dataesr/scientific-paragraphs-categorization huggingface dataset:
An a priori MCA was undertaken to evaluate sign-and-symptom associations at diagnosis of acromegaly (primary endpoint), using factors (e. g. , morphologic manifestations) and associated variables (e. g. , facial modifications, prognathism) derived from CRFs and patient questionnaires. Inconsistencies between CRFs and patient questionnaires were managed according to the scheme in Online Resource 3. Two types of variable were used: active variables were used to construct axes; supplementary variables were projected onto the dimensions of the original result to aid interpretation only. The a priori MCA (primary endpoint) was conducted with 38 active and 16 supplementary variables, and data from 319 patients. To investigate whether the quality of representation could be improved by increasing the number of patients with data and decreasing the number of variables, a post hoc MCA was subsequently conducted which omitted six factors (BMI, educational level, employment status, first person who suspected acromegaly, type of tumor, size of tumor), grouped manifestations into fewer variables overall (e.
Patient disposition and baseline characteristics An unexpectedly large proportion of the endocrinologists contacted responded (52/62) and then agreed to recruit at least one patient (37/62). As one physician did not recruit any patients, the total number of participating physicians was 36. Of the 16 physicians who responded but did not participate: five physicians indicated that they did not usually participate in a study; three stated a lack of interest; two stated a lack of time; three agreed to participate but either did not initiate a visit or recruit a patient; two withdrew from the study before sending the patient questionnaire; and data were missing for one physician. Participating and non-participating endocrinologists were representative of the different areas of France. In total, 648 patients were included in the study (global population), which was greater than the planned sample size of 550 patients.
Sign-and-symptom associations at diagnosis As a result of missing data, the a priori MCA included data from only 319 patients from the analysis population (n = 472). The first three axes of the a priori MCA explained only 19. 0% of the total inertia (Table  2 ) and did not allow the identification of characteristic sign-and-symptom associations at diagnosis. The primary endpoint was therefore considered to be negative and because of this, the a priori MCA maps are not reported here. A post hoc MCA was undertaken in an attempt to improve the quality of representation. In this MCA, the numbers of factors and the categories of individual variables were reduced, morphologic manifestations were removed, and data included from a greater number of patients (n = 405, analysis population). Despite these measures, the first three axes of the post hoc MCA explained only 25.
To mitigate such concerns, variables for which the observation was missing in >15% of patients were excluded. The a priori MCA was conducted with fewer than the ten patients per variable recommended by Everitt  [21]. While this may have made it more difficult to determine sign-and-symptom associations, associations were also not apparent in the post hoc MCA. The latter analysis included fewer factors, fewer categories for individual variables, omitted morphologic manifestations, and included data from a greater number of patients. Finally, ACRO-POLIS was conducted at sites in France and represents diagnosis of acromegaly in the French healthcare system. Despite these data from the ACRO-POLIS study generally resonate with, and expand upon, those from other published studies. There is concordance with respect to baseline characteristics, including the frequencies of microadenoma and macroadenoma,  [1, 14-16, 29, 30]  and the importance of endocrinologists and general practitioners in the diagnosis of acromegaly  [14] [15] [16].
Compliance with ethical standards Conflict of interest P. C. is a consultant and for  Ipsen ,  Novartis  and  Pfizer , and an advisory board member for Ipsen. T. B. has received institutional research support from  Novo-Nordisk ,  Sandoz  and  Pfizer , consultancy or lectureship fees from  Ipsen ,  Novartis ,  Strongbridge , and  Pfizer , and has served as investigator for clinical trials funded by  Novartis, Ipsen and Strongbridge. G. R. has received research grants from  Ipsen  and  Novartis ; has served as investigator (principal or coordinator) for clinical trials funded by  Novartis and Ipsen , and is a member of  Advisory Boards for Ipsen and Novartis. A. T. is a consultant for  Ipsen ,  Novartis , and  HRA Pharma  and has received lecture fees from  Novartis ,  HRA Pharma  and  Pfizer.
A. C. has no financial disclosures. B. D. has received unrestricted research grants from  Pfizer  and  Novartis , and has served as investigator for clinical trials funded by  Novartis ,  Ipsen  and  Pfizer. B. D. is a member of advisory board for  Ipsen ,  Novartis  and  Pfizer  and has given lectures for  Ipsen ,  Novartis  and  Pfizer. PPR has served as coinvestigator for clinical trials funded by  Pfizer  and  Ipsen. A. H. is an employee of Ipsen. F. E. is an employee of Ipsen. P. C. has received unrestricted research and educational grants from  Ipsen ,  Novartis , and  Pfizer as Head of the Department of Endocrinology and Reproductive Diseases, Hôpitaux Universitaires Paris-Sud. P. C. has served as investigator (principal or coordinator) for clinical trials funded by  Novartis ,  Pfizer ,  Ipsen ,  Italfarmaco , and  Antisense Therapeutics.
We applied two winter wheat varieties ((1) J9843 (human selected type being), Beijing Municipal Bureau of Agriculture, China, and (2) ZM175 (human selected type being), Institute of Crop Science, Chinese Academy of Agricultural Sciences, China), three water treatments (W0 is rainfall only, W1 is rainfall plus 100 mm, and W2 is rainfall plus 200 mm), and four nitrogen treatments (N0 is no fertilizer, N1 is 195 kg/ha, N2 is 390 kg/ha, and N3 is 780 kg/ha). Three hyperspectral and high-definition digital images of winter wheat field were acquired by using a snapshot hyperspectral sensor and a high-definition digital camera mounted on a UAV. Ground hyperspectral data were collected by using a near-surface spectrometer. During the ground-measurement campaign, winter wheat LAI, AGB, and canopy height were also measured for each plot.
Measurement of Field Hyperspectral Reflectance Ground winter wheat canopy reflectance was measured by using an ASD Field Spec 3 spectrometer (Analytical Spectral Devices, Boulder, CO, USA). ASD Field Spec 3 spectrometer has a full-range detection capacity (350 nm to 2500 nm, spectral resolution: 3 nm at 700 nm, 8. 5 nm at 1400 nm, 6. 5 nm at 2100 nm) and provides uniform visible near-infrared shortwave infrared data collection over the entire solar spectrum. Data was resampled to 1 nm spacing automatically. Measurements were collected during the jointing  (21 April 2015) , flagging  (26 April 2015) , and flowering (13 May 2015) stages of winter wheat growth. After the UAV flight (from 11. 00 h a. m. to 2. 00 h p. m. ), ground measurements were carried out under windless conditions and stable light levels.
Because the acquired panchromatic images are rich in texture information, and stitching is relatively simple; and the 50 × 50 (125 bands) hyperspectral cubes are characterized by rich spectral information, but lack texture information, hyperspectral image fusion was used to splice the UHD 185 hyperspectral images. The fusion steps are implemented in Cubert Cube-Pilot software (Cube-Pilot, Version 1. 4, Cubert GmbH, Ulm, Baden-Württemberg, Germany). After fusion, all hyperspectral images with 1000 × 1000 (125 bands) were stitched together using an image stitching process.
Radiometric Calibration Flights were conducted during the jointing  (21 April 2015) , flagging  (26 April 2015) , and flowering (13 May 2015) stages of winter wheat, with the UAV carrying a UHD 185 snapshot hyperspectral sensor (abbreviated as UAV-UHD) and a Sony DSC-QX100 high-definition digital camera (abbreviated as UAV-DC). The UAV-UHD and UAV-DC images were obtained at 50 m altitude and during stable light conditions, so atmospheric correction was not required. The UHD 185 and Sony DSC-QX100 exposure times were fixed depending on the intensity of sun light. The UHD 185 was calibrated on the ground before the UAV flight by using Cubert Cube-Pilot software (Version 1. 4) and a BaSO 4 whiteboard. The original DN values of the UAV-DC images were calibrated by imaging a black-and-white fabric placed on the ground and using DN i = DN original -DN black DN white -DN black × 255, (3) where DN i is the band names, such as R, G, B, DN original is the original DN value of the high-definition digital camera images; and DN white and DN black are the original DN values from the white-and-black fabric in the UAV-DC images (see Figure  1 ).
These steps are implemented in PhotoScan from Agisoft (Agisoft PhotoScan Professional Pro, Version 1. 1. 6, Agisoft LLC, 11 Degtyarniy per. , St. Petersburg, Russia, hereinafter referred to as PhotoScan). Executing these steps produces a DOM and the corresponding DSM of each flight. The CSM is generated by subtracting the digital elevation model (DEM) from DSM (DEM represents the variations of field soils under crops). The main steps to generate DEMs, CSMs, and UAV-based crop height from DSMs are as follows: (1) In total of 279 soil point coordinates were recorded by using the ARCGIS software (ARCGIS, Environmental Systems Research Institute, Inc. , Redlands, CA, USA) and DSM, and then using the ARCGIS extract tools and DSM to obtain the elevation of each soil point. (  2 ) The field DEM is obtained by interpolating the surface elevation between soil points by using the ARCGIS kriging tools.
In recent years, RF has been widely used in machine-learning research. It operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. RF has a higher accuracy, better tolerance to outliers and noise, and makes excellent use of the input data. Above-ground biomass and leaf area index estimation models (RF and PLSR) calibration and validation were done by using MATLAB software. The parameters of RF were optimized by using the method from Yue et al. [7]. The tree number was set to 800, and parameter mtry was set to 6 for the entire growth periods.
Statistical Analysis With three measurements, we acquired a total of 144 datasets. Two groups of data (Groups 2 and 3, in Figure  1 , used as the calibration set) were used to analyze the relationship between spectral, DSM, and combined information and AGB and LAI. Group 1 was used as the validation set. After data analysis, we used 96 sets of data to build the models and 48 setsdatas to validate the models. In this study, the correlation analysis was done by using the statistical software R 3. 5. 0 and calibration set. The coefficient of determination R 2 , root mean square error (RMSE), normalized root mean square error (nRMSE), and mean absolute error (MAE) were used to evaluate the performance of each model. Mathematically, a higher R 2 corresponds to a smaller RMSE, nRMSE and MAE, and thus represents better model accuracy.
Relationships between Vegetation Indices and Leaf Area Index and Above-Ground Biomass We used linear and nonlinear regression analysis to investigate VIs with Equations (  4 ) and  (5). The best VIs and equations (Table  4 ) were selected based on growing stages and remote-sensing data (G-VIs, DC-VIs, and UHD-VIs). Good relationships between VIs and LAI and AGB were obtained and the regression equations of LAI and AGB were built for winter wheat (see Table  4 ). The regression equations for LAI and AGB were validated using the calibration datasets, and the resulting R 2 , MAE, and RMSE are shown in Table  4  and in Figures  6a-c  and 7a-c. For the LAI estimated for three growth stages, the RMSEs range from 0. 88 to 1. 01, the MAEs range from 0. 67 to 0. 80, and R 2 ranges from 0. 46 to 0. 59.
The regression equations for LAI and AGB were validated using the calibration datasets, and the resulting R 2 , MAE, and RMSE are shown in Table  4  and in Figures  6a-c  and 7a-c. For the LAI estimated for three growth stages, the RMSEs range from 0. 88 to 1. 01, the MAEs range from 0. 67 to 0. 80, and R 2 ranges from 0. 46 to 0. 59. For AGB estimated over the three growth stages, the RMSEs range from 1. 71 to 2. 25 t/ha, the MAEs range from 1. 19 to 1. 88 t/ha, and R 2 ranges from 0. 26 to 0. 67. All G-VIs, DC-VIs, and UHD-VIs give good results for a single growing stage. However, the precision decreases when data from all three growing stages are used in the modeling. The results show that G-VIs are effective for estimating LAI and AGB in the jointing and flagging stages, but DC-VIs are more effective for estimating LAI and AGB in the flowering stage and over all three stages.
Relationships between (i) Crop Height and (ii) Leaf Area Index and Above-Ground Biomass Data for three single growing stages and all three growing stages were used to evaluate the relationships between crop height and LAI and AGB. We applied a linear and nonlinear regression analysis to investigate VIs by using Equations (  4 )-(  9 ). The results indicate a low correlation between crop height and LAI and AGB for single growing stages, especially for the jointing stage. A good correlation exists between crop height and AGB when using data from the three growing stages (see Figure  6a-c  shows the relationship between best VIs and AGB, and the associated validation accuracy of the three sensors. Figure  7a-c  shows the relationship between best VIs and LAI, and the associated validation accuracy of the three sensors.
Relationships between (i) Crop Height and (ii) Leaf Area Index and Above-Ground Biomass Data for three single growing stages and all three growing stages were used to evaluate the relationships between crop height and LAI and AGB. We applied a linear and nonlinear regression analysis to investigate VIs by using Equations (  4 )-  (9). The results indicate a low correlation between crop height and LAI and AGB for single growing stages, especially for the jointing stage. A good correlation exists between crop height and AGB when using data from the three growing stages (see Table  5  and Figure  6d-f ). However, because of low relativity, it is difficult to directly use crop height to estimate LAI when the data cover multiple growing periods (see Table  5  and Figure  7d-f ). The regression equations for LAI and AGB are validated by using the calibration datasets, and the resulting R 2 , MAE, and RMSE are shown in Table  5  and Figures  6  and 7.
81%). Table  A1  ranks the predictor variables according to their importance in AGB and LAI estimation. G-indicates data measured by a steel tape ruler; UHD-indicates data measured using the snapshot hyperspectral sensor mounted on the UAV; and DC-indicates data measured by using the digital camera mounted on the UAV. Table  8  shows AGB and LAI estimates based on crop height and VIs and using the RF and PLSR methods. The results indicate that the AGB estimate can be improved by using crop height, Vis, and the RF and PLSR methods. The best R 2 values are 0. 96 (RF: MAE = 0. 40, RMSE = 0. 57, nRMSE = 10. 47%) and 0. 81 (PLSR: MAE = 0. 83, RMSE = 1. 12, nRMSE = 20. 58%). However, the accuracy estimates of LAI based on crop height and VIs remains almost constant, with R 2 values of 0. 95 (RF: MAE = 0.
24, RMSE = 0. 31, nRMSE = 8. 22%) and 0. 65 (PLSR: MAE = 0. 56, RMSE = 0. 77, nRMSE = 20. 42%). We used the validation dataset to validate the crop height, VIs and PLSR and RF methods for estimating LAI and AGB. Figures  8  and 9  show the relationships (fitting formula: y = ax + b, R 2 , MAE, and RMSE) between the predicted and measured winter wheat AGB and LAI. The results show that the methods based on crop height and VIs provide better estimates of AGB than those based only on VIs (Figure  8 ). More importantly, all results based on crop height and VIs fit better (their slopes a are closer to unity; see Figure  8 ). As is the case for the LAI modeling dataset, the relationship in Figure  9  indicates that no notable difference exists between methods based on crop height and VIs and those based only on VIs.
The accuracy of the estimates of LAI and AGB is better when using RF and PLSR (Tables  7  and 8 ) than when using linear and exponential estimates (Table  6 ). These results are consistent with those of Refs. [46] [47] [48] , which suggest that estimation accuracy can be improved with PLSR and RF. The present results also prove that RF and PLSR can be used to combine multiple types of remote-sensing data (spectral and CSM data) to estimate AGB of winter wheat. Compared with validation results using modeling (Tables  7  and 8 ) and a calibration dataset (Figures  8  and 9 ), PLSR provides better stability than RF. Overfitting may influence RF estimates of AGB and LAI for a small number of samples. Therefore, we use PLSR to map AGB and LAI.
Conclusions This study compared ground spectrometer data with remote-sensing data from UAV-based snapshot hyperspectral sensors and high-definition digital cameras for estimating winter wheat LAI and AGB. Leaf area index and above-ground biomass were estimated by combining the spectral VIs and crop height (CSMs) via linear and exponential equations, RF, and PLSR. (i) We found that the correlation between the VIs × height (G-height × LCI, DC-height × r and UHD-height × LCI) and AGB is much greater than that when using a single VI or just the crop height. The results indicate that the combined use of VIs and crop height (CSMs) provides more accurate estimates of winter wheat AGB. (ii) The combined methods (VIs × height) to estimate AGB from UAV-mounted snapshot hyperspectral sensors and high-definition digital cameras provide an accuracy similar to that obtained when using a ground spectrometer to collect the data.
7804 × e 2. 3615x 0. 67 0. 87 1. 12 AGB DC-b y = 2762. 4 × e -17. 77x 0. 74 0. 86 1. 19 Flowering UHD-BGI y = 0. 1356 × e 6. 7115x 0. 67 0. 93 1. 18 G-SPVI y = 0. 5733 × e 3. 7940x 0. 72 0. 55 0. 66 LAI DC-b y = 39963 × e -28. 74x 0. 81 0. 47 0. 65 UHD-MCARI y = 39. 998 × e -210. 9x 0. 72 0. 51 0. 67 G-LCI y = 0. 6274 × e 2. 9275x 0. 26 1. 88 2. 25 AGB DC-r y = 0. 0033 × e 26. 549x 0. 67 1. 19 1. 71 Total for three stages UHD-LCI y = 0. 4263 × e 3. 7273x 0. 30 1. 84 2. 18 G-LCI y = 0. 4182 × e 3. 0703x 0. 59 0. 67 0. 88 LAI DC-B y = 15. 549 × e -0. 018x 0. 55 0. 70 0. 90 UHD-LCI y = 0. 4307 × e 3. 2404x 0. 46 0. 80 1. 01 Note: AGB: above-ground biomass; LAI: leaf area index; MAE: mean absolute error; RMSE: root mean square error; G-indicates data measured by a steel tape ruler; UHD-indicates data measured using the snapshot hyperspectral sensor mounted on the UAV; and DC-indicates data measured by using the digital camera mounted on the UAV.
1491 × x -1. 6028 0. 42 0. 96 1. 25 G-height y = 0. 1464 × x -2. 3713 0. 27 1. 3 1. 61 AGB DC-height y = 0. 1522 × x -1. 3578 0. 36 1. 26 1. 61 Flowering UHD-height y = 0. 1602 × x -0. 0044 0. 38 1. 21 1. 53 G-height y = 0. 081 × x -2. 5379 0. 22 0. 78 1. 00 LAI DC-height y = 0. 0872 × x -2. 1487 0. 33 0. 76 0. 97 UHD-height y = 0. 0905 × x -1. 3123 0. 33 0. 74 0. 94 G-height y = 1. 1613 × e 0. 0248x 0. 73 1. 08 1. 49 AGB DC-height y = 0. 1074 × x + 1. 2048 0. 73 1. 00 1. 29 Total for three stages UHD-height y = 0. 1736 × x -1. 1141 0. 71 1. 04 1. 39 G-height y = 0. 0009 × x + 3. 7193 0. 01 1. 01 1. 31 LAI DC-height y = -0. 0003 × x + 3. 783 0. 01 1. 01 1. 31 UHD-height y = 0. 0133 × x + 3. 2676 0. 02 1. 01 1. 30 Note: AGB: above-ground biomass; LAI: leaf area index; MAE: mean absolute error; RMSE: root mean square error; G-indicates data measured by a steel tape ruler; UHD-indicates data measured using the snapshot hyperspectral sensor mounted on the UAV; and DC-indicates data measured by using the digital camera mounted on the UAV.
PrP Sc immunohistochemistry PrP Sc was revealed using the SAF84 anti-PrP monoclonal antibody (0. 5 mg/ml) that recognizes the 161-170 sequence of human PrP (kindly provided by Dr. J. Grassi, CEA/SPI, Saclay, France). For epitope retrieval, tissue sections were incubated in formic acid (10 min) followed by autoclaving at 121 °C for 10 min. The secondary antibody was a biotinylated goat anti-mouse antibody (Amersham, France) (1:1000 in PBS/0. 1% Triton X-100). The avidin-peroxidase complex (Vectastain Elite kit, Vector Laboratories, Clinisciences) was then added, followed by DAB. Slides were scanned using a Nanozoomer Slide Scanner (Hamamatsu, platform MRI, INM Montpellier). The number (number/mm 2 ) of vacuoles or of LC3-II-positive cells and the surface area (µm 2 /mm 2 ) of astrocytes and neurons were determined using the ImageJ software.
EHEC Strains and Culture Conditions Escherichia coli O26:H11 strain 21765  (Galia et al. , 2015)  and E. coli O157:H7 strain EDL933  (Perna et al. , 1998)  were studied. Both strains were sequenced and genome sequence and annotation are available in international databases NCBI (RefSeq E. coli O157:H7 strain EDL933: NZ_CP008957. 1; RefSeq E. coli O26:H11 strain 21765 1  ; contig accession numbers CDLB01000001 to CDLB01000102). For the studies of EHEC colonization mouse intestinal tract, a spontaneous streptomycin-resistant (Str r ) mutant was derived from each of the clinical EHEC isolates (named 21765 SR225 and EDL933 SR225, respectively). Briefly, wild type strains were first revived from frozen stock by incubation 24 h at 37 • C in 9 mL brain heart infusion (BHI, bioMérieux, Marcy-l'Étoile, France).
Samples were incubated for 30 min at 4 • C in the dark. Five microliters of sample were mounted between slide and cover slip. Suspensions were examined using an inverted microscope (IX-71; Olympus, Rungis, France) equipped for epifluorescence observations. Use of a conventional fluorescein long-pass filter coupled with direct light allowed simultaneous visualization of dyed EHEC strains and MFGs. Milk samples were photographed with a DP70 camera using a DP Controller, and were imported into DP Manager software (Olympus) as TIFF or JPEG files. Micrographs were processed using AxioVision Release Version 4. 8  (Carl Zeiss, Oberkochen, Germany).
Analysis of Mouse Samples Real-Time PCR Correlation between cycles to threshold (Ct) apparitions of O-antigens (O26-antigen or O157-antigen) or 16S rDNA genes and EHEC or bacterial concentration was determined using fresh cultures (BHI, 37 • C) of O157:H7 strain EDL 933 SR225 and O26:H11 strain 21765 SR225 at end log phase (OD600 nm of approx. 0. 8; See Supplementary Figure  S4 ). Bacteria were counted by plating 10-fold serial dilutions in tryptone salt broth (bioMerieux) of fresh culture on PCA. Total gDNA was extracted using an EZ1 DNA Tissue Kit (Qiagen) and an EZ1 automatic DNA extractor in accordance with the manufacturer's recommendations. Calculation of equivalent cell number from real-time PCR data was obtained by amplifying serially diluted gDNA using a 10× dilution series to generate a standard curve according to the number of bacteria used for total gDNA extraction.
Image Analysis The position of each bacterium in the pictures was identified (x b , y b ) using the Zen Blue software (Zeiss). Bacteria labeled with FITC and DAPI and attached to the intestinal layer when intestinal villi were entirely observable were counted. The contour of the basal lamina of the intestinal section was extracted by hand (∼300 points). The contour was then interpolated linearly between each two consecutive points so that the distance between points is of the order of some pixels using a MATLAB script. The curvilinear abscissa, s, along the organ was then directly computed so that the position of a point "i" is given by: S i = i j=1 (x i -x i-1 ) 2 + (y i -y i-1 ) 2 , S 0 = 0 This gives the distance of each point of the organ from the recto-anal junction, s = 0, to the duodenum, s = L.