dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparing outlet discharge data to simulation results The model was evaluated based on the discharge calculated at the Mercier outlet compared to discharge data observed at that location. Two types of comparison were performed: (1) long-term continuousbased comparison; and (2) short-term event-based comparison. For (1), the Nash-Sutcliffe efficiency (NSE) and the bias (PBIAS) were calculated according to Eqs. (A1) and (A2) in Appendix. Criteria were calculated for each year from 2007 to 2010 as well as over the entire simulation period (January 2007 to December 2010) at hourly and daily timesteps. NSE was calculated both for discharge and square root of discharge, which contrary to NSE on discharge gives less weight to high values. For the event-based analysis (2), simulations were evaluated using differences in peak value, and peak time (Eqs.
Evaluation of fast flow components using water level data In the PUMMA model, fast flow corresponds to surface runoff. On forest or agricultural areas, it is generated by the HEDGE module. In HEDGE, surface runoff is produced when the whole soil profile is filled up. This surface runoff is routed between modelling units by the OLAF module. The corresponding parameters are soil depth and porosity, and topology of the OLAF module. Whereas for residential areas and roads, URBS module is prefered. The main drivers for controlling surface runoff are the interception reservoir, the surface hydraulic conductivity and link coefficients (corresponding to the fraction of surface runoffespecially on impervious road and built surfacesdirectly connected to the drainage network). As for the surface, runoff collected from road and built surfaces is directly connected to the stream network.
Conclusions on the model diagnostic Although not calibrated, PUMMA seems to present a few strengths, among which: specification of precipitation (at least for seasonal analysis purpose), specification and calculation of evapotranspiration, and water transfer specification (lateral hydraulic conductivity and river routing). Observed model weaknesses appear to come mainly from soil water storage underestimation, all over the catchment. Specification of water pathways in the upstream catchment should also be improved. The last direction towards model improvement could be the inclusion of infiltration excess (Horton) runoff. However, this is probably a second order factor as compared to soil water storage and water pathways specifications. Being the dominant module of PUMMA for the Mercier catchment, as it represents both agricultural and forest land use, these conclusions apply specifically to the HEDGE module, in particular the relevance of the evapotranspiration representation and parameterization, and the underestimation of soil water storage.
Acknowledgements The first author thanks  Polytechnique Montréal  for providing the funding allowing his 6-month stay at  Irstea Lyon. Most of the data used in this study were collected during the  AVuPUR (Assessing the Vulnerability of PeriUrban Rivers)  project funded by the  French National Research Agency (ANR)  under contract  ANR-07-VULN-01 , the  IRIP  project funded by  Région Rhône-Alpes and Agence de l'Eau , and in the framework of  OTHU (Observatoire de Terrain en Hydrologie Urbaine). We thank  Judicaël Dehotin  and the  IRIP  project for providing soil moisture data.
For each sampling point, sexual steroids and Vitellogenin levels were measured in the plasma and a histological follow up of the oogenesis progression was performed. During spawning, each spawn was split into two parts. The first one was not fertilized and frozen for further molecular analyses while the other part was fertilized to assess the developmental progression. All details are given in the method paragraph. https://doi. org/10. 1371/journal. pone. 0226878. g001 ten minutes; Phloxine (VWR) was diluted in water at 0. 5% and used for five minutes; Light Green (Sigma) was diluted in water at 0. 5% and used from three to five minutes. Stained sections were examined, photographed and analyzed using a light upright optical microscope (Nikon Eclipse Ni-U) associated with a DS-Fi1 digital camera and the software NIS BR (Nikon France, Champigny-sur-Marne, France) at low magnification (x2 and x4).
Microarray analysis The Eurasian perch array (SurePrint G3 Custom Gene Expression Microarray, 8x60K -Agilent Technologies) contains 48,986 non-redundant probes previously identified and available from the PhyloFish Database  [43]. The "One-Color Microarray-Based Gene Expression Analysis (Low Input Quick Amp Labeling) Protocol" was followed for samples preparation, hybridization, washing and scanning of slides and data extractions. Briefly, 150 ng of total RNA were used for the amplification/Cy3-labeling step. After this step, samples were purified (RNeasy mini kit, Qiagen) and quantified using a NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies). Samples exhibiting a yield higher than 1. 03 μg of cRNA and a specific activity higher than 10. 30 pmol of Cy3/μg of cRNA were fragmented and used to hybridize arrays (one sample failed the labeling step and was excluded from the experiment).
Samples (600 ng of Cy3-cRNA) were randomly distributed onto four slides. After 17h of hybridization at 65˚C, slides were washed, dried and scanned with an Agilent Technologies Scanner (G2505C). Scanned images were extracted with Agilent Feature Extraction software. Data extracted from scanned images were normalized and log(2) transformed for statistical analyses (all data are available in the Gene Expression Omnibus database under the accession code GSE119802). Using the GeneSpring software, an unpaired t-test followed by a Benjamini-Hochberg correction was used to identify genes differentially expressed (DEG) between the two populations (p<0. 05). Then, a hierarchical clustering analysis (unsupervised average linkage) was performed using Cluster 3. 0 software (version1. 52). Clusters were visualized using TreeView software (version 1.
Gene ontology analyses In order to give an overview of gene ontology (GO) terms represented among differentially expressed genes between F1 and F7+ populations, they were functionally annotated and classified using Blast2GOv4. 1. 9 software  [44]. Default parameters were used for blastx and GO annotations. In the following, we performed a GO overrepresentation analysis in which we compared the list of DEG to a reference list corresponding to all expressed genes in the microarray. To retrieve this reference list, we first filtered all genes present in the array. Genes were, considered as expressed when they presented a signal above background in at least 75% of the samples and in at least one of the populations. In a second time, as the Eurasian perch genome is not yet available in any databases allowing performing GO analyses, we chose to retrieve human orthologs identifiers for the reference and DEG lists.
It allowed us to perform the analysis. In order to find these identifiers, we aligned probes designed for the array corresponding to each sequence in the lists against the Eurasian perch transcriptome extracted from the genome, provided by  [45] , with minimap2 (version 2. 7 with -m 20 parameters). Because the gene prediction file had small UTRs, we extended the prediction on both transcript sides by 2kb. If a probe had a unique alignment, it was assigned to the corresponding transcript. If the probe had two corresponding transcripts located one after the other on the genome, we assigned it to the transcript having the match closest to its center. Probes with no match or over two matches were not assigned to a transcript. We then retrieved the human orthologs identifiers from the annotation file provided by  [45].
Real-time PCR analysis Genes presenting a log(2) fold change (log(2)FC) > 4 in microarray were additionally studied by real-time qPCR in all samples previously used for the microarray. After RNA extraction, a DNase treatment (DNase I, RNase free-Thermo Scientific) was applied to 5 μg of all samples (n = 32) following the manufacturer's protocol. The reverse transcription was performed in a final volume of 20 μl using a M-MLV Reverse Transcriptase (Sigma-Aldrich), 1 μg of RNA and random nonamers (2. 5 μM-Sigma-Aldrich) and following the manufacturer's protocol. Reverse transcript products were diluted 1:27 and 5 μl were used for the real-time PCR, using PerfeCTa SYBR Green SuperMix (Quanta Bioscience) and 5 pmol of each primer. Primers were designed using Primer3Plus or Primer design Tool-NCBI software (S1 Table ).
The realtime qPCR was performed using a Step One Plus system (Applied Biosystems, Foster City, USA). The PCR program consists of a first step at 95˚C for two minutes followed by 40 cycles consisting of a denaturation step at 94˚C for 15s, an annealing step at 50-58˚C depending on the primers pairs for 15s and an elongation step at 72˚C for 30s. The amplification was followed by a melting curve stage, according to manufacturer's parameters in order to check the primers specificity. The abundance of the target cDNA in each sample was calculated using a serial dilution of a pool of all cDNA samples using the StepOne Software (Applied Biosystems, version 2. 1). This dilution curve was used to certify the reaction efficiency (80-120%). All samples were analyzed in duplicate and the geometric mean of the expression level data of the adenosine kinase-like, RNA18s, TATA-box binding and ELAV-like protein 1-like (S1 Table ) was used as reference to normalize the data obtained.
Using Panther software, no Pathways was enriched and the term "immune system" was overrepresented (FDR p-value = 0. 00251; S8 Table ). Four of the genes represented belong to the butyrophilin or the butyrophilin-like family but two of them were up-regulated in the F1 population while the others were down-regulated in this population. For three of these genes, the log(2)FC was >2 and were thus among the 16% most differentially expressed genes between the two populations. Indeed, among the 358 DEG only 60 presented a log(2)FC > 2 between the two populations. Fifty DEG showed a log(2)FC between 2 and 3-fold (S4 Table ) and 10 genes had a log(2) FC higher than 4-fold (5 over-expressed in the F7+ population and 5 in the F1; Fig  5B ). We choose to check the expression level of the genes having a log(2)FC > 4 by real-time qPCR.
Among them, three sequences could not be identified because the probes actually recognized contigs grouping numerous unidentified genes in the Eurasian perch transcriptomic database (Fig  5B ). Among the remaining genes, four were more expressed in the F7+ population: mex3b (log(  2  Expression level differences were confirmed for mex3b (log(2)FC = 7. 26 for the RT-qPCR), the uncharacterized protein (log(2)FC = 1. 65 for the RT-qPCR), hace1 (log(2)FC = 1. 65 for the RT-qPCR) and per2 (log(2)FC = 3. 13 for the RT-qPCR; Fig  6 ). However, for bloc1s1 and nibl1 the expression levels between both populations were not significantly different by RT-qPCR even if they followed the same profile than in the microarray (Fig  6 ). In addition, concerning iyd not only no significantly different expression was observed, but also the profile observed by RT-qPCR was in favor to a higher expression in the F7+ population, which is in contradiction with the microarray data.
In any case this difference of egg quality may be explained by the resource allocation theory  [12]. Even if domestication of Eurasian perch begun 20 years ago, numerous questions remain to properly achieve their breeding in recirculating aquaculture systems. Since no selective program has begun yet for this species  [23] , the artificial selection driven by farmers remains empirical. In addition, rearing conditions are not fully optimized and fish farmers continue to make changes in their practices. Consequently, an imbalance between biological functions may occur in this species because resource intakes are allocated toward certain traits at the expense of other functions, such as reproduction. No zootechnical practices may be undertaken to compensate the lack of intake that may occur at each generation.
In another study performed in the steelhead trout (O. mykiss)  [3] , authors proposed that it could simply reflect an adaptation of individuals to the captive environment, implying an up-regulation of genes involved in this function in domesticated fish. However, no information on the direction of perturbations (up-or down-regulation of genes in domesticated or wild populations) was given. In our study, the term "immune system" involved genes that were up-regulated either in the F1 population (five genes) or in the F7+ population (four genes). Additionally, most of the highlighted genes code for Butyrophilin proteins involved in several biological functions, on top of their role in the immune system. More particularly, they regulate the oil droplets secretion in the milk produced by mammals  [67].
In xenopus, mex3b mRNA presents a long conserved untranslated 3'UTR involved in its auto regulation  [83]. In turn, the protein is involved in the mRNA stability and regulates the abundance of several genes, involved in diverse cellular functions  [83]. In the present work, the maternal expression of mex3b mRNA presents the highest variation of expression level between the two populations (log2FC > 7 in the RT-qPCR). Consequently, the high expression of this transcript in the F7+ eggs may be linked to fine tuning of numerous molecular functions in the embryo and lead to diverse phenotypes. Finally, a mRNA coding for an uncharacterized protein was found to be significantly more expressed in the eggs laid by F7+ females. BLAST analyses against the Uniprot and NCBI databases showed that they are highly homologous to other uncharacterized proteins in other fish species presenting homologies with some domains of ADP ribosylation enzymes.
Acknowledgments We thank,  Paul Georges  and  Franck Bertrand  for their involvement in some experiments as trainees,  Alain Iuretig  for the care given to the breeders during the growing period and  Yves Le Roux  for the assistance with the survival statistical analysis. We are also grateful to  Mikhail Ozerov  and  Freed Ahmad  from  University of Turku  for kindly providing the annotated Eurasian perch genome used in GO analysis and the members of the  UR AFPA DAC and LPGP Rennes  for their support.
Material and methods The study was conducted in three main phases: First, the agricultural areas most seriously affected by environmental pressures ("Pressure Areas") were identified using various spatially explicit datasets on e. g. soil erosion, water pollution, and pollination pressures. In a second step, local agroforestry experts were consulted to propose suitable agroforestry practices for their regions suffering from environmental pressures. Finally, the annual carbon storage impact of the proposed systems was identified and evaluated in the light of European agricultural GHG emissions. In the next subsections, these three main phases are described more in detail, while we address advantages and limitations of the adopted approach, as well as possible improvements, in the Discussion section.
Selection of indicators to assess environmental pressures The indicator selection passed three stages as visualised in Fig. 2. First, indicators characterizing benefits provided by agroforestry systems were chosen. Torralba et al. (2016)  summarized them into i) timber, food, and biomass production, ii) soil fertility and nutrient cycling, iii) erosion control, iv) biodiversity provision and  Hart et al. (2017)  completed the list with v) climate change mitigation. Given that continental spatial datasets covering most of the European countries and addressing the indicators in a consistent way were limited, the selection focussed in a second step on the CAP 2014-2020 context indicators. The CAP monitoring and evaluation framework is composed on a set of socio-economic, sectorial, and environmental indicators to reflect the impact and provide (annual) information of the performance of the strategy  (European Commission, 2017b , 2014).
Within this list four context environmental indicators were related to agroforestry benefits. These were i) water abstraction in agriculture (C. 39) addressing pressures on available fresh water resources (indicator: e. g. irrigated area), ii) water quality (C. 40) dealing with agricultural water pollution by nitrates and phosphates (indicator: e. g. Gross Nitrogen Balance), iii) soil organic matter (SOC) in arable land (C. 41) as SOC influences soil structure, aggregate stability, nutrient availability, water retention and resilience (indicator: SOC content), and iv) soil erosion by water (C. 42) the most widespread form of soil degradation (indicator: erosion) (European Commission, 2017b). Third, as indicators for climate change mitigation and biodiversity were not addressed within the CAP monitoring, we reviewed the literature and identified relevant datasets.
In conclusion, only consistent spatial datasets, which were available with a wide European coverage, were included in the analysis. Accordingly, environmental pressures related to: i) soil health (soil erosion by wind and water, soil organic carbon), ii) water quality and abstraction (water pollution by nitrates, irrigation), iii) climate change (rising temperature), and iv) biodiversity (pollination and pest control pressures, reduced soil biodiversity) were identified. Individual pressure maps were spatially aggregated and combined into the "Pressure Areas" map showing all regions where one or several environmental pressures occur. To identify the "Priority Areas" for intervention, the sum of pressures per spatial unit (pixel size = 100 m x 100 m) was expressed as an accumulation map or a "heatmap of environmental pressures".
Assessment of carbon sequestration in biomass The total biomass production (aboveground wood and root biomass) of the woody elements and the carbon storage potential of the proposed agroforestry systems were assessed based on literature data (see Supplementary material) and from (regional) test sites [units: t biomass ha -1 a -1 ; t C ha -1 a -1 ]. Herein the values represented an average potential per year of tree life and did not consider any dynamics of tree growth over time, or other impact factors such as water and nutrient availability, temperature, tree density, etc. Potential minimum and maximum values of carbon storage in biomass (both above-and belowground) of each agroforestry practice for each biogeographic region were extracted separately for pasture and arable land. These values were used for upscaling the results to the "Priority Area", assuming that in those regions, the total available farmland would be converted into agroforestry with one of the recommended agroforestry practices.
Carbon storage potential For each system the annual carbon storage potential of the woody elements (including roots) was identified using data from the literature and in each geographical region, the minimum and maximum storage potential were determined. The wide range of practices selected corresponded to a wide range of carbon storage potentials, between 0. 09 and 7. 29 t C ha -1 a -1. In Table  4  these data were upscaled to the entire Priority Area of each biogeographical region. Overall, implementing the proposed agroforestry practices in the Priority Areas could mitigate between 2. 1 and 63. 9 million t C a -1 depending on the systems chosen, which is between 7. 7 and 234. 8 million t CO 2eq a -1. In 2015, the 28 members of the European Union (EU28) together with Switzerland emitted 4,504.
European environmental Pressure Areas In response to the first question, several environmental pressures that can be mitigated by establishing agroforestry practices were selected. According to  Alam et al. (2014)  and  Torralba et al. (2016)  these include soil conservation, the improvement of water quality, nutrient retention, climate regulation, and enhanced biodiversity. We investigated nine environmental pressures and mapped their occurrence in European agricultural land, based on existing spatially explicit databases at a continental European scale. The best available data were used, although it should be noted that differences in scales (100-1000 m pixel size), time periods  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  and models (e. g. modelled soil losses in EU vs.
soil erosion risk map in Switzerland) existed that might result in spatial inaccuracies  (Schulp et al. , 2014). However, as other authors have pointed out, this is an intrinsic limit of all pan-European, spatially explicit studies: "as fully harmonized data on the different aspects are not available, the possible bias from inconsistencies between the different data layers is unavoidable"  (Malek and Verburg, 2017). All the datasets used, required some degree of modelling and the maps therefore show predicted rather than measured environmental pressures. Moreover, not all the existing environmental problems in agricultural areas could be addressed. Methane emissions, ammonia emissions, and zoonoses contamination, for example, were not included in the analysis presented here. In addition, biodiversity aspects in terms of quality and diversity  (Zhang et al.
Acknowledgements We acknowledge funding through Grant  613520  from the  European Commission  ( Project AGFORWARD ,  7th Framework Program ). JVRD was supported by the  Government of Asturias  and the  FP7-Marie Curie-COFUND program  of the  European Commission  (Grant 'Clarín'  ACA17-02 ). We would like to thank three anonymous reviewers for their constructive comments, which helped to improve an earlier version of the manuscript.
The goal of the present work is to develop IAQ analysis procedures based exclusively on the parameters measured by sensors located indoors, taking into account possible sensor calibration issues. We present here the results of an approach based on unsupervised clustering  [6]. Unsupervised classification refers to algorithms that require no training set (blind partitioning), no a priori knowledge of the structure of the dataset, and automatically define the different classes. This approach is therefore adapted to our goal. The unsupervised classification method selected here is based on the K-means algorithm.
Instruments and measurement conditions The experimental results analyzed here were acquired during an intensive measurement campaign in February and March 2015 in a junior high school in northern France. This campaign was part of the MERMAID study  (2012) (2013) (2014) (2015) , which aimed to investigate the specificities, if any, of air quality within low energy consumption public buildings  [7]. Along with all the conventional or advanced instruments deployed, we also installed miniature sensors nodes (figure  1 ) developed in the lab, based on commercial electrochemical sensors (Alphasense, B4 series) for the quantification of NO, NO 2, and O3, and a NDIR CO2 sensor (Alphasense IRC-A1), together with non-selective metal oxide sensors. Data collection, storage and transmission were achieved with a Raspberry Pi B+ and an Arduino board.
Experimental datasets In the present paper, we focus only on the analysis of the signals from the selective sensors: NO, NO2, O3 and CO2, during the intensive campaign from February 28 to March 6, 2015, averaged over 1 minute (total of 8160 datapoints per sensor). In the absence of specific sources, such as combustion devices, NOx and ozone are mostly pollutants coming from outdoors. CO2 indoors mostly comes from the respiration of the occupants, with possible additional intake from outdoors. The CO2 sensor directly outputs the concentration, while the electrochemical sensors output is a voltage. The sensors were not calibrated before their installation in the room under investigation. The same species were also simultaneously measured with calibrated online gas analysers (Thermo Scientific 42i and 49i for NOx and O3) and with a Testo 480 probe for CO2.