dataesr/scientific-paragraphs-categorization huggingface dataset:
Filtering of emissivity spectra and resulting database To minimize redundancies in the library of simulated emissivity spectra, we filtered the SAIL-Thermique input and output spectra by removing those that depicted similarities in spectral behavior. We used the spectral angle mapper (SAM) algorithm that calculates  the similarity between two spectra  (Girouard et al. , 2004). SAM measures the angle γ between two spectra, treating them as vectors in a v-dimensional space (v being the number of spectral values). We implemented the SAM procedure as following. Each spectrum was used as a reference and compared to the other ones. In case any of the latter was similar to the reference spectrum, it was removed from the library. Two spectra were considered similar when angle γ between both was lower than 1°.
The SAM based filtering was firstly applied to SAIL-Thermique input spectra. Among the 313 soil spectra and 64 leaf spectra to be filtered separately, SAM selected 65 spectra of soil reflectance/transmittance and 35 spectra of leaf reflectance. When combined with the LAI and ALA values, we simulated 63,700 canopy emissivity spectra (65 soil spectra × 35 leaf spectra × 7 LAI values × 4 ALA values) using SAIL-Thermique. The SAM based filtering was secondly applied to SAIL-Thermique output spectra, which led to the selection of 271 simulated emissivity spectra. The database of 271 emissivity spectra was split into two equal parts: a calibration data set with 136 spectra, and a validation dataset with 135 spectra.
Setting up a library of surface outgoing radiance to apply and validate TES As explained in Section 2, TES is applied to TIR remote sensing measurements at the surface level, i. e. over a set of waveband values of surface outgoing radiance within the channels of the considered sensor. The surface outgoing radiance is the sum of the emission component that involves surface emissivity and radiometric temperature, and of the reflection component that involves atmospheric downwelling irradiance and surface reflectance, the latter being assumed equal to 1emissivity  (Schmugge et al. , 1998). We computed the emission component from the emissivity spectra and the prescribed values of surface radiometric temperature. We used the 135 emissivity spectra from the validation dataset. For each spectrum, 10 radiometric temperature values were randomly drawn (uniform distribution) within the [270-340] K range, with a maximum value of 305 K for LAI larger than 4, so that radiometric temperature is consistent with vegetation cover at satellite overpass around solar noon, for a large range of soil, plant and atmospheric conditions  (Chávez et al.
, 2011). We computed the reflection component from both emissivity spectra and spectra of atmospheric downwelling irradiance. We used the same emissivity spectra as selected when computing the emission component. We simulated the spectra of atmospheric downwelling irradiance using the MATISSE-V2 (Acronym for French label meaning "earth advance modeling for imagery and simulation of scenes and their environments") atmospheric radiative transfer model  (Labarre et al. , 2010; Labarre et al. , 2011). We used atmospheric profiles from the Air Force Research Laboratory (AFRL) dataset  (Simoneau et al. , 2006)  as inputs for MATISSE-V2. We selected three atmospheric profiles ("US standard 1976", "tropical" and "mid-latitude winter") that represented a large range of atmospheric conditions, from dry to humid atmospheres and from cold to warm atmospheres.
The MATISSE simulations were conducted over the [740-1335] cm -1 spectral interval, with a 1 cm -1 spectral resolution that corresponds to 0. 005 μmat7. 5μm and to 0. 02 μmat 13. 5 μm. The spectra of atmospheric downwelling irradiance were then resampled to the spectral resolution of the SAIL-Thermique simulations, by using a linear interpolation. We combined each of the 135 emissivity spectra from the validation dataset with the 10 prescribed values of radiometric temperature and the three spectra of atmospheric downwelling irradiance. Among the resulting 4050 spectra of surface outgoing radiance, we selected those for which the surface -air temperature gradient ranged between -10 K and +30 K, where air temperature is the temperature value at the first atmospheric profile level  (Du et al.
Calibrating and validating the TES method The ε-min -MMD empirical relationship of the TES method was calibrated for each sensor spectral configuration. We used the waveband values derived from the 136 emissivity spectra of the calibration dataset. Coefficients A, B and C were calibrated over the dataset of (εmin, MMD) pairs, by minimizing the quadratic difference between actual and predicted values. We used the R "Optim" function that relies on the Newton's method (R Development Core Team, 2011), and we obtained the same calibration results regardless of initial guess. The resulting root mean square error (RMSE) was calculated as the calibration residual error. Additionally, we evaluated the calibrated ε-min -MMD relationship against the 135 emissivity spectra from the validation dataset.
Calibration of the TES empirical relationship Fig. 3  displays a typical example of the calibration results for the εmin -MMD relationship when considering the MASTER sensor. We obtained similar results with the other sensor spectral configurations. Indeed, the calibrated relationships were rather similar for low MMD values, with differences in ε-min value around 0. 007 for a MMD value of 0. 1, and they could be quite different for large emissivity spectral contrasts, with differences in ε-min value up to 0. 02 for MMD values of 0. 3. Also, the channel number impacted the MMD range, up to 0. 05 MMD units for large MMD values. Table  2  displays the results we obtained when calibrating the ε-min -MMD relationship for each sensor spectral configuration, as well as when comparing the ε-min estimates against those derived from the validation dataset.
The A, B, C coefficients were different from one configuration to another, but they were similar when channel numbers were close (e. g. , MODIS versus MISTIGRI with 3 channels, ASTER versus MISTIGRI with 4 channels, or ASTER versus HyspIRI). The calibration er-rorsdecreasedby25-30% when the channel number increased from 3 to 10, and it remained lower than 0. 0065. Similar results were obtained when comparing ε-min estimates against those derived from the validation dataset, with errors lower than 0. 007.
Validation of the TES retrievals of emissivity and radiometric temperature Fig. 4  displays a typical example of the results obtained when comparing TES retrievals of waveband emissivity against prescribed values from the validation dataset. Prescribed values were waveband emissivities derived from the emissivity spectra used to compute waveband surface outgoing radiance over which was apply TES (see Fig. 1 ). This typical example corresponded to the ASTER spectral configuration, and we obtained similar results with the other sensor spectral configurations. It is shown that the emissivity spectra were well retrieved by TES. For a given spectrum, the retrievals of waveband emissivity were closer to the prescribed values at larger wavelengths that usually corresponded to flatter portions of the emissivity spectra (10 to 12 μm).
Fig. 5  displays the comparison of the TES retrievals of waveband emissivity against the prescribed values derived from reference spectra of the validation dataset, when considering the MISTIGRI spectral configuration with four channels. We obtained similar results with the other sensors. Regardless of channel, we noted larger discrepancies around the 1:1 line for large emissivity values. Besides, retrievals of waveband emissivity agreed better with prescribed values for larger wavelengths (10 to 12 μm) that corresponded to flatter portions of emissivity spectra (Fig. 4 ). This was observed for all sensor spectral configurations, with RMSE values decreasing from 0. 01 to 0. 005, apart from the MASTER channel at 7. 8 μm(Fig. 6 ). Fig. 7  displays the comparison of TES retrievals of radiometric temperature against the prescribed values when considering HyspIRI.
As an example, Fig. 8  displays the εmin -MMD relationship when considering the newly fitted calibration we proposed in the current study and that proposed by  Gillespie et al. (1998)  for the ASTER spectral configuration. We obtained similar results when dealing with MODIS. Fig. 8  shows that both calibrations provided different ε-min predictions, and that the ε-min predictions derived from our calibration were systematically larger than those derived from the Gillespie's calibration, mostly for intermediate MMD values. The bias between predictions and actual values from our calibration dataset was obviously close to zero for our calibration, while it was about   0. 006 for the Gillespie's one. As compared to the residual error we obtained with our calibration (RMSE value of 0. 0055, Table  2 ), the quadratic difference between actual values from our calibration dataset and predictions from the Gillespie's calibration was twice larger (RMSE of 0.
0095). We quantified the impact of differences in ε-min predictions by comparing the Gillespie's calibration for ASTER against our validation dataset. The comparisons of TES retrievals against prescribed values are displayed in Figs. 9 and 10. It is shown that lower estimates of εmin induced (1) lower estimates of waveband emissivity for all ASTER channels, with bias values ranging from 0. 007 to 0. 009, and (2) larger estimates of radiometric temperature, of about 0. 4 K. Resulting errors on waveband emissivity and radiometric temperature were almost twice larger than those obtained when validating our calibration. Indeed, RMSE ranged from 0. 011 to 0. 015 for emissivity and it was 0. 6 K for radiometric temperature with the Gillespie's calibration (Figs. 9 and 10), to be compared to RMSE ranging from 0.
Discussion The calibration/validation results for emissivity and temperature retrievals were good. This underlines the relevance of the TES empirical relationship, especially when (1) using emissivity spectra simulations that account for radiative transfer within the canopy with subsequent cavity effect and (2) considering a range of soil and plant conditions, from bare soil to full vegetation cover. However, the absolute RMSE values found in this study should be considered with caution for two reasons. First, we addressed the land surface level without any consideration of atmospheric or instrumental perturbations. Second, the calibration and validation datasets were not fully independent since they were derived from the same model, which partly explains the similar RMSE values we observed for calibration and validation.
The resulting differences on emissivity and radiometric temperature retrievals could reach up to 0. 025 (0. 015 on average, Fig. 9 ) and 2 K (0. 6 K on average, Fig. 10 ). The larger ε-min estimates obtained with our calibrations were due to the difference in the samples used to fittheε-min -MMD relationship. First, we excluded soil spectra corresponding to mineral samples that tended to shift downward the εmin -MMD relationship, as shown by  Schmugge et al. (1998). Second, our calibration dataset included more vegetation surfaces as compared to former studies, since we focused on plant canopies with LAI between 0 and 7. Third, introducing spectra with cavity effect tended to shift upward the ε-min -MMD relationship  (Anton and Ross, 1990; Chen et al. , 2004; Merlin and Chehbouni, 2004; Olioso, 1995).
This was confirmed when comparing the calibration from SAIL-Thermique simulations against that from soil and leaf spectra only (Fig. 11 ). Former calibration results were different from one study to another (Section 2). Differences were also observed in the current study that relied on a new type of spectral library, where the latter accounted for cavity effect while including a wide range of soil and plant conditions. Overall, the variability we observed on calibration results questioned the relevance and representativeness of any dataset to be used for calibration, and it underlined how the choice of the spectral library is a crucial step. An appropriate set up would require accounting for sample occurrences at the worldwide scale. The use of a radiative transfer model for the calibration of the ε-min -MMD relationship permitted to account for radiative transfer within vegetation canopy and subsequent cavity effect while considering a wide range of soil and plant conditions.
Concluding remarks The current study focused on evaluating the TES performances over various vegetation canopies, from bare soil to full vegetation cover. This research is based on the use of the SAIL radiative transfer model to account for cavity effect within vegetation canopy. As compared to previous studies based on the use of leaf and soil spectra only, differences occurred mainly for intermediate vegetation cover, with non-negligible consequences on the TES retrievals of emissivity and radiometric temperature. This raises the question of the representativeness of the calibration dataset, in relation to the considered type of land surface. To a lesser extent, the TES performances were in agreement with those reported in former studies: retrieval performances are better when considering more channels, the emissivity is better estimated for spectral intervals with flatter emissivity spectra, and it is less well estimated for large emissivity values because of the gray body problem.
The current study relied on a synthetic dataset. Although comparative results permitted to underline the impact of cavity effect for a large range of soil and plant conditions, it is necessary to confront these outcomes against ground-based measurements, given (1) measurement methods were recently proposed for reflectance spectra  (Neinavaz et al. , 2016a; Neinavaz et al. , 2016b; Rock et al. , 2016)  and (2) multispectral measurements have been conducted over large spectral bands  (Olioso et al. , 2014; Olioso et al. , 2007). Besides, the current study focused on land surface level, without any consideration of instrumental and atmospheric perturbations, whereas the latter can have significant consequences (e. g. ,  Hulley et al. , 2012b; Jacob et al. , 2004). In the context of forthcoming space missions such as HyspIRI  (Green et al.
Calculations and Statistical Analyses Data are reported as mean±standard error (SEM). Substrate oxidation was calculated using Ferrannini's equations. [36] The difference between the 2-h incremental area under the curve (iAUC) for glycaemia after ingestion of the two cereals products was assessed using the Student's t-test for paired data. Statistical analyses were conducted using Stata Software (v13 Stata Corp, College station, TX, USA). P values <0. 05 were considered to be significant. To compare the effects of the three test meals (Glucose solution, High-SDS biscuit and Low-SDS rusk) on the iAUC or parameter concentrations, analyses of variance for repeated measures were used, with post hoc comparisons using Bonferroni correction when appropriate. To compare the effects of the three test meals on kinetics, mixed models were used, with the subject as random effect; the test cereal product and time as fixed effects; and the interaction of breakfast product x time.
One specific aim of our study was to assess how starch digestibility modulates the postprandial inflammatory profile in the context of glycaemic excursions. We demonstrated that a high-SDS breakfast can decrease the postprandial glycaemic response and insulin demand in overweight subjects with metabolic risk. This result has been previously shown, mainly in metabolically healthy subjects,  [17, 20, 26, 27]  whereas others have reported contradictory results. [37] Another study showed that consumption of a HSDS product can lower the glycaemic response in insulin-resistant obese subjects. [24] ur data on postprandial inflammatory parameters are, overall, in agreement with previously published papers on CHO quality. The hs-CRP concentration was unchanged during the postprandial phase, whereas IL-6 and TNF-α concentrations increased slightly with no difference between the three test sessions.
Acknowledgements A. M. , S. V. , M. L. designed the study; S. L. P. , S. N. , E. B. , M. S. , J. -A. N. , H. R. performed the experiments and analysed the data; M. L supervised the study; S. L. P. , S. N. , A. M. , J. -A. N. wrote the manuscript. We are grateful to all of the research professionals at the  CRNH Rhône-Alpes. This study was sponsored by  Mondelez France R&D , which also prepared and provided the cereal products.
Phylogenetic Analysis of Fishes Phylogenetic analysis of five cyprinid species (grass carp, blunt snout bream, crucian carp, silver carp, and bighead carp) were constructed from two mitochondrial genes (COI and cytb) using MEGA program (version 6. 0)  (Tamura et al. , 2013). The corresponding gene sequences were downloaded from GenBank (accession numbers in Supplementary Table  S1 ). Common rabbits (Oryctolagus cuniculus) was used as the outgroup taxon. The concatenated sequences of the two mitochondrial genes were aligned using ClustalW in MEGA (pairwise and multiple alignment parameters: gap opening penalty 15, gap extension penalty 6. 66, delay divergent sequences 30%, DNA transition weight 0. 5, and no use of a negative matrix)  (Thompson et al. , 1994; Tamura et al. , 2013). The phylogenetic tree was constructed based on the aligned DNA sequences by the neighbor-joining method using Kimura 2-parameter model in MEGA (Test of phylogeny options: Bootstrap 999 replicates; Substitutions to include: Transitions + Transversions; Rates among sites: Uniform rates; Pattern among lineages: Same (Homogeneous); Gaps/Missing Data: Complete Deletion; Codon Positions: 1st + 2nd + 3rd + Non-coding)  (Kimura, 1980; Tamura et al.
Metabolite Extraction and Metabolite Profiling Analysis Extraction of metabolites from gut content samples was according to  Lisec et al. (2006)  with some modification. Gas chromatography was performed on a HP-5MS capillary column [5% phenyl/95% methylpolysiloxane (30 m × 250 µm i. d. , 0. 25 µm film thickness, Agilent J&W Scientific, Folsom, CA, USA)] to separate the derivatives at a constant flow of 1 mL/min helium. Raw gas chromatography/mass spectrometry data (GC/MS) data were converted into CDF format (NetCDF) files by Agilent GC/MS 5975. Data Analysis was processed by the XCMS 2. Detailed protocols for metabolite extraction and GC/MS analyses were provided in Supporting Information Methods.
Pyrosequencing Data Processing and Bioinformatics Analysis Quality filtering, denoising, and chimera checking of the sequences obtained from pyrosequencing were performed with the mothur software  (Schloss et al. , 2009) , as described previously  (Li et al. , 2015). The average read length was 250 bp. The resulting sequences were rarefied separately by random subsampling of 5,935 sequences from each sample in mothur, thereby equalizing the sampling effort across samples  (Schloss et al. , 2009). To predict the functional profiles of microbial communities, OTUs were picked using a closed reference (Greengenes ver. 13. 5) at 97% sequence similarity, with normalization to control for differences in 16S rRNA copy number among OTUs, and their metagenomic contributions were predicted using PICRUSt, from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways  (DeSantis et al.
The heatmap was constructed using the heatmap 2 function of the R gplots package  (Gentleman et al. , 2004). The machine learning algorithm Random Forests  (Breiman, 2001)  was used to identify the discriminatory OTUs and presumptive functions between species using the package R randomForest with 5000 trees and all default settings. The Boruta algorithm was used to select the features with predictive power  (Kursa and Rudnicki, 2010). In order to understand the relationship between some specific gut microbes and certain metabolites, redundancy analysis (RDA) was performed. All statistical analyses were performed using R version 3. 1. 0. All permutation tests (i. e. , ANOSIM and Mantel test) were conducted using 999 permutations.
In addition, OTUs of Bacteroides and Clostridium XVIII correlated positively with proline, serine, and threonine. Cetobacterium OTU1079 and OTU766 correlated positively with methionine and N,N-Di-(2-Hydroxyethyl)-methanamine, while OTU2177 in the same genus was positively correlated with phenylalanine. The presumptive functions of the gut microbial communities were also examined using PICRUSt. The fish gut samples had NSTI values of 0. 11 ± 0. 05. For comparison,  Langille et al. (2013)  found that Human Microbiome Project samples had the lowest (best) NSTI values (0. 03 ± 0. 2). Other mammalian guts had a higher mean NSTI value (0. 14 ± 0. 06), and diverse communities such as soil also had a much higher NSTI value (0. 17 ± 0. 02). Thus, the fish gut samples provide a reasonable data set to examine predictions from PICRUSt.
B. and N. S. ) for air-space opacity, atelectasis, nodules, bronchiectasis, mosaic attenuation, and lymphadenopathy. 10, 11 Available histopathology specimens (29 specimens from 11 patients) were reviewed by 2 hematopathologists (C. M. B. and J. R. G. ). Patients' most recent immunology results are described; postrituximab B-cell levels were excluded. ] [14] [15]  A poor pneumococcal polysaccharide vaccine (PPV) response was defined as a less than 4-fold increase in antipneumococcal IgG titer at 4 to 6 weeks after PPV vaccination. Significant associations in clinical complications were determined by odds ratios (ORs) with 95% CIs and Fisher exact tests by using GraphPad Prism software (version 6; GraphPad Software, La Jolla, Calif). P values of less than. 05 were considered significant.
by gain-T. C. is supported by the  National Children's Research Centre ,  Our Lady's Children's Hospital Crumlin, Dublin, Ireland. A. C. has a  Wellcome Trust Postdoctoral Training Fellowship for Clinicians  ( 103413/Z/13/Z ). K. O. is supported by funding from  BBSRC ,  MRC , the  Wellcome Trust , and  GlaxoSmithKline. R. D. and D. S. K. are funded by  National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge, United Kingdom. C. S. and S. E. are supported by the  German Federal Ministry of Education and Research  ( BMBF 01 EO 0803  grant to the  Center of Chronic immunodeficiency  and  BMBF 01GM1111B  grant to the  PID-NET initiative ). S. N. F is supported in part by the  Southampton UK NIHR Wellcome Trust Clinical Research Facility  and  NIHR Respiratory Biomedical Research Unit.
M. A. A. I. is funded by  NHS Innovation London and King's College Hospital Charitable Trust. A. F. , S. L. , A. D. , F. R. -L. and S. K. are supported by the  European Union 's  7th RTD Framework Programme  ( ERC  advanced grant PID-IMMUNE contract  249816 ) and a government grant managed by the  French Agence Nationale de la Recherche  as part of the  ''Investments for the Future'' program  ( ANR-10-IAHU-01 ). S. L. is supported by the  Agence Nationale de la Recherche (ANR)  ( ANR-14-CE14-0028-01 ), the  Foundation ARC pour la Recherche sur le Cancer (France) , the  Rare Diseases Foundation (France) , and the  Francois Aupetit Association (France). S. L. is a senior scientist and S. K is a researcher at the  Centre National de la Recherche Scientifique-CNRS (France). A. D. and S. K. are supported by the '' Institut National de la Sant e et de la Recherche M edicale.