dataesr/scientific-paragraphs-categorization huggingface dataset:
In the ND treatment, ER / ER MEGAN was not found to be significantly dependent on SW (y = 0. 653e 10. 52x , R 2 = 0. 13, Fig. 4a ). However, in the AD plot, ER / ER MEGAN increased exponentially with SW (y = 0. 192e 51. 93x , R 2 = 0. 66, Fig. 4b ) and in particular when SW became higher than the wilting point θ w measured at the O 3 HP site (0. 15 m 3 m -3 ). Similar findings were obtained for SW-7, SW-14 and SW-21, for both the ND and AD treatments (Table 1). In order to provide a better description of the impacts of ND and AD on ER as observed at the O 3 HP, an empirical type model, based on ANN optimisation of our observations at the O 3 HP, was developed specifically for Q. pubescens isoprene emissions. Training and validation of the different ANNs tested were made using values of ER, T , P , PAR, ST, and SW measured at the O 3 HP (COOPERATE database).
5a ), when ER variability was always poorly represented regardless of the different ANN settings considered. July corresponds to the period where trees started to adapt to ND and AD; this period was possibly insufficiently represented in our dataset to be well taken into account by our statistical approach. An overall underestimation of 6 % and 12 % was observed in the ND and AD respectively. For comparison, ER MEGAN calculated with a value θ w of 0. 15 m 3 m -3 are presented again in Fig. 5b  for both the ND and AD treatment. Under ND, the global contribution of the two lowest frequencies (-14 and -21 days) considered in G14 was, relative to the contribution of the two highest frequencies (instantaneous and -7 days), higher than under AD (Fig. 6 ). In particular, in October 2012 and April and June 2013, the two lowest frequencies respectively represented 20 %, 97 %, and 50 % of the total in the ND compared to 3 %, 55 %, and 26 % in the AD.
2. 8 ORCHIDEE land surface model: providing future conditions to investigate ER sensitivity to climatic changes Present-day T and P were assessed as the 2000-2010 daily averages derived from the ISI-MIP (Inter-Sectoral Impact Model Intercomparison Project) climate dataset (Warszawski et al. , 2014) over the Mediterranean area. This dataset contains the bias-corrected daily simulation outputs of the Earth system model HadGEM2-ES.
Acknowledgements. We are particularly grateful to  Pierre Eric Blanc ,  Jean Claude Brunel ,  Gérard Castagnoli ,  Armand Rotereau , and other  OHP  staff for support before and during the different campaigns. We thank members of the DFME team from IMBE:  Stéphane Greff ,  Caroline Lecareux ,  Sylvie Dupouyet , and  Anne Bousquet-Melou  for their help during measurements and analysis. This work was supported by the  French National Agency for Research (ANR)  through the projects  CANOPÉE  ( ANR-2010 JCJC 603 01 ) and  SecPriMe 2  ( ANR-12-BSV7-0016-01 ),  INSU (ChARMEx) ,  CNRS   National program EC2CO-BIOEFECT  ( ICRAM  project), and  CEA. We are grateful to  ADEME/PACA  for PhD funding. For O 3 HP facilities, the authors thank the research federation ECCOREV FR3098 and the  LABEX OT-Med  (no.
ANR-11-LABEX-0061 ), funded by the  French Government  through the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ). The authors thank the  MASSALYA instrumental platform (Aix Marseille Université , lce. univ-amu. fr)for the analysis and measurements used in this publication. Edited by: Xinming Wang Reviewed by: four anonymous referees.
Water balance modeling A box plot of average FTSW BR across all varieties for a given year shows 2012 and 2016 having significantly lower soil water content during the berry-ripening period than 2013 through 2015 (Figure  1 ). A plot of 2017 model output was not included as phenology and carbon isotope data are still under evaluation. FTSW estimates for 2017, however, were used for comparison against predawn water potential measurements. Absolute values of FTSW are likely underestimated due to difficulty estimating TTSW. Also, the model does not account for varietal differences in horizontal root system distribution. Overall, however, resulting relative differences between varieties remain valid.
Data from subsequent years should also improve the power of statistical determinations of differences between varieties. Water potential data collected in 2017 from eight varieties finds predawn measurements correlated well with soil water content estimates from a water balance model using the phenology of the individual varieties. The reason for the significantly different response of Semillon is uncertain and being investigated. Also, plots of midday leaf versus predawn leaf water potential measurements show the early development of hydroscape plots that might be useful in further characterizing the stomatal closure behavior of the different varieties in response to changing soil water content. Comparison of the different water potential measurements may give additional insight into stomatal closure response to decreasing soil water content.
Streamflow data for calibration Daily precipitation data were collected at the Tartu Observatory (15 km from the centre of the catchment). Daily average streamflow [m 3 s -1 ] of the Porijõgi was determined from the Parshall flume by Estonian Weather Service (EWS) at the Reola gauge. Streamflow was calculated by the EWS from stage-height measurements. For the subcatchments, we gauged streamflow from the closing weirs at the following frequencies during 2007-2013: eleven gaugings from March till December 2007, seven from March till December 2008, once a year at the spring-peak flow between 2009 and 2012, and ten gaugings between April and December 2013. Peak flow was captured in all the years. Streamflow at the Reola gauge correlated strongly with all the subcatchments (R 2 > 0. 8). Thus linear interpolation was used to fill the gaps in daily streamflow data of the subcatchments.
Water chemistry data for calibration Bimonthly water chemistry measurements from the Reola gauge were used for the entire catchment during 1987-2015. For the eight subcatchments, measurements by  Mander et al. (2000)  were used for the years 1987-1998. During 2007-2013 we sampled water from the closing weirs of the subcatchments at the same frequency as the streamflow data (see the previous section). We analysed the samples for ammonium N, nitrate N, total N, and total P following the APHA standards (APHA-AWWA-WEF, 2005). In order to interpolate to the days without measurements, streamflow events were identified on the Reola gauge graph. The nearest nutrient-concentration measurement within the streamflow event was assigned to a day  (Rekolainen et al. , 1991). Daily N and P runoffs, kg ha -1 d -1 were calculated.
Annual N runoff, kg ha -1 y -1 was calculated from the daily runoffs as follows: ∑ = × = Q Q conc N i n n N n 1 (2) where: Q N was annual N runoff, n was the number of days in the year, Q n was the average streamflow during the day, m 3 s -1 , and conc Nn was the nearest nutrient-concentration measurement within the streamflow event  (Rekolainen et al. , 1991)  2. 6. Land-use and soil data for calibration Land-use data for Porijõgi in years 1987 to 1997 was obtained from annual field surveys by  Mander et al. (2000). The following land-use categories were determined: arable land (including cultivated grassland, fertilised, with the sod tilled after each 5-8 years), forest (including shrub), grassland (without fertilisation, extensively mowed or non-mowed, including fallow land), and mire (swamps, fens, bogs, wet meadows).
Validation We used data from the 9 agricultural drainage-water stations in the monitoring and evaluation programme of Estonian Rural Development  Plan 2007 -2013 (Fig. 1; Supplementary Table). Exact locations of the individual farmlands were disclosed according to Public Information Act. Size of the catchments was < 31 km 2. Streamflow and concentration of total N and total P was sampled every two weeks under the commission of the Agricultural Research Centre of Estonia during  2007 -2014 (ARC, 2016)). The ARC measured soil N and P content in October 2007. Soil type and texture class were determined from the 1: 10 000 Estonian Soil Map. N and P fertilisation rates were taken from the farm-gate nutrient balance and use study based on books and interviews conducted by the ARC in the 120 farms using land in the The J28 catchment was located in a drained calcareous fen in the Oru municipality.
Acknowledgements This work was supported by the following funds: the  IUT2-16 ,  PUTJD619  and grants No. 6083 ,  7527  and  9387  of the  Estonian Research Council , Target Funding Projects SF0182534s03, SF0180052s07 and SF0180127s08 of the  Estonian Ministry of Education and Research , the  Parrot program  of the  French Institute in Estonia , the  European Regional Development Fund  (the  ENVIRON and EcolChange Centers of Excellence) , the  Estonian Environmental Investment Centre  Project No. 4234 , and the  Marie Skłodowska-Curie Actions  individual fellowships offered by the  Horizon 2020 Programme under REA , grant agreement number  660391.
Preliminary findings on a remunerated workforce in Ferlo and emerging research question As described by  Wane et al. (2009 Wane et al. ( , 2010)) , the quantitative data gathered for this research came from existing databases collected between June 2006 and June 2007 using questionnaires on a subset of 149 pastoral encampments from five sites (Boulal, Keur Momar Sarr, Niassanté, Tatki and Thieul). A pastoral encampment is a large unit of residence grouping one or more households. Results show that 25% of pastoral settlements declare that they use a remunerated workforce. These findings contribute to the revision of the idea that Sahelian pastoralism is an economic activity based solely on family labour. In these pastoral farms, production and sales decisions are often disconnected from market imperatives.
Phage Detection and Annotation Identification of phage genes is similar to any other gene annotation process. There are several annotation pipelines  [72] , such as RAST  [73] , Prokka  [74]  and others. Genome annotations starts by identifying genes, or more precisely open reading frames (ORF), i. e. , identifying start and stop positions in same frame of the prokaryote genome, along with function identification. Predicting ORF is done using software like Glimmer  [75]  or GeneMark  [76]. The next step consists in using these predictions and search databases, such as Genbank  [77]  and SwissProt  [78] , using mainly BLAST  [79] , or other programs. The accuracy of this step depends of the annotation software and the quality of the annotations already in the reference database  [72, 80]. To efficiently decrease annotation error comparison of results from multiple annotation services should be performed, interchanging information between annotation services  [80].
There are, however, specific packages and web servers for detecting and inferring prophage presence and completeness. Search for homologous may be done in relevant protein viral databases, like Phantome (http://www. phantome. org/), PHAST/PHASTER database  [81] , and VirSorter  [82]. These packages include the first ones developed, namely Prophage Finder  [83] , Phage_Finder  [84]  and Prophinder  [85]. Recently PHAST and PHASTER were developed performing 40 times faster and presenting results up to 15% more sensitive in comparison with the previous ones  [81, 86]. Other prophage identification tools have been described, such as PhiSpy  [87]  which analyzes several other sequence-based statistics to help identify novel phages (AT and GC skew, protein length and transcription strand directionality) that are not represented in existing phage databases, and VirSorter  [82]  that handles metagenomic data with improved performance for fragmented genomes.
These tools may run locally like (PhiSpy, Phage_Finder, Prophage Finder), to access through a web-server (PHAST/PHASTER), or made available through cyberinfrastructure (iPlant Discovery Environment  [88] , a Web portal of iPlant's cyberinfrastructure that houses several apps for sequencing analysis and other data-intensive technologies) that provides a web-based user interface. The strategies applied by the above phage finder tools are based on databases of gene and protein sequences of phage origin and other typical sequences found in phages, such as attachment sites attP and attB. Thus, finding novel prophages sequences is challenging when there is no counterpart in the database that serves the tool. Moreover, these tools allow to identify the contigs presenting phage sequences, but not necessary the complete phage sequence, which is rarely present in one contig only  [89].
In C and D the strains are colour-coded according to the population assignment by STRUCTURE using PST and MLST genes, respectively (adapted from  [10] ). Assembled prophages should then be annotated using general annotation pipelines, or specific for phage annotation, such as PHAST  [81] , Phages v. 1. 0 (available online: http://www. phantome. org/ PhageSeed/Phage. cgi?page=phast). Phage genomes typically have mosaic architectures and contain several small open reading frames of unknown function, approximately two-thirds the average size of bacterial genes, which is challenging for annotation and comparative analysis  [90]. Available tools to predict the potential gene function include BLASTP (using local or NCBI databases)  [79] , HHPred (more sensitive then BLASTP and allowing protein structure prediction)  [91] , presence of conserved domains detected using BLAST, and pairwise comparisons of protein-coding genes to group genes by sequence similarity or conserved domains  [92].
Since the tool is intended to help design integrated circuit systems, the Cadence platform was chosen. We have developed a library of high-level and low-level mod-els of the mainly used blocks for impedance spectroscopy from the literature  [4] [5] [6]. For high-level models we use Verilog-AMS, which is a derivative of the Verilog hardware description language that enables designers to create analog and mixed-signal modules containing high-level behavioral descriptions of the system blocks. For the low-level model description, we use transistor-level models simulated by the Cadence Virtuoso suite. We apply this tool to the design of an integrated circuit suitable for in vivo monitoring of bluefin tuna physiological parameters.
This indicates some interaction of the hexyl chain with the pyridine moiety. In any case, the 3 MLCT lifetimes were determined as for the other complexes by excitation of the lowest-energy Fe-pyridine transition. Figure  9b  shows the effect of the side-chain substitution for C8, which displays a longer τ 2 than C1 (12. 0 ± 1. 0 ps). A similar 20% increase of the 3 MLCT lifetime is observed for C9, τ 2 = 19. 5 ± 1. 0 ps, in comparison with C3. The trend goes in the right direction, but it is unclear whether these relatively small improvements are due to the postulated increase of the Fe-ligand bond rigidity or shielding effects against solvent quenching, since we are lacking structural data for these compounds. As expected, the absorption spectra of C1 and C8 are almost identical (Figure  9 ), both for the pyridine-and the imidazole-related transitions.
Indeed, the alkyl side chains are not electronically conjugated with either of the two moieties. Comparison of C3 and C9, however, reveals a splitting of the pyridine transition, by the appearance of a shoulder in the spectrum of C9 at 410 nm and broadening of the low-energy tail. This indicates some interaction of the hexyl chain with the pyridine moiety. In any case, the 3 MLCT lifetimes were determined as for the other complexes by excitation of the lowest-energy Fe-pyridine transition. Figure  9b  shows the effect of the side-chain substitution for C8, which displays a longer τ2 than C1 (12. 0 ± 1. 0 ps). A similar 20% increase of the 3 MLCT lifetime is observed for C9, τ2 = 19. 5 ± 1. 0 ps, in comparison with C3. The trend goes in the right direction, but it is unclear whether these relatively small improvements are due to the postulated increase of the Fe-ligand bond rigidity or shielding effects against solvent quenching, since we are lacking structural data for these compounds.
Biophysical parameters that were produced by the PROSPECT model inversion together with texture parameters compiled from the literature were deduced. Then we investigated their relationship to damage caused by Yellowing and Esca. This study examined whether spectral and textural data can identify the two diseases through the use of Neural Networks. We obtained an overall accuracy of 99% for both of the diseases when textural and spectral data are combined. These results suggest that, first, biophysical parameters present a valid dimension reduction tool that could replace the use of complete hyperspectral data. Second, remote sensing using spectral reflectance and digital images can make an overall nondestructive, rapid, cost-effective, and reproducible technique to determine diseases in grapevines with a good level of accuracy.
It enabled the identification of a single sensitive band and a spectral ratio for satellite in-field monitoring of the disease  [4]. Seven spectral bands at the leaf-level and one band at canopy-level were identified by  [5]  for assessing the infestation level of rice leaf folder damage through the analysis of hyperspectral data gathered from healthy and infected sample leaves. Biophysical parameters can be generated from high dimensional raw reflectance data using the PROSPECT model inversion, reducing the dimensionality of the spectral measurements. Researchers have adopted Radiative Transfer Models (RTM) for many applications. For example, the study in  [6]  demonstrated that RTM, together with hyperspectral (HS) airborne imaging, can be of great importance to determine the Leaf Area Index in grassland.
We considered two grapevine diseases in our study: Esca and yellowing of the grapevine and data were acquired from two different regions in France. Esca of the grapevine is a type of grapevine trunk diseases. In the last years, several European wine countries such as France, Italy and Spain have noted an increase of occurrence of the disease. Li et al. [13]  assessed the nature of Esca spread within commercial vineyards in Bordeaux region, France for a long period of time. Another work was carried out in  [14]  from 2004 till 2006 in five mature vineyards in Aquitaine Province, France to monitor Esca occurrence. Leaf symptoms mainly appear as a stripe pattern (Figure  1 ), they can begin to emerge any time during the growing season, but they are mostly seen during July and August. When looking inside the trunks and main branches, a cross-sectional cut of symptomatic shoots uncovers the concentric rings of dark spots  [15, 16].
The aim of this paper was to study the feasibility of detecting Esca and yellowing of the grapevine using spectral and texture data. On one side, we presented different methods and functions for performing a PROSPECT model inversion to calculate the biophysical parameters. On the other side, we compared the efficiencies of texture and spectral analyses in classifying sample leaves. The novelty of this work relies on suggesting the optimal association of methods and functions that are used for the PROSPECT model inversion. Furthermore, to our knowledge, this is the first study that attempts to test the ability of combining spectral (biophysical parameters) and image data (texture parameters) in distinguishing damaged grapevine leaves from healthy ones. The results of using biophysical parameters are also compared to using complete hyperspectral measurements.
Spectral and Image Data Analysis for Disease Detection The complete approach that is applied in this paper is summarized in Figure  6. The first step of our workflow is the computation of biophysical parameters using PROSPECT model inversion requiring the hyperspectral spectrum. In same time, texture parameters is estimated from RBG image data. Both information are merged, and are used in a classification neural networks predicting health status of the leaf. The choice of the method and the function for the PROSPECT model inversion will be detailed next.
The corresponding workflow is presented in the Figure  7. Python programming language was used to implement the different algorithms, along with libraries like PROSAIL and Scipy. • RGB to HSV conversion First, RGB images were converted to Hue Saturation Value (HSV) space through a non-linear transformation. This color space was selected since it is a good descriptor that is close to human perception. Hue describes an actual color as perceived, saturation is the purity of a color, and value means the lightness or darkness of a color. v = max(r, g, b) (5) s = (v -min(r, g, b) × 255)/v v = 0 0 v = 0 (6) h =      (g -b) × 60 s v = r 180 + (b -r) × 60 s v = g 240 + (r -g) × 60 s v = b      (7) i f h < 0then h = h + 3 = 60 (8) where r, g, and b represent pixel values of red, green, blue (RGB) color of image, respectively, and h, s, and v represent the new color channels in the HSV color space.
Classification of Esca Disease Table  3  and Figure  9  present the classification results of data gathered from Bordeaux and the ROC curves relative for each dataset, respectively. Texture features gave the best accuracy of classification (100%). Image data were clearly more advantageous than using spectral information for the classification of Esca, in fact, when complete spectral data or BPs are applied as input, the accuracy was lower (80. 62-77. 5%). Combining BPs and TPs gave a satisfying result (99. 37%), implying the convenience of the association of different types of features for a better classification result. When we evaluated the confusion matrices, we found that the number of FP and FN are almost the same, which indicates that the classifier is not over evaluating the diseased class over the healthy one.
Classification of Yellowing Disease Table  4  and Figure  10  present the classification results of data that were collected from Burgundy and the ROC curves relative for each dataset, respectively. For identifying the Yellowing of the grapevine, the combination of BPs and TPs gave the most accurate result of detection (99. 54%). Spectral information was more adequate in this case than image data, in fact, when applying complete spectra or only BPs, the detection precision is high (93-95. 45%) with respect to only 70. 45% of good results for TPs. Similar to results that were obtained for Esca disease, we found that the classifier could present the good accordance with the truth data without over-evaluation.
Conclusions and Perspectives New systems are needed to respond to the worldwide spread of crop diseases. In particular, non-contact systems are required to aid farmers and producers in automatically identifying initial symptoms of plant diseases. PROSPECT-5 was upgraded in the visible region to take into account anthocyanin (PROSPECT-D). In our inversion method, the least accurately predicted parameter was Car. An addition of another parameter to PROSPECT-5 may provide an overall increase of the inversion method precision. In the case of Grapevine that is affected by Esca showing red/brown tones, the presence of the disease might be predicted by anthocyanin pigments, therefore the PROSPECT-D model is examined for detecting Esca and Yellowing in the near future. This study indicated that remotely sensed data that are recorded by a hyperspectral spectrometer and a digital camera have the potential to aid in monitoring damage in grapevines under field conditions.
Acknowledgments: We thank all the vine-growers who participated in this study that is part of the  DAMAV  project  (Automatic Detection of Grapevine Diseases ). We also would like to thank  Alice Dubois  and  Sylvain Bernard  from the  Regional Federation of Defense against Pests of Provence Alpes Côtes Azur ,  Corinne Trarieux  from the  Interprofessional Office of Burgundy Wine ,  Jocelyn Dureuil  from the Chamber of Agriculture 71 and finally  Arnaud Delaherche  from  Pape Clement Castle in Bordeaux , for their excellent technical assistance.