dataesr/scientific-paragraphs-categorization huggingface dataset:
Compliance Rates and Data Quality From the 76 sites that responded to the initial survey, 20 (26%) offered structural imaging only, while 56 (74%) sites offered multimodal imaging in addition: diffusion, n ϭ 50 (66%); perfusion, n ϭ 49 (65%); spectroscopy, n ϭ 53 (70%); and all 3 MM imaging sequences, n ϭ 44 (48%). A total of 50/85 sites (59%) successfully recruited patients and acquired imaging data (5 sites withdrew and 4 sites subsequently joined the study after the survey). The compliance rates for structural and MM imaging for these sites can be seen in On-line Table  2. All 30 sites that had committed to return diffusion imaging did return it at least once for each patient (100%); of 28 that had committed to return perfusion imaging 23 did (82%), and of 31 that had committed to return spectroscopy 22 did (71%).
ACKNOWLEDGMENTS We thank the patients and parents who participated in the HERBY study and the staff at the study sites;  Dawn Saunders ,  Lee Coleman , and  Lisbeth Reneman  for their contributions as previous members of the CRRC; and  F. Hoffmann-La Roche Ltd, Australian Children's Cancer Trials , the  Innovative Therapies for Children with Cancer European Consortium , and the  European Society for Pediatric Oncology , for their support.
This study was funded by  F. Hoffmann-La Roche Ltd  (study number  BO25041 ; clinicaltrials. govNCT01390948). All research at  Great Ormond Street Hospital National Health Service Foundation Trust and University College London Great Ormond Street Institute of Child Health  is made possible by the  National Institute for Health Research Great Ormond Street Hospital Biomedical Research Centre. The views expressed are those of the authors and are not necessarily those of the National Health Service, the  National Institute for Health Research , or the  Department of Health.
Acknowledgements We would like to thank  Łukasz Mikulski  for his help in extending Example 8 to the general case. W. Jamroga ,  W. Penczek , and  M. Knapik  acknowledge the support of the  National Centre for Research and Development (NCBR) ,  Poland , and the  Luxembourg National Research Fund (FNR) , under the PolLux/FNR- INTER  project  VoteVerif  ( POLLUX-IV/1/2016 ). W. Jamroga is also affiliated with the  Interdisciplinary Centre for Security, Reliability, and Trust, SnT, University of Luxembourg. This work was also supported by the  CNRS/PAS  project  PARTIES  and the invited professorship programme of University Paris 13.
Design of primers and real-time qRT-PCR analysis. The oligonucleotide primer sets used for real-time qPCR analysis were designed on the basis of sunflower gene sequences (https://www. heliagene. org). All the genes used and the related heliagene accession number, amplified probe length and primer sequences are listed in S1 Table. ACO primers correspond to those used in Oracz, et al. 46. Two µg total RNA was reverse-transcribed and amplified, and the relative expression was calculated with three reference genes as described by Meimoun, et al. 47. Gene expression analyses were performed on seeds from 3 different lots. The distance matrix expression analysis was performed by sorting points into neighbourhoods (SPIN) algorithm by Euclidian distance  48. The PCA analysis was carried-out using Clustvis tool  49.
Aptasensor electrochemical characterization Electrochemical Impedance Spectroscopy (EIS) measurements were performed using as background electrolyte a PB containing ferri/ferrocyanide [Fe(CN)6] 4-/3-at 1 mM. The presence of this redox probe allows the detection of a current response much higher than using electrochemically inert solutions. EIS experiments were carried out at an applied potential of 0. 15V (vs. Ag/AgCl reference electrode), obtained from the equilibrium redox potential of [Fe(CN)6] 4-/3-, with a frequency range from 10kHz to 1Hz, a voltage amplitude of 5 mV and a sampling rate of 100 points. The EIS spectra were plotted in the form of complex plane diagrams (Nyquist plots, -Zimvs. Zre) and fitted to a theoretical curve corresponding to the equivalent circuit with FRA (Frequency Response Analyzer) software.
Electrochemical detection of TET The effect of TET concentration on the aptasensor signal was studied by cyclic voltammetry. In presence of TET a proportional decrease of both reduction and oxidation peaks was observed. However, due to the noise obtained for reduction peaks, it was decided to consider the variations related to the oxidation peaks (fig. 4-a ). The average intensity of these oxidation peaks for n=5 electrodes prepared in the same conditions was 45± 3µA. These reproducibility data refer to the mean value of 3 assays for each electrode, and inter-assays variations were lower that 0. 25 µA. As shown in fig. 4-b , the developed aptasensor exhibited a good linearity for TET concentrations ranging from 0. 05 µg/L to 20 µg/L. The detection limit, determined as the tetracycline concentration inducing a response three times higher than the blank variation, was 0.
Acknowledgements:Table 1 : 1 This work was partially founded through Innovec'EAU project (Interreg V SUDOE Programme, SOE1/P1/F0173, 2016-2019). M. Ahmad Alawad sincerely thanks Erasmus Mundus Peace 2 programme and Collège de France PAUSE programme (programme national d'aide à l'accueil en urgence des scientifiques en exil) for their invaluable contribution to financing his doctoral work. Analytical performances of aptasensors described in literature for the determination of TET (CV: Cyclic Voltammetry DPV: Differential Pulse Voltammetry, EIS: Electrochemical Impedance Spectroscopy, SWV: Square Wave Voltammetry, MB: Methylene Blue). (10 -9 M = 0. 444 µg/L) 10 -11 1. 1 10 -10 -4. 5 10 -8 This work.
More costly than range-free" localization algorithms Range-free localization algorithms  [7]  Localization is independent of the hardware devices and the distance computing Results are not as accurate as tracking error level Hierarchical routing protocols  [15]   [16]  More adaptable for conserving energy, convenient topology management, high-efficiency energy use, and simple data fusion Implementation complexity, database management problem, lack of structural Independence, implementation limitation Flat routing protocols  [14]  No effort is made to organize the network or its traffic, discovering the best route hop The network IDs have no subnet structure and cannot be summarized by hop by any path. Multi-hop routing protocols  [14]  Allows a clear minimizing of energy based on the local topology information, use of multi-hop communication mode, no complicated calculation in routing phase, routing mechanism is simple and efficient Less energy effifcient, less endtoend delay, higher packet loss, the node can not store large amounts of routing information Single hop plane routing protocols  [14]  Much more energy efficient then multi-hop routing  [3]  Best network parameters such as end-toend delay, lower packet loss.
As far as the transmission of information between sensor nodes is concerned, every cluster head and its next-hop cluster head find the optimization of the next-hop cluster head to ensure the minimum energy dissipation of data transmission to Sink node. By the way, a continuous selection next-hop cluster head is based on weight function, the data are transferred to the next-hop cluster head and subsequently, the last cluster head sends all data to sink node. In the first round of networks, the process of clustering is the same as the LEACH only if the threshold node is i > T (n), the node i becomes the cluster head. In the next 'round' of networks which is based on the energy strategy of electing cluster head compared to the state of cluster members energy finds the largest energy node i. Correspondingly, the energy dissipation model adopts wireless channel models in the reference  [14].
The Author of EMHR protocol  [32]  assumes that the distance between the node cluster-head i and the adjacent cluster head j is d(i,j), the distance between the adjacent cluster head j and the Sink node is d(j,S). The weighting function is set up as seen in the following equation (1). Where: S(i). E is the remaining energy of the cluster head i and S(i). max is the initial energy of the cluster-head i. Thereby, it means a continuous selection of the next-hop cluster-head based on this weighting function and the data is transmitted along the next-hop cluster-head. Once the minimum value of this function is selected for the next hop, the last cluster-head sends all data to the sink node. Considering only the distance between the CH and its next-hop cluster head by the weight function and even the residual energy of the cluster-head transmitter, this ignores other factors that affect the energy consumption and the network lifetime.
Subsequently, it saves the energy consumption of cluster head when Sink node is very far in WSN. Selecting another next-elected cluster head in data transmission has an important influence on the overall energy consumption of CH. The current CH and its next-elected cluster head are able to optimize the next elected cluster head selection in order to ensure a minimum energy dissipation of the data transmission to BS. Following the same manner, the next elected CH is selected based on this weighting function and the data are sent using the next elected CH. Each node has the lowest function is elected as CH that transmits all data to BS. The weighting function is described in the below equation 5. d(i, j ) is the distance between node CHi and adjacent CHj and d(j,S) is the distance between CHj and Sink node.
Simulation setup In this section, simulation experiments are implemented to study the accuracy of proposed mechanisme (localization algorithm and routing protocol) based on grid scan in Matlab. We set up and study the simulation results of the suggested algorithm. We therefore, the PERLA, and the MEVI algorithms with our proposals relying on the famous weighting function equation. Besides, we provide the simulation results procured under the following simulation conditions. The metrics for the performance evaluation are accuracy localization, energy consumption, number of transmitted packets, number of dead nodes and transmission time, as they constitute the most critical parameters in wireless sensor networks. Note that 100 sensor nodes are randmly placed in a square area with a size of 100x100 m.
Depending on spatial resolution and environmental conditions, such difference can reach 16 K over vegetation canopies  (Kimes and Kirchner, 1983)  and 12 K over urban areas  (Lagouarde et al. , 2013 (Lagouarde et al. , , 2010)) , which demands to be considered in most applications. However, until now, the TRD is ignored in operational LST estimation algorithms  (Sobrino and Romaguera, 2004; Wan and Dozier, 1996; Yu et al. , 2009). The presence of TRD effects seriously limits the inter-comparison of measurement datasets and LST products acquired at different view angles over the same scene at minimal observation time differences, but also the time series analysis from different viewing angle observations in the context of long-term monitoring, and the mapping over large areas with changes in viewing angles.
For inland water bodies (3. 56% of land surface, according to FROM-GLC dataset  (Gong et al. , 2013) ), barren lands (16. 51% of land surface)  (Garcia-Santos et al. , 2012; Sobrino and Cuenca, 1999) , and snow and ice (12. 81% of land surface)  (Cheng et al. , 2010b) , most of the models are trying to simulate the emissivity directionality, as this is the main source of directional effects on LST over relatively homogeneous surfaces. The structure of such surfaces is simpler when compared with those of vegetation and urban surfaces. The TRD attributed to emissivity is also a main concern for sea surfaces  (Masuda et al. , 1988; Wu and Smith, 1997). Although the proportion of urban landscapes is the smallest (0. 66% of land surface), many models were developed to simulate the urban surface DBT  (Fontanilles et al.
, 2015) , the ratio of off-nadir to nadir DBT (quantity 5) and the ratio of off-nadir to nadir DRT (quantity 6)  (Vinnikov et al. , 2012). In addition, the difference between off-nadir and nadir DBT/DRT (quantity 7/8) is usually defined as directional anisotropy to express the TRD effect. It is widely used in urban TRD modeling studies  (Lagouarde et al. , 2010). Another advantage of quantities 5-8 is that they can be used to describe the TRD pattern which can be further assumed steady within a time period. This will benefit to the multi-angle dataset construction. All quantities 1-8 have different values in different directions. In addition, six indicators were proposed to describe the TRD effect with a unique value for one scene, including the difference between maximum and minimum DBT (DBT max -DBT min ,  Cao et al.
TRD measurement and observation Multi-scale measurements and observations were performed over homogeneous and heterogeneous scenes, including a laboratory measurement of one sample, in-situ measurements of possibly mixed samples, airborne observations of a patchwork of different objects and satellite observations of a patchwork of different areas. The characterization and evidence of the TRD phenomenon have been originally reported from ground measurements  (Monteith and Szeicz, 1962; Sutherland and Bartholic, 1977). Later, the impact of TRD was studied in the laboratory by measuring temperature and emissivity properties of homogeneous samples  (Labed and Stoll, 1991; Snyder et al. , 1997). Usually, the directionality of homogeneous surfaces is attributable to the emissivity. Since 2000, equipment and facilities with more view angles and better efficiency have been developed to obtain in-situ multi angle datasets over heterogeneous vegetated landscapes  (Li et al.
, 2004). For TRD observations over urban canopy, a scientific protocol of airborne measurements was proposed to overcome the footprint limitation of ground measurement equipment  (Lagouarde et al. , 2004). Multi-angle datasets of vegetation and urban canopies were obtained with a trade-off between viewing angles and time-consumption of the experiments. ATSR series are space-borne sensors offering more variability in viewing angles (the same scene is sampled at 0°and 55°) in the TIR band, and are reliable data sources for studying the effects of TRD at the satellite scale, although caution must be taken concerning the small differences in the dual-view observation times. Recently,  Hu et al. (2016a)  presented a first satellite-based assessment of urban TRD (> 10 K) using ten years of the MODIS LST product.
To increase the automation and speed of measurement acquisition,  Yan et al. (2012)  developed a portable Multi-Angle Observation System (MAOS) which can provide the bi-directional reflectance factor (BRF) and DBT distributions concomitantly. As shown in Fig. 1b , it is composed by a spectroradiometer (FOV = 25°), a TIR radiometer (FOV = 28°), a video camera and a two-dimension automatic goniometer. The improved system is able to make > 13 zenith measurements in 6 min at an arbitrary azimuth direction. The circular trace average method observes different areas in different viewing directions which may reduce the quality of the measurement dataset  (Zhang et al. , 2000). Therefore,  Li et al. (2004)  designed an automated system to improve the accuracy of TIR measurements consisting of two identical thermal cameras and a metal ring to fix the view areas for different viewing directions (we call it the fixed target average method).
1c ). A tower crane was constructed for near-surface observations with height ranging from 1. 0 m to 26 m above the ground (the target size is 15 m × 20 m when the height is 15 m). The directional radiance of the TIR band can cover -1°to 53°using an oblique FLIR 655sc camera with a FOV equal to 54°× 68°and an oblique angle equal to 26°. The tower can move straight on the slideway and a turntable device changes the observation azimuth. The high frequency of the sensors is aimed at acquiring datasets with a high overlap rate to extract multi-angle values. It provides the spatio-temporal averaged result of different directions by segmenting the acquired images into small sectors according to the viewing angles (we call this the straight trace segmentation method). The information extraction method is similar to the circular trace segmentation method.
In this type of experiments, the target area must be large enough to be representative of the considered landscape. Then, the spatio-temporal averaged multi-angle DBT dataset can be obtained after sensor calibration, lens distortion correction, geometric correction, viewing angle retrieval, and atmospheric correction. Such experiments were also carried out over the cities of Marseille and Toulouse  (Lagouarde et al. , 2012 (Lagouarde et al. , , 2010 (Lagouarde et al. , , 2004) )  with observed maximum TRD effect of 7 K and 10 K, respectively. Voogt and Oke (1998) used a helicopter to assess quantitatively the magnitude of TRD in different districts of the city of Vancouver. The results stressed strong TRD effects in all urban zones with a maximum value of 9 K over the high-rise downtown area near midday.
, 2004 ) by a ray-tracing method. The outputs are reflectance, radiance, radiative temperatures, directional images at any latitude in the atmosphere, and also the urban 3D radiative budget. Kanda et al. built  an outdoor micro-scale model (named COSMO) of an urban canopy with 32 × 16 blocks within 100 × 50 m 2 in Japan  (Kanda et al. , 2007). Morrison et al. (2018)  proposed a method to extract the component brightness temperature from near surface TIR camera observations over COSMO and simulate the directional anisotropy by DART. They found that the maximum difference between offnadir and nadir directions can be up to 6. 18 K at 13:00. Recently, DART was coupled with energy models for mapping the anthropogenic fluxes of Basel, London and Heraklion  (Chrysoulakis et al. , 2018). For that, DART combined with urban geometric database and BOA irradiance to obtain urban radiative budget fluxes from time series of Landsat-8 images at 100 m spatial resolution.
, 2011 (Hénon et al. , , 2007)) , the DARTEB model  (Gastellu-Etchegorry, 2008 ) and the TEB model  (Masson, 2000). The TUF-3D model was also used to evaluate the urban emissivity model based on sky view factor  (Yang et al. , 2016 (Yang et al. , , 2015)). Within these models, the radiation module, the conduction module and the convection module are considered with different degrees of complexity. Most of them use the canyon street model with flat roof while some models use the regular cubic block model  (Krayenhoff and Voogt, 2007). Grimmond et al. (Grimmond, 2010)  performed a comparison of 33 widely used urban energy budget models using a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada.
Parametric models Parametric models for urban canopies have been increasingly considered in recent years. At first, they aimed to simulate directly the TIR signal by kernel driven models developed for a vegetation landscape. For instance, the RL model was used to describe the urban TRD effect over an urban surface  (Duffour et al. , 2016b; Lagouarde and Irvine, 2008). Results show that the R 2 of RL model can be up to 0. 80 for airborne multi angle dataset collected over the city of Toulouse  (Lagouarde et al. , 2010). The VIN2012 model  (Vinnikov et al. , 2012)  was also used to fit the limited observations over urban canopy  (Jiang et al. , 2018)  in order to study the estimation of complete temperature of urban surface  (Adderley et al. , 2015; Voogt and Oke, 1997 ) by different DRTs. Wang et al.
proposed two new parametric models. One for a sparse urban canopy without mutual shadowing  (Wang et al. , 2018b)  and one for a dense urban canopy with mutual shadowing effect  (Wang et al. , 2018a)  respectively. The GUTA-sparse model was validated with airborne multi angle dataset over the city of Marseille  (Lagouarde et al. , 2004). The RMSEs obtained for four flights were between 0. 40 and 0. 56 K while the anisotropy amplitudes at the four flight times all exceeded 8 K. The GUTA-osg model was evaluated based on the anisotropy of airborne measurements of temperature over the city of Toulouse  (Lagouarde et al. , 2010) , which gives RMSEs < 0. 8 K and R 2 > 0. 85. To describe the urban surface emissivity anisotropy,  Sun et al. (2015)  developed a parametric model (USEA) with three kernels referenced to the VIN2012 kernel model.
DART, MGP and SVAT models were used to account for the TRD effect in LST product inter-comparison or validation  (Ermida et al. , 2014; Guillevic et al. , 2013 Guillevic et al. , , 2012)) , with difficulties induced by model requirements of many input parameters. Fortunately, parametric TRD models can normalize the angular effect for existing LST products, including correction to the nadir direction and hemispherical direction. Ren et al. (2014)  corrected the estimated DRT to the nadir direction for WiDAS airborne multi angle dataset by using the ROSS-LI model. They found that the temperature difference between the normalized nadir temperature and the minimum temperature of the observing directions was 0. 5-2. 0 K for most vegetated pixels and always several Kelvin for most non-vegetated pixels.
, 2014b). As already mentioned, physical TRD models and parametric models can be used to perform angular normalization. However, detailed structural information and component temperature distribution are difficult to obtain, which restricts the implementation of physical models. On the other hand, the lack of quasi-synchronous multi-angle datasets at the satellite scale limits the implementation of parametric TRD models. In order to decrease this constraint, multi-observations at different times in the same location  (Ermida et al. , 2017)  and multipixels of different location with the same land cover type  (Liu et al. , 2018)  are combined to form an equation system of multi-angle observations that can be used to estimate the kernel coefficients. Until now, only the GOES and SEVIRI disks are normalized by parametric models.
SULR estimation considering the TRD effect SULR is an essential component of Earth surface radiation balance. The meteorological, hydrological, and agricultural research communities require an accuracy of 5-10 W/m 2 for SULR retrieved from satellite data. However, the RMSE of existing products (e. g. CERES-FSW, ISCCP-FD and GEWEX-SRB) ranges from 19. 92 to 33. 6 W/m 2  (Cheng and Liang, 2016). The methods for estimating SULR can be divided into three main categories: 1) the physical temperature emissivity algorithm  (Tang and Li, 2008; Wang et al. , 2005)  with inputs of broadband emissivity, radiometric temperatures and downward longwave radiation (DLR); 2) the hybrid method  (Cheng and Liang, 2016; Jiao et al. , 2015; Wang et al. , 2009) , with a relationship between clear-sky TOA radiance and SULR built using an artificial neural network model or a multivariate linear model based on an extensive simulated database.
The separation between LST and LSE is avoided in order to improve the accuracy; 3) hemispherical integration methods with consideration of the Earth surface TRD effect  (Hu et al. , 2017 (Hu et al. , , 2016b)). The importance of the TRD effect of SULR estimate over vegetation and urban canopies had been reported long before  (Otterman et al. , 1997; Sugawara and Takamura, 2006). Cheng and Liang (2014)  provided error ranges of surface longwave net radiation incurred by ignoring TRD of non-vegetated surfaces  (water, snow, sand, soil and minerals). These works led to the pioneering development of considering the TRD effect in SULR estimation by  Hu et al. (2016b). In this work, the 4SAIL model was used to simulate the multi angle DBT and also SULR dataset coupling with the MODTRAN atmospheric radiation transfer model as a support.