dataesr/scientific-paragraphs-categorization huggingface dataset:
Values are not significant (NS) or significant at * P < 0. 05, * * P < 0. 01, * * * P < 0. 001. c h 2 , broad-sense heritability. d Data combined for strains GMI1000, PSS366, and CMR134. Composite Interval Mapping indicated the presence of another resistance QTL on linkage group 14 (E05) (Table  5 ), which was effective against strains CFBP2957 and CFBP3059. For the CFBP2957 strain, a maximum LOD score of 16. 3 was observed at 3. 0 cM. For the CFBP3059 strain, a maximum LOD score of 27. 1 was observed at 8. 0 cM. The position of maximum LOD differed slightly according to the strain. However, as the confidence intervals overlapped, the QTL is assumed to be the same and was named EBWR14. This QTL was also detected in the analyses season per season, with a maximal LOD position FIGURE 3 | Graphs of LOD scores, and intervals of EBWR2, EBWR9, EBWR14 resistance QTLs to R.
LOD curves were obtained before refining the QTL position with refineqtl R command, whereas QTL intervals were obtained after the refineqtl command. FIGURE 4 | Phenotypic distribution of genotypes at three eggplant bacterial wilt resistance QTLs for the Waudpc variable. Allelic effects (allele "A" of susceptible parent P1 and "B" of resistant parent P2) are plotted with the "effectplot" module of R/qtl package at: -EBWR9 locus, combined data of GMI1000, PSS366, and CMR134 strains (A); -EBWR2 locus, strain PSS4 (B); strain TO10. Indonesia (C); strain TO10. Réunion (D); -EBWR2/EBWR14 loci, strain CFBP2957 (E); strain CFBP3059 (F). Means of Waudpc, standard errors (in brackets) and allelic effects ranking (ANOVA followed by LSD test, 95% confidence interval) are indicated. LSD test was not conducted for EBWR9 locus due to its group's imbalanced number of RILs.
ACKNOWLEDGMENTS The authors would like to thank  Jean-Michel Baptiste  and  Sylvain Lebon  ( CIRAD, La Réunion, France ) for their excellent technical work in the greenhouse trials. We also wish to thank  Thierry Jaunet  and  Samabhorn Bhuvitarkorn  ( HM. Clause Company ) for providing the phenotypic data with strain TO10 in Indonesia. We are grateful to  David Savio  for preparing the seeds for the RIL population. We thank  Edith Lallemand ,  Didier Fontaine ,  Martial Grondin ,  Véronique Ledoux , and  Emmanuelle Chapier  ( CIRAD, La Réunion, France ) for providing technical assistance in the microbiology experiments,  Sami Doganlar  and  Anne Frary  ( Izmir Institute of Technology, Urla, Turkey ) for their advice on the  GBS  project,  Frédéric Chiroleu  ( CIRAD, La Réunion, France ) for his help with statistical analyses and  Olivier Garsmeur  ( CIRAD, Montpellier, France ) for his help with the bioinformatics analysis.
FUNDING This research was financially supported by the  Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) , the  Ministry of Agriculture, Food, Fisheries, Rural Affairs and Spatial Planning  through the  "RESAUBER" project  ( CASDAR C-2013-09 ) and the  Agence Nationale de la Recherche et de la Technologie (ANRT)  through the  CIFRE PhD. (Grant No. 2013/1477  for SS). This work was also funded by the  European Regional Development Fund (ERDF) , by the  Conseil Régional de la Réunion  and by the  Conseil Général de la Réunion.
Cytotoxicity test. After a 72 h treatment, cell survival was measured by the (3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide) MTT assay (Sigma-Aldrich) as we previously performed  25. The combination index (CI) values were calculated with the Calcusyn software ® according to the Chou and Talalay theorem, which sets out the additive effects (CI = 1), synergisms (CI < 1) and antagonisms (CI > 1) in drug combination.
Western blot analysis. Cells were lysed in radioimmunoprecipitation assay buffer (RIPA, 50 mM pH 8. 0, NaCl 250 mM, Triton-X100 0. 1%) and western blot was performed as previously described  25. The primary antibodies used were directed against α -tubulin (clone DM1A, Sigma Aldrich, 1/1000), pAkt-S473, Akt-total (Cell Signaling, USA, 1/1000) or glyceraldehyde 3-phosphate deshydrogenase (GAPDH, Sigma Aldrich, 1/10000). Peroxydase-conjugated secondary antibodies (Jackson Immunoresearch, USA) and chemiluminescence detection kit (Millipore) were used for visualization. Signal was quantified with Image J ® software.
Flow cytometry. Flow cytometry analysis of cell cycle and apoptosis, in cells treated for 24 h or 48 h respec- tively, was performed as previously described  45. For autophagy analysis, cells were treated for 24 h, harvested and incubated for 30 min at 37 °C with 1:500 Cyto-ID ® autophagy detection reagents (Enzo Life Sciences, Enz- 51031-K200). Flow cytometry analysis was performed with BD FACSCalubur ™ (BD Biosciences) and data were analyzed in BD CellQuest ™ Pro software. Real-time metabolic analysis. Simultaneous multiparameter metabolic analysis of live cells was performed in the Seahorse XF24 ® extracellular flux analyzer (Seahorse Bioscience, USA). A549 cells were seeded in XF24 V7 multi-well plates (15,000 cells per well) for 5 h at 37 °C in 5% CO 2. Cells were then exposed to drugs for 24 h prior to the assay.
5 μ M). To normalize OCR and ECAR data to cell number, cells were fixed with glutaraldehyde 1%, stained with violet crystal 0. 1% in methanol 20% (Sigma-Aldrich), which was finally solubilized in DMSO to measure dye absorption with a microplate spectrophotometer (600 nm). Glucose consumption and lactate production assay. YSI 2950 ® (Life Sciences, USA) was used to measure the total flux of glucose and lactate. Cells were exposed to drugs 24 h after seeding. At each time point, medium was collected, centrifuged 5 min at 1,200 rpm and kept at -20 °C before measurement. Metabolite concentrations were normalized to cell number as described for real-time metabolic analysis. In vivo animal model. All the procedures and animal care complied with French Government official guidelines. In vivo experiments were approved (authorization number 0100903) by the Regional Committee for Ethics on Animal Experiments (Marseille number 14), and were conducted in the Scientific Centre for Functional Exploration (approval number F-13-055-5).
Statistical analysis. Each experiment was performed at least in triplicate. Data are presented as mean ± S. E. M. Statistical significance was tested using unpaired Student's t test. For experiment using multiple variables, statistical significance was assessed via two-way ANOVA. A significant difference between two conditions was recorded for *p < 0. 05; **p < 0. 01; ***p < 0. 001. Statistical analyses were performed with GraphPad 5. 0 statistical software.
Acknowledgements We would like to thank  Emilie Denicolaï  and  Nathalie Baeza  for their assistance with the Ki67 immunostaining analysis. The authors were supported by  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique, Aix-Marseille University , and  Assistance Publique des Hopitaux de Marseille. This work was also supported by grants from  Gefluc ,  Société Française de Pharmacie Oncologique (SFPO) , the  French National Academy of Pharmacy ,  Canceropole PACA, Institut National du Cancer (SIRIC Label INCa-DGOS-Inserm  # 6038 ) and by the  French Government (National Research Agency, ANR)  through the  'Investments for the Future' A*MIDEX (program reference # ANR-11-IDEX-0001-02 ). EP is funded by a  Marie Curie-Sklodowska Fellowship  from the  European Research Council  (grant agreement # 626794 ).
Patient population We analyzed data from 220 adult AS patients with normal flow (stroke volume index >35 mL/m 2 ) who underwent comprehensive Doppler-echocardiography and multidetector computed tomography without contrast within 3 months. We excluded patients with rheumatic AS, infective endocarditis, and moderate or severe aortic regurgitation. The study was approved by the Ethics Committee of the Quebec Heart and Lung Institute, Quebec, Canada, who granted us a waiver of consent.
Multidetector computed tomography To assess AS severity by aortic valve calcification (AVC),  14  multidetector computed tomography scans without contrast were performed using a 64-slices helical scanner (Somaton Definition; Siemens AG Medical Solution, Germany) with a tube potential at 120 kV and a tube current-time product at 60 to 80 mAs. Operators blinded to clinical and echocardiographic data performed all computed tomography examinations and analyses. The entire heart was assessed by 2. 4-to 3-mm-thick transverse slices with a pitch of 0. 15 to 0. 25 mm during end-inspiration breath-hold. Acquisition was triggered by ECG at 60% to 70% of the R-to-R-wave interval. Aortic valve calcification score was measured with the Agatston scoring method using commercially available and validated software (Aquarius iNtuition; TeraRecon Inc, San Mateo, CA) and all aortic valve calcification data were expressed in Agatston units (AU).
Discussion Since the first detection of the Q biotype in the Yunnan Province in 2003  19  , the Q biotype has subsequently been found and become the prevalent biotype in the Yangtze River Valley  19, [32] [33] [34] [35]  and on crops in most locations in China  21  , although the south and southeastern coastal areas of China are dominated by the B biotype  20. Our research also revealed that the B biotype was always the predominant biotype in Fujian during 2005-2014. In addition, the Q biotype was first detected in some areas in 2013 and widely detected throughout the province in   2. Data for 2005 and 2009 partially derived from He et al. 30  and Zheng et al. 31  , respectively. Maps were created by using the R statistical program version 2. 15. 2  65  with four add-on packages 'maps' 66 , 'maptools' 67 , 'ggplot2'  68  and GISTools  69.
The data for creating the base map of Fujian Province, China was obtained from the Global Administrative Areas (GADM) database (Version 2. 8, http://www. gadm. org/). 2014. Wang et al. 22  reported finding the Q biotype in Fuzhou and Zhangzhou cities on sampled cauliflower in October 2008. Although another record relevant to the detection of the Q biotype in Fujian occurs in GenBank (accession number FJ594434), we could not find any descriptions in the paper published by Teng et al. 36. Regardless, it appears that the Q biotype invaded Fujian, particularly in areas where the development of agriculture was advanced. However, population establishment of the Q biotype may have not been successful initially. We re-sampled whiteflies in the fields where Wang et al. 22  had in 2009, but we failed to detect the Q biotype using the specific mtCOI primers used in the present study (see section 2.
Maps were created by using the R statistical program version 2. 15. 265with four add-on packages 'maps' 66 , 'maptools' 67 , 'ggplot2'68and GISTools69. Location ID City District Town/Street Host plant Habitat type Collection date n  † 56 Longyan Shanghang Gutian Cucumber Protected area 2014/11/6 24 57 * Xinluo Hongfan Tomato Protected area 2014/9/2 24 58 Eggplant Protected area 2014/9/2 24 59 Nanping Shunchang Yuankeng Soybean Open field 2014/8/27 13 60 Yanping Xiadao Soybean Open field 2014/8/27 26 61 Xiqin Soybean Open field 2014/8/27 24 62 Ningde Pingnan Lingxia Broccoli Open field 2014/12/9 24 63 Shouning Da'an African daisy Open field 2014/7/31 26 64 Xiapu Songcheng Cabbage Open field 2014/10/31 24 65 Putian Licheng Huangshi Eggplant Open field 2014/12/15 24 66 Quanzhou Hui'an Luoyang Hami melon Protected area 2014/11/20 28 67 Licheng Fuqiao Brocoli Open field 2014/10/30 24 68 Sanming Jiangle Guangming Soybean Open field 2014/8/28 24 69 * Tomato Protected area 2014/8/28 40 70 Shaxian Qiujiang Tomato Protected area 2014/11/7 24 71 Yong'an Xiaotao Tomato Open field 2014/11/5 24 72 Youxi Chengguan Sweet potato Open field 2014/8/28 24 73 Xiamen Tong'an Datong Eggplant Open field 2014/12/9 36 74 Zhangzhou Longhai Dongyuan Cabbage Open field 2014/9/3 36 75 * Longhai Gangwei Cucumber Open field 2014/9/3 57 76 Zhangpu Sui'an Cherry tomato Open field 2014/12/12 16 77 Zhao'an Qiaodong Eggplant Open field 2014/10/31 24 78 Xitan Eggplant Open field 2014/10/31 24 79 Cucumber Open field 2014/11/10 24.
HARDWARE Figure  1  and Figure  2  show the IGN ultra-light photogrametric camera in which we implement our algorithm. It is based on a global shutter CMOS 20MP sensor (CMOSIS CMV20000) and a Zynq 7030 SoC/FPGA from Xilinx consisting on a two ARM cortex A9 CPUs running a Linux OS, and a programmable logic portion used originally to acquire image data from the sensor. It also has low-cost inertial sensors that will provide information about attitude of the camera during shootings. To make accurate 3D geometric measurements from images, this camera is This algorithm is implemented and executed in the camera. At first, it was developed on a desktop PC. The architecture of the algorithm is illustrated in Figure  3.
FEATURES MATCHING Using the internal orientation parameters produced by the optical calibration process and the 3D rotations between poses provided by IMU, we can compute the predicted position of homologous points of detected features (Figure  5 ). We consider this predicted position as the center of a search area in which the correct position should be found by ZNCC template matching with sub-pixel accuracy. This part achieves already in software a real-time performance given the fact that useless features are filtered out by the grid selector and that the size of the search areas is small (11 × 11 pixels) due to IMU prediction. We benefit from the presence of two cores on the camera to accelerate this part in software using the OpenMP library.
TEST SURVEYS We performed some experiments to quantify the accuracy and performance of our accelerated resampling method. We used the hardware previously described in "hardware" section. Several datasets have been acquired by the IGN camera. Each dataset consists in sequence of ten images acquired at the maximum frame rate of the CMOS (30 images/s). For this first test, the camera prototype we used was fitted with a Bayer sensor and has a resolution of 20 MP (5120 × 3840). Our work aims to be used for a narrow spectral imaging system consisting of several monochrome IGN cameras, thus we use the 5 MP (2560×1920) grayscale images obtained from the original pixel-stream by averaging each green pair of the Bayer pattern. The Copter 1B UAV from Wikhydro used for surveys is shown in Figure  11  Figure  11  (1): geometrical transformation using the 3D rotation and bilinear interpolation, (2): Accelerated geometrical transformation with bilinear function method and bilinear interpolation, (3): Accelerated geometrical transformation with bilinear function method and Nearest-neighbor interpolation All tests were run on two platforms: Firstly, a HPZ210 Workstation with an Intel Xeon 3.
30 GHz processor and 8. 0 GB of RAM. Secondly, the photogrametric IGN camera with 666 MHz dualcore ARM Cortex-A9 processors. The number of images is 10. Table  3  shows that for software versions, the factor gain in time is 5 between (1) and (  2 ) and 7 between (1) and (  3 ). This factor becomes 8 between the initial software version(1) and the hardware version. Benefiting from the presence of dual-core processors in the camera, we use the OpenMP library to accelerate the algorithm, the number of threads is set to 2, the time of processing of this part is reduced by half. Our method achieve a real-time performance in comparison to the time needed for writing an image in the SD-Card storage. The IP duplication is used to process in Figure  13  demonstrates visually that no motion blur affect the stacked image.
Performance of the prediction model The parameters for our model were estimated using data from 50 patients treated for metastatic thyroid cancer. For each patient throughout the treatment duration, empirical data for the concentration of stimulated Tg was available at each treatment cycle, prior to RAI administration. This data was used in the parameterization process for our model. The population and inter-individual variability values for the model parameters were of the Nonlinear Mixed Effect Models (NLME) type. The six parameters of the final model, ρ λ , , , , , a k T N e d 0 , were estimated using Monolix software (version 2016R1 Antony, France: Lixoft SAS, 2016). These parameters were assumed to be log-normally distributed amongst the individuals in the population, to insure biological relevant values.
Accessibility on tokamak The CEA IRFM is equipped with a Virtual Reality (VR) room and real time simulation software to check the assembly and the maintenance feasibility. The virtual scale-one mock-up helps verifying the integration of components and to avoid possible collisions. It   allows operators to practice the assembly as well. This process allows optimization of the accessibility without real platform and antenna. The human accessibility to the antenna as installed on the WEST tokamak was done with the XDE software and Unity3D with an Oculus Rift. Hereafter, as an example, we present the antenna accessibility study from the platform (Fig. 12 ). An avatar can be controlled in real-time by a human in the first person with contact feedback to validate accessibility of capacitor command (see  [12]  for more detail about the tools and the process).
Interferometric Bench The measurement of the shape and the deformation parameters of the MOEMS devices is made thanks to an interferometric characterization bench, fully developed in-house (hardware and software). Characterizations are done in static or dynamic modes, including measurements of optical surface quality at different scales, actuator stroke, maximum mirror deformation, and cut-off frequency; the latest parameter has not yet been measured in this study. This bench is a high-resolution and low-coherence Twyman-Green interferometer. It is a modular set-up around three main parts: illumination, interferometric cavity, and imaging system. The illumination is provided by a halogen lamp with an interference filter (typical example: λ0 = 650 nm, ∆λ = 10 nm). Fixing the temporal light coherence eliminates all parasitic fringe patterns induced by classical high coherence sources such as lasers.
Interferometric Bench The measurement of the shape and the deformation parameters of the MOEMS devices is made thanks to an interferometric characterization bench, fully developed in-house (hardware and software). Characterizations are done in static or dynamic modes, including measurements of optical surface quality at different scales, actuator stroke, maximum mirror deformation, and cut-off frequency; the latest parameter has not yet been measured in this study. This bench is a high-resolution and low-coherence Twyman-Green interferometer. It is a modular set-up around three main parts: illumination, interferometric cavity, and imaging system. The illumination is provided by a halogen lamp with an interference filter (typical example: λ 0 = 650 nm, ∆λ = 10 nm). Fixing the temporal light coherence eliminates all parasitic fringe patterns induced by classical high coherence sources such as lasers.
Cryogenic Experiment In front of our interferometric setup has been installed a cryo chamber for reaching temperatures as low as 100 K when not loaded, using a cryogenic generator, and vacuum pressure in the range of 10 -6 mbar (Figure  5a ). The chamber is equipped with an internal screen radiatively insulating the sample from the chamber. Temperature sensors and local heaters are used for controlling the environment, and connected to a custom built control electronics and control-command software. The component is monitored continuously thanks to a glass window at the entrance of the chamber. As we are using low coherence interferometry, the fringe contrast can be kept at maximum by balancing the optical path in the reference arm of the interferometer with a compensation plate identical to the chamber window.
The interferometric measurements are done with the LAM-developed software, in Matlab, and linked with the Matlab driver provided by Iris AO. The GUI is showing a view of the mirror with numbered segments, and the global Zernike coefficient as well as local (at segment level) piston/tip/tilt positions could be tuned for each segment. A calibration step has been done by Iris AO for measuring each actuator response (3 actuators/segment). Then, a "best flat" condition is calculated in order to minimize the residual wavefront error on the surface, and applied. Figure  7  is a screen shot made at the beginning of the experiment when the best flat condition is applied; at the left hand side is the interferometric image The device is driven by the Graphical User Interface (GUI) provided by Iris AO for the integration and pre-characterization phases.
The interferometric measurements are done with the LAM-developed software, in Matlab, and linked with the Matlab driver provided by Iris AO. The GUI is showing a view of the mirror with numbered segments, and the global Zernike coefficient as well as local (at segment level) piston/tip/tilt positions could be tuned for each segment. A calibration step has been done by Iris AO for measuring each actuator response (3 actuators/segment). Then, a "best flat" condition is calculated in order to minimize the residual wavefront error on the surface, and applied. Figure  7  is a screen shot made at the beginning of the experiment when the best flat condition is applied; at the left hand side is the interferometric image of the mirror with the "best flat" condition; at the right bottom is the view of the Iris AO GUI for driving the device.
010 0. 014 0. 018 0. 022 -6 -4 -2 0 2 ∆r max /R ln[-ln[F(∆r max /R))]] q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q (d) To precisely determine the parameters of the GEVD for each of our simulations, we use the package evd  (Stephenson 2002; Penalva et al. 2013)  publicly available for R (The R Project for Statistical Computing) 3. Independently, we confirm these parameters using a nonlinear least squares fit to the GEVD. Although in our fluid simulations the data is not strictly independent, as assumed by extreme value theory, we find that the GEVD provides an excellent fit. The shape, location, and scale parameters resulting from our fit are summarized in Table  2. In the table a value of κ = 0 is indicated when the best fit is of the Gumbel form. The value of the shape parameter κ is either small and negative, or zero for all of our simulations.
Acknowledgements. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework  ( FP7/2007-2013) / ERC  grant agreement no. 320478. I. B thanks MOCA at  Monash University  for warm hospitality. This work used the DiRAC Complexity system, operated by the  University of Leicester IT Services , which forms part of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment is funded by  BIS National E-Infrastructure capital  grant  ST/K000373/1  and  STFC DiRAC Operations  grant  ST/K0003259/1. DiRAC is part of the  National E-Infrastructure. This work also used the  University of Exeter Supercomputers , ZEN and ISCA, funded by the  STFC/DiRAC , the  Large Facilities Capital Fund of BIS  and the  University of Exeter.
13 (Cold Spring Harbor Laboratory, New York, NY, USA) (http://hannonlab. cshl. edu/fastx_toolkit/) software tools were applied to evaluate and improve the quality of the raw read sequence data, respectively. Subsequently, draft K. pneumoniae genomes were de novo assembled using Velvet version 1. 2. 10 (EMBL-EBI, Hinxton, UK) (https://www. ebi. ac. uk/~zerbino/velvet/) by taking advantage of the VelvetOptimiser script version 2. 2. 5. Obtained draft genome sizes were 5. 5 Mbp for Kp703 and Kp45 isolates, and 5. 7 Mbp for the Kp2948 isolate. A preliminary pan-genome overview on the coding sequence (CDS) homology and content was deduced using RAST version 2. 0 (Fellowship for Interpretation of Genomes, Burr Ridge, IL, USA) annotation (http://rast. nmpdr. org/) and subsequent sequence-based comparison, where proteins with sequence identity above 75% were considered as homologues.