dataesr/scientific-paragraphs-categorization huggingface dataset:
Formosat-2 Leaf Area Index Formosat-2 is an NSPO (Taiwan) satellite that can generate daily multispectral images of the Earth's surface at an 8 m resolution and with a swath of 24 km. It functions on a tasking mode, i. e. , it does not acquire data systematically like Sentinel-2 but rather must be programmed for a target area. Its sensor detects radiation within four frequency bands of blue, green, red and near-infrared. After geometric, atmospheric and radiometric corrections were made and clouds are detected  (Hagolle & al. , 2008 and 2010) , measured reflectances were entered into the neural network BV-NET, which inverts the PROSAIL radiative transfer model  (Claverie, 2012). This neural network deduces a set of vegetation parameters (among them the LAI and fraction of vegetation cover (FCOVER)) for each pixel.
Comparison methods We extracted the outputs of both simulations from the Auradé and Lamasquère station parcels. We then calculated correlation coefficient (R²) and Root-Mean Square Error (RMSE) values between monthly averages of the measured and simulated latent heat fluxes (LE, evapotranspiration). Then, we analyzed differences between both simulations for the entire modeling domain by calculating correlation coefficients between the monthly simulated evapotranspiration time series for each parcel. These correlation maps allow one to identify parcels where effects of using Formosat-2 data on the temporal evolution of evapotranspiration are more pronounced. (1) where w is the volumetric soil water content, w wilt is the volumetric soil water content at the wilting point and w fc is the volumetric soil water content at field capacity.
Local comparisons with in situ measurements Our simulated evapotranspiration comparisons between eddy covariance measurements of the study sites in Auradé and Lamasquère show that using Formosat-2 data in the SURFEX simulation improves the correlation and RMSE of almost every year (Tables  2  and 3 ). The improvement is more significant when measurement fields are covered in sunflower or maize crops, i. e. , summer crops. By contrast, the effects of Formosat-2 data are not as strong for wheat and rapeseed crops, i. e. , winter crops. To understand why these differences appear, we compared the time series of LAI and evapotranspiration for both sites for 2006 (Fig. 5  and 6 ). Silage maize was grown in Lamasquère in 2006. This crop is harvested before plant senescence; hence, the observed cycle is shorter than a typical maize cycle.
This better description of the LAI based on Formosat-2 leads to a better simulation of evapotranspiration timing (Fig. 6 ). In particular, the evapotranspiration peak is delayed by one month for summer crops (i. e. , on Lamasquère) and thus fits the measurements better (Sect. 4. 2 below). However, the Formosat-2 data do not match the actual amplitude of the evapotranspiration peak. This is most likely due to the lack of irrigation in the model given that the Lamasquère site was irrigated between June and August (147 mm). Adding the measured irrigation rates to the SAFRAN precipitation forcing improves this simulation with respect to LE measurements (not shown here). Hydrol. Earth Syst. Sci. Discuss. , doi:10. 5194/hess-2016 -661, 2017  Manuscript under review for journal Hydrol. Earth Syst.
Conclusion The use of high spatial resolution and high revisit frequency Formosat-2 data rather than standard ECOCLIMAP-II vegetation parameters was found to improve the representation of the vegetation phenology of the SURFEX for a cultivated area characteristic of southwestern France. Local comparisons with in situ observations show that this in turn improves monthly evapotranspiration simulations. This finding is particularly true for summer crops, whereas the impact is less significant for winter crops and is even less significant outside of crop areas. From our spatial comparisons, we suggest that our local scale results are likely transposable across the whole domain. For crop growing areas, the Formosat-2 LAI is lower than that of ECOCLIMAP-II before crops grow, and thus, the Soil Water Index (SWI) remains higher during the crop growing season.
g. , sowing and harvest dates and fertilizer inputs). In the present study, Formosat-2 data were limited to a 24 km by 24 km area whereas water management agencies typically manage river basins at a regional scale (above 10 4 km²). Sentinel-2A already images the Earth' land area at a similar radiometric spatial resolution over a 10-day revisit period. The optimal revisit period of 5 days should be achieved in the next few months after the launch of Sentinel-2B. Our study shows that Sentinel-2 observations will soon allow us to improve the monitoring of water resources in large anthropized river basins. Scalable algorithms for crop type mapping and LAI Hydrol. Earth Syst. Sci. Discuss. , doi:10. 5194/hess-2016 -661, 2017  Manuscript under review for journal Hydrol. Earth Syst. Sci. Published: 10 January 2017 c Author(s) 2017.
Acknowledgments: This study was conducted as part of the  REGARD  project entitled  « Modélisation des REssources en eau sur le basin de la GAronne , interaction entre les composantes naturelles et anthropiques et apport de la téléDétection » (http://www. cnrm-Hydrol. Earth Syst. Sci. Discuss. , doi:10. 5194/hess-2016-661,2017 Manuscript under review for journal Hydrol. Earth Syst. Sci. Published: 10 January 2017 c Author(s) 2017. CC-BY 3. 0 License.
Longitudinal MS Data The MS patients involved in this study were scanned multiple times over a different period for each patient, ranging from 2. 5 to 6 years. The minimum number of scans is 3, while the maximum Disease duration (years) 2. 9 (1. 9) 8. 3 (4. 8) 7. 5 (2. 9) 14. 9 (6. 1) EDSS median (range) 1 (0-4) 2 (0-5. 5) 4 (2-7. 5) 5 (3-8. 5) is 10. The gap between two consecutive scans is either 6 months or 1 year. In total there are 619 MS scans, but because of missing lesion loads and metabolic features, there are 592 (95. 6%) scans with full complete data, leading to an average of 6-7 complete scans/patient.
MRSI Acquisition MRSI data was acquired from one slice of 1. 5 cm thickness, placed above the corpus callosum and along the anterior commissure -posterior commissure (AC-PC) axis, encompassing the centrum semioval region, and took 5 min and 20 s. A pointresolved spectroscopic sequence (PRESS) with TR = 1,690 ms and TE = 135 ms was used to select a volume of interest (VOI) of 105 × 105 × 15 mm 3 during the acquisition of 24 × 24 (interpolated to 32 × 32) phase-encodings over a field of view (FOV) of 240 × 240 mm 2.
MRSI Processing MRSI data processing was performed using SPID  (Poullet, 2008; Poullet et al. , 2008)  in MatLab 2015a (MathWorks, Natick, MA, USA). AQSES-MRSI  (Poullet et al. , 2007; Sava et al. , 2011)  was used to quantify N-acetyl-aspartate, Choline (Cho), and Creatine (Cre), using a synthetic basis set. The basis set incorporates prior knowledge of the individual metabolites in the quantification procedure. MPFIR (maximum-phase finite impulse response) filtering  (Sundin et al. , 1999)  was included in the AQSES-MRSI procedure for residual water suppression, with a filter length of 50 and spectral range from 1. 9 to 3. 4 ppm. A band of two voxels at the outer edges of each VOI was discarded in order to avoid chemical shift displacement artifacts and lipid contamination artifacts.
Feature Extraction In this study we use three types of features: clinical (e. g. , patient age, disease duration, and EDSS), lesion loads, and metabolic features. The clinical features are routinely acquired in the hospital. The lesion loads were computed based on T1 and FLAIR, using the MSmetrix software  (Jain et al. , 2015)  developed by icometrix (Leuven, Belgium). The computation of metabolic features was performed in two steps: three metabolic ratios (NAA/Cho, NAA/Cre, Cho/Cre) were computed for each "good quality" voxel and then averaged, leading to three metabolic features extracted from each MRSI grid.
Classifiers Three supervised classifiers implemented in Python 2. 7. 11 with scikit-learn 0. 17. 1  (Pedregosa et al. , 2011)  have been used: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Random Forest (RF). We tuned each classifier's parameters by optimizing the F1-score over a fivefold cross validation on the training set within a grid search of individual parameters, specified further for each particular classifier. Fisher's LDA  (Fisher, 1936)  is based on a linear combination of input features, with three possible solvers: singular value decomposition, least squares solution, and eigenvalue decomposition. Tuning involved choosing between the first solver and the last two solvers combined with shrinkage varying from 0 to 1 in steps of 0. 1. Class unbalance was adjusted by setting the priors parameter equal to class probabilities.
RESULTS Figure  1  shows boxplots comparing MR metabolic features (Figures  1A-C ) and lesion loads (Figure  1D ) extracted from HC and each MS course. Boxplots are drawn using default style in MatLab, meaning the middle line inside the box represents the median value, the vertical limits are the 25th and 75th percentiles (q 1 and q 3 ), each whisker covers 1. 5 the interquartile range (i. e. , q 3 -q 1 ), and the crosses outside the whiskers represent outliers. Supplementary Figures  1 2 3 4 show the MS data points in various 2-D feature spaces. Using the previously described (Section 2. 6) linear mixedeffects models we found that the fixed effect MS course is statistically significant in the evolution of NAA/Cho, NAA/Cre, Cho/Cre, and LL, with corresponding p-values of: 3. 4 × 10 -6 , 2 × 10 -4 , 2.
Other MS classification studies are Muthuraman et al. (  2016 ) and  Kocevar et al. (2016) , both based on diffusion features. The first one reports a classification accuracy of 97% between 20 CIS and 33 RR patients. The second one analyzes classification tasks based on DTI data from a cross-sectional subset of our database. They found very high F1-scores (91. 8% for both HC-CIS and CIS-RR) after training SVM-rbf on six global brain connectivity metrics. For RR vs. PP their maximum F1-score was 75. 6%, which is lower than our results based on metabolic features, while for RR vs. SP, their maximum F1-score was 85. 5%, which is comparable to our results. It is also worth mentioning that they did not use any clinical data, which might improve their results. In this study, we analyzed the added value of combining standard clinical data with quantitative magnetic resonance features.
Disease duration interval for RR patients is much larger than for CIS patients, as most of CIS patients have a disease duration lower than 5 years, which can be seen in Supplementary Figure  3A. Because RR patients have more relapses than CIS patients, the number of lesions will be higher and the lesion volume as well, while EDSS scores are mainly in the same range, as seen in Supplementary Figure  4A. BAR values in Table  A2  show a maximum of 85, when combining patient age, disease duration, EDSS, and lesion load. However, the corresponding maximum F1-score of 71 is much lower because the dataset is unbalanced (61 CIS vs. 214 RR), heavily influencing the classifier's precision. In this case the F1-score reflects better than BAR the difficulty of discriminating CIS from RR forms. CIS and SP patients' distribution over different features is visible in Supplementary Figures  2B, 3B , 4B and it is clear that these two are the least and most advanced forms of MS.
Because RR patients will eventually evolve into SP forms during their lifetime, we grouped together RR and SP patients for a separate classification task versus CIS patients. BAR values in Table  A2  show a maximum of 92, when combining patient age, disease duration, EDSS and lesion load. The same discussion as for CIS vs. RR apply: the corresponding maximum F1-score is only 72 because the dataset is very unbalanced (61 CIS vs. 410 RR+SP) and the precision will be very low. RR and PP patients can be discriminated using only EDSS by visually inspecting Supplementary Figure  4C. Training a linear classifier on clinical data (patient age, disease duration, and EDSS) gives the maximum F1-score of 85. Adding the 3 metabolic features keeps the score at 85, while adding lesion load information lowers the score to 79.
This drop in the F1-score suggests that lesion load is not useful in differentiating RR from PP patients. Indeed, these two MS forms have the closest lesion load averages (16. 7 and 20. 8 ml), as shown in Table  1. In contrast, the clinical status of RR and PP patients are very different, as reflected by the EDSS values of 2 for RR and 4 for PP. Moreover, training LDA on individual metabolic features always provided higher F1-scores than lesion load, therefore we can conclude that for RR vs. PP, metabolic features have a higher discrimination power than LL. BAR values in Table  A2  are also closer to the F1scores in Table  5  because the dataset is more balanced compared to previous cases. RR and SP patients can also be discriminated using only EDSS by visually inspecting Supplementary Figure  4D.
FUNDING This work was funded by  European  project  EU MC ITN TRANSACT 2012  (no. 316679 ) and the  ERC Advanced Grant  BIOTENSORS no. 339804. EU: The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007(FP7/ -2013 )). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.
The food enzyme considered in this opinion is a 4-a-D-glucan maltohydrolase (EC 3. 2. 1. 2) obtained from grain of barley (Hordeum vulgare), by the companies Genencor International B. V. and Senson Oy. This b-amylase is intended to be used in several food-manufacturing processes: baking and brewing processes, distilled alcohol production, and starch processing for the production of glucose syrups. The compositional data provided for the food enzyme were considered sufficient. The manufacturing process did not raise safety concerns. Based on the maximum use levels recommended for the respective food processes, dietary exposure to the food enzyme-total organic solids (TOS) was estimated on the basis of individual data from the EFSA Comprehensive European Food Consumption Database. This exposure estimate is similar to or lower than the exposure to a fraction of barley comparable to the food enzyme-TOS, resulting from the consumption of barley-derived foods.
Chemical parameters The molecular mass of 59. 6 kDa reported in the dossier for the b-amylase from barley Hordeum vulgare) in the food enzyme, was calculated from the amino acid sequence deduced from the cDNA sequence (UniProtKB -P16098 7  ). Data on chemical parameters of the food enzyme were provided for three commercial batches (Table  1 ). The average total organic solids (TOS) content of the three commercial batches was 49. 3% (w/w); the values ranged from 47. 4% to 51. 9%. The TOS content is a calculated value of 100% food enzyme minus % water content minus % ash. Enzyme activity, expressed as degrees diastatic power (°DP), ranged from 1,828 to 2,033 °DP/g batch. The average enzyme activity/TOS ratio of the three commercial food enzyme batches was 3. 95 °DP/mg TOS; the values ranged from 3.
77 to 4. 16 °DP/mg TOS. Considering the low variability of the enzyme activity/TOS ratio of the three batches, the average activity/mg TOS ratio of 3. 95 °DP/mg TOS was used for subsequent calculations. The protein pattern of the food enzyme was determined using sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis. The protein profiles provided for three food enzyme batches were comparable. In addition to the band assigned to b-amylase by the applicant, i. e. slightly above 50 kDa, the presented gels showed two further major protein bands at ~38 and 12 kDa with similar intensities, and several other additional bands. The observed complexity of the protein profiles reflects the fact that the food enzyme is extracted from barley grain without further protein fractionation. No analytical data on the activities of other enzymes in the food enzyme product were provided.
Manufacturing process The manufacturing process comprises extraction and downstream processing. A comprehensive dataset related to the manufacturing process including a list of raw materials used and a flow diagram was provided. The food enzyme is manufactured in accordance with Regulation (EC) No 852/2004 12 , with food safety procedures based on HACCP principles, and in accordance with current good manufacturing practice. Prior to extraction, barley grains may be subjected to dehusking and/or malting. The food enzyme is obtained by extraction with water and separated from insoluble barley material by centrifugation and filtration. After separation, the liquid containing the enzyme is concentrated to reach the desired enzyme activity and/or to increase the enzyme activity/TOS ratio. Concentration may be achieved by ultrafiltration, diafiltration and/or evaporation.
Dietary exposure Following the EFSA Guidance Document on food enzymes (EFSA, 2009b), a comparison was made between: • dietary exposure to the food enzyme-TOS, resulting from the intended use as proposed by the applicant (herein referred to as 'FE-TOS'); and • dietary exposure to a fraction of barley comparable to the food enzyme-TOS, resulting from the consumption of barley-derived foods (herein referred to as source material TOS equivalent, 'SMT-Equivalent'). In both cases, food consumption data from the EFSA Comprehensive European Food Consumption Database (hereafter the EFSA Comprehensive Database 13  ) were used. Exposure estimates were calculated using the methodology described in the CEF Panel statement on the exposure assessment of food enzymes (EFSA CEF Panel, 2016). The assessment of the food processes covered in this opinion involved selection of relevant food groups and application of process and technical conversion factors (Appendix B), as appropriate (Section 3.
EFSA Comprehensive European Food Consumption Database Since 2010, the Comprehensive Database has been populated with detailed national data on food consumption. Competent authorities in European countries provide EFSA with data regarding the level of food consumption by individual consumers, as taken from the most recent national dietary survey in their country (EFSA, 2011a). The food consumption data gathered by EFSA were collected using different methodologies and thus direct country-to-country comparisons should be interpreted with caution. Depending on the food category and the level of detail used in exposure calculations, uncertainties might be introduced owing to possible subjects' underreporting and/or misreporting of consumption amounts. Nevertheless, the EFSA Comprehensive Database represents the best available source of food consumption data across Europe.
Exposure assessment methodology Chronic exposure was calculated based on individual consumption, averaged over the total survey period, excluding surveys with only one day per subject. High-level exposure/intake was calculated for only those population groups in which the sample size was sufficiently large to allow calculation of the 95th percentile (EFSA, 2011a). Exposure to the FE-TOS was calculated by multiplying values reported in Appendix B for each food category by their respective consumption amount per kilogram of body weight (kg bw) separately for each individual in the database. The dietary exposure to the SMT-Equivalent was calculated by first estimating the intake of barley grain from all dietary sources (applying recipe and conversion fractions reported in Appendix C). Second, the so-derived intake of barley grain was converted into a barley fraction comparable to the food enzyme-TOS via application of a factor provided by the applicant to take into account the yield of the FE-TOS from the barley source (Section 3.
Exposure to food enzyme-TOS according to the intended use proposed by the applicant Exposure to the FE-TOS was estimated based on intended uses and the recommended maximum use levels of the food enzyme-TOS provided by the applicant (Table  2 ). The FE-TOS exposure was calculated from foods produced involving baking and brewing processes. Foods/ingredients derived through alcohol (distillation) processes and starch processing, i. e. spirits and glucose syrups, were excluded from the analysis. Experimental data on the significant removal (> 99%) of protein in the course of these processes have been provided (Documentation provided to EFSA, No. 6). The Panel considered this evidence as sufficient to conclude that the presence of residual amounts of TOS after distilling or filtration and purification during processing is negligible.
Relevant food groups and/or individual foods were selected from the Comprehensive Database and were assumed to always contain the FE-TOS at the maximum recommended use level. This will result in an overestimation of exposure to FE-TOS. To facilitate matching of the reported use levels for baking and brewing processes with foods identified in the Comprehensive Database, the selected foods were disaggregated to ingredient level as appropriate, and converted into the corresponding raw material, i. e. flour or grain, via the application of conversion factors (Appendix B). For example, consumption of 100 g of bread was converted into an intake of 70 g flour (recipe fraction of 0. 7) and then multiplied by 20 mg TOS/kg flour, as provided by the applicant, to arrive at an exposure of 1. 4 mg TOS/100 g bread.
Exposure to the FE-TOS was calculated by multiplying values reported for each food category by their respective consumption amount per kilogram of body weight separately for each individual in the database. Table  3  provides an overview of the derived exposure estimates. The average and 95th percentile exposure to the food enzyme-TOS per age class, country and survey are reported in Appendix E -Table  1. The contribution of the food enzyme-TOS from each FoodEx category to the total dietary exposure is indicated in Appendix E -Table  2.
Dietary exposure to SMT-Equivalent resulting from the consumption of barley-derived foods The FoodEx categories likely to contain barley or be produced from barley were selected from the EFSA Comprehensive Database, at the most detailed level possible (up to FoodEx Level 4) (EFSA, 2011b). The process of selecting relevant food groups was aided by obtaining qualitative information from the Mintel's Global New Products Database. 15 ajor FoodEx categories identified as containing barley or being derived from barley comprise beer, coffee imitates, breakfast cereals, barley-derived distilled spirits and malted beverages. Barley was found as a minor ingredient in some FoodEx categories, but due to a lack of detailed recipe information, these categories were not taken into account in the assessment (see Appendix D).
b-Amylase and other proteins extracted from barley grain Potential allergenicity of b-amylase from barley was assessed by comparing the amino acid sequence (Kreis et al. , 1987) with those of known food allergens according to the EFSA Scientific opinion on the assessment of allergenicity of GM plants and microorganisms and derived food and feed of the Scientific Panel on Genetically Modified Organisms (EFSA GMO Panel, 2010). Using > 35% identity in a sliding window of 80 amino acids as the criterion, no matches were found. With regard to the allergenicity of b-amylase from barley, conflicting data are presented in various allergen databases. In the AllergenOnline database, 17 b-amylase from barley is not listed as an allergen, whereas in the WHO/IUIS Allergen Nomenclature database 18 b-amylase from barley is indicated as Hor v 17  19  and indicated as a food allergen with no further information.