQ-BR23 questionnaires via a touch pad or from their home via a secure web portal. Patients will complete the questionnaires via the CHES software  [26]. The CHES software was developed by ESD (Innsbruck, Austria) in collaboration with the EORTC Quality of Life Group to facilitate the inclusion of HRQoL measurement instruments in research projects and daily clinical practice. This mode of collection provides clinicians with real-time results and generates alerts in the event of clinically significant deterioration of patients' HRQoL scores, allowing them to tailor treatment.
Secondary endpoints The secondary endpoints will be anxiety and depression assessed by the Hospital Anxiety and Depression Scale (HADS) questionnaire, social support assessed by Sarason's Social Support Questionnaire (SSQ6) questionnaire, and HRQoL assessed by the FACT-G questionnaire. HRQoL data using the FACT-G questionnaire will be assessed at inclusion in both arms to ensure comparability between groups and at 12 months to assess the predictive value of HRQoL on compliance with ET. Patient satisfaction with care will be assessed by the EORTC-SAT-C33 and EORTC-OUT-PATSAT-7 questionnaires. Physician perception regarding the utility of systematic HRQoL evaluation will be assessed using an ad-hoc questionnaire derived from the work of Velikova  [19, 28] , including the perceived utility and satisfaction of routine assessment HRQoL, reasons for use/non-use, and the intention to adopt this assessment in routine care.
Data collected Sociodemographic characteristics (age, sex, level of education, family situation, socio-professional category, and work time), medical and surgical history, date of tumor diagnosis, tumor characteristics, previous treatments, patient clinical characteristics at inclusion and at each follow-up visit (weight, height, and overall patient condition), concomitant treatment, type of ET received, HRQoL data (FACT-G), anxiety and depression (HADS), social support (SSQ6), treatment modification (change in the type of ET), treatment-related toxicities, and their grade will be collected. Patient satisfaction with provided care will be measured using the EORTC-SAT-C33 and the EORTC-OUT-PATSAT-7. Clinician perceptions of systematic HRQoL assessment (utility and perceived satisfaction, reasons for use or non-use, and the intention to adopt it in practice) will be collected via an ad-hoc questionnaire.