dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluences are converted to H*(10) using the conversion coefficients from the publication 74 of ICRP  [2]  (paragraph 2. 1). As the software offers to the user the possibility to define any energy distribution (input file), simulations were also made (calculations in air without barrier) using the energy steps of the ICRP 74 A. 21 table. For I-131, the calculated value for H*(10) was 2. 38E-04 Sv/h (± 0. 7%). The significant increase of the H*(10) value (+ 7%) using the ICRP 74 energy steps instead of the default option for the energy distribution (see table  3 ) can explain the difference observed for the gamma dose constant calculated with RayXpert ® and MCNPX, if this effect is added to the small impact of the ray selection rule (~ 2-3%). Note that the simulations with MCNPX were made using the ICRP 74 energy steps for the conversion coefficients (calculation of H*(10) with tally F4).
The RayXpert ® selection rule for the emission photon rays does not impact significantly the calculated ambient dose equivalent rate if it is applied in MCNPX. Finally, biasing methods proposed in RayXpert ® were checked (TTB approximation and energy-cutoff). They reduce significantly the calculation time and/or the statistical error, while having an impact on the results lower than 10%, which is acceptable for radiation protection purposes. This work should now be pursued along three major lines. Firstly, the calculations should be extended to other radionuclides encountered in medical facilities. Then the efficiency of RayXpert should be tested for routine use and realistic configurations found in brachytherapy and nuclear medicine facilities. Finally, the possibility to use this software for radiology facilities should be studied.
Identification of 31 JNK target genes To further identify bona fide JNK target genes, we performed in situ hybridization on en-GAL4/UAS-hep act embryos, in which the JNK pathway is activated in a striped pattern, as seen with the puc control (Fig  1B ). Because of the great number of identified genes, we first made a selection of genes based on their potential localization and function (membrane, secreted, motors, etc. ) and on available expression data from public databases (such as FlyExpress). In addition, comparison of our embryonic data set with Drosophila Schneider-2 cells induced by the lipopolysaccharide JNK activator, allowed the extraction of a list of genes having a common transcriptional response to JNK activity  [28, 29]. In total, we selected and tested 194 genes by in situ hybridization and identified 31 bona fide JNK target genes (Fig 1B ; S4 Table ).
It can therefore be considered as a general HOX target gene, i. e. regulated by most Hox paralogs, as previously defined  [40]. Another example of a general target is the Drosophila gene optix, which is activated by the head HOX genes labial and Deformed (Dfd) and inhibited by the trunk HOX genes  [51]. Nonetheless the general HOX target genes do not represent the majority. A genomic analysis in the Drosophila embryo identified more than 1500 genes regulated by at least one of the six HOX paralogs tested (Dfd, Scr, Antp, Ubx, abd-A, Abd-B)  [52]. Only 1. 3% of these genes are regulated by the six paralogs and 1. 5% by the five paralogs that we used in our study. Interestingly more than 40% of the ~1500 HOX target genes are also present in the JNK genomic data set that we obtained. This strong overlap well reflects the fact that the LE runs along most of the body AP axis encompassing the thorax and abdomen.
85%), evaluating the number of false positive to 14 out of the 1648 genes identified. The FDR associated with the LOF comparison is 3. 19%, i. e. corresponding to 3 or 4 false positive out of the 113 isolated genes. The enrichment scores were obtained using the DAVID Bioinformatics Resources  [27]. Comparison of microarray data sets was performed with THEA  [29]. Microarray data have been deposited in the Gene Expression Omnibus (accession number GSE21805).
Therefore d NS was divided in 10 regions and the parasegmental boundary was set between the 7 th and 8 th cells. CurvedPeriodicity calculates the average signal from each cell of each segment, and also the AP ratio per segment. Taking advantage of the normalization, the software is able to pool the confocal images per condition, synthetizing the data and extracting the mean and s. d. for d LE and d NS , and to export it directly into an Excel file. In case of negative numbers (i. e. expression below background), the expression level was set to 0. For the AP analysis, the mean of the signals from each segment from T1 to A7 (n embryos; n = 15 to 20) was calculated. For the compartmental analysis, the signals for a given cell of all the segments (n segments = 150 to 200, with 10 segments per embryo) were averaged.
Acknowledgments We wish to thank  A. Brand ,  F. Maschat ,  J. P. Vincent ,  E. Sanchez-Herrero ,  Y. Graba ,  S. Merabet ,  G. Zimmermann , the  Bloomington Drosophila Stock Center (BDSC ;  NIH P40OD018537 ), the  Vienna Drosophila Resource Center (VDRC) , the  Drosophila Genomics Resource Center (DGRC)  and the  Developmental Studies Hybridoma Bank (DSHB)  for fly stocks and reagents. We also thank  P. Spe  ´der,  M. Gettings ,  C. Thomas ,  N. Haupaix  for help in in situ hybridization screening. We acknowledge the imagery platform PRISM of our institute for their assistance. We thank  S. Merabet  and members of the lab for insightful discussion.
Work in SN's laboratory is supported by the  "Centre National de la Recherche Scientifique" , the  "Agence Nationale de la Recherche " (program  ANR-11-BSV2-0022 -JNKDro ) and LABEX  SIGNALIFE  [# ANR-11-LABX-0028-01 ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Abstract. This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March).
The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R 2 = 0. 74, RMSE = 0. 009 m 3 m -3 ) when the wheat is smaller than one wavelength (∼ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases.
With the SNR GNSS-IR technique, the interference between the direct and the reflected signals is observed through temporal variations of the SNR data  (Bilich and Larson, 2007; Zavorotny et al. , 2010; Chew et al. , 2014). Changes in geophysical or biophysical parameters affect the phase, amplitude and frequency of the SNR modulation pattern. The SNR is also influenced by surface roughness, and by the position of the antenna with respect to the surface and to the satellite  (Larson and Nievinski, 2013; Chew et al. , 2016). The SNR modulation primarily depends on the following: the relative height of the GNSS antenna above the reflecting surface (ground or vegetation surface), satellite elevation, the superposition of the direct signal and of the reflected signal, which varies along with changes in the satellite track positions, the right hand circular polarization (RHCP) and left hand circular polarization (LHCP) gain pattern of the receiving antenna, (RHCP usually increases the SNR when the satellite elevation angle increases, LHCP is related to imperfections of the antenna and is greater than RHCP for the reflected signal), reflection coefficients for the reflecting surface, related to the water content and to the ground mineralogical content of the reflecting surface, surface topography and roughness and the satellite transmitted power.
Roussel et al. (2016)  integrated both GPS and GLONASS SNR data to retrieve soil moisture over bare soil. Using data from a field study,  Wan et al. (2015)  showed that the amplitude of the SNR data presented a good linear relationship with the vegetation water content (VWC), but it was restricted to VWC values of less than ∼ 1 kg m -2. In addition to the amplitude of the SNR data, it was also possible to infer VWC by the MP1 rms index, which is a linear combination of L1 and L2 carrier phase data and L1 pseudorange data  (Small et al. , 2010) , and by the NMRI (Normalized Microwave Reflection Index) which is derived from the MP1 rms  (Small et al. , 2014; Larson and Small, 2014). In this study, the SNR GNSS-IR technique was used to analyze GNSS SNR data obtained with a single classical geodetic antenna receiver over an intensively cultivated wheat field in southwestern France.
The data were used to retrieve either soil moisture or relative vegetation height during the growing period of the wheat crop. The method proposed by  Chew et al. (2016)  (hereafter referred to as CH16) was used to retrieve soil moisture. Moreover, we performed a wavelet analysis in order to extract the dominant period of the SNR. We investigated to what extent vegetation height influenced the dominant period resulting from the wavelet analysis. The main justification for investigating the impact of vegetation height was that it impacted the relative antenna height (the distance from the antenna to the reflecting surface). Vegetation growth tended to decrease the relative antenna height and broke up the constant height assumption used in soil moisture retrieval algorithms. In this context, key objectives of this study were to (1) assess the soil moisture retrieval technique in either short or tall vegetation conditions and (2) retrieve vegetation height along the wheat growth cycle.
SNR data and preprocessing The GNSS SNR data were acquired from an antenna at 2. 51 m above the soil surface over an experimental field covered by rainfed winter wheat in Lamasquère, France (43 • 29 10 N, 1 • 13 57 E; see Fig. S1  in the Supplement). These GNSS data were collected by GET (Géosciences Environnement Toulouse) for a whole growing season, from January to July 2015. A Leica GR25 receiver equipped with an AS10 antenna was used and data were acquired at a sample frequency of 1 Hz. Only the S1C SNR signal strength on the civilian L1 C/A channel of the GPS constellation was used in this study because the used receiver could not track the L2C signal. The latter is only transmitted by the recent Block IIR-M ("Replenishment Modernized") and IIF ("Follow-on") GPS satellites. Vey et al.
After fission or fusion reactor shutdown the activated structure emits decay photons. For maintenance operations the radiation dose map must be established in the reactor building. Several calculation schemes have been developed to calculate the shutdown dose rate. These schemes are widely developed in fusion application and more precisely for the ITER tokamak. This paper presents the rigorous-two-steps scheme implemented at CEA. It is based on the TRIPOLI-4 ® Monte Carlo code and the inventory code MENDEL. The ITER shutdown dose rate benchmark has been carried out, results are in a good agreement with the other participant.
MENDEL MENDEL is a depletion code developed at CEA dedicated to radioactivity calculations related to neutron irradiation and based on deterministic depletion solvers. MENDEL is the successor of the DARWIN/PEPIN2 code  [17]. MENDEL is developed in C++ with an objectoriented architecture allowing other codes to be easily coupled. For example, the depletion solvers of MENDEL are currently used in the deterministic transport code APOLLO3 ®  [18]  and in the Monte Carlo transport code TRIPOLI-4 ® , in order to perform depletion calculations. Recently, a new capability has been added to handle the propagation of the nuclear data uncertainties by a probabilistic approach  [8]  based on URANIE  [19] , the CEA uncertainty and sensitivity platform.
Conclusions A shutdown dose rate calculation scheme was implemented in the TRIPOLI-4 ® code. It is based on the rigorous-two-step methodology (first step: neutron transport + depletion calculation, second step: decay gamma transport). The MENDEL inventory code was used for depletion calculation. This paper presents a numerical benchmark (ITER SDR benchmark) to validate the TRIPOLI-4 ® R2S. Results are in good agreement with the other participant (UNED). Improvement of the R2S scheme are planned: to accelerate the calculation (MENDEL parallelization), to propagate the statistical error of the first step and to handle different materials in a mesh-cell. Extended validation exercises will be carried out with the Frascati Neutron source Generator SDR experiment  [11]  and the JET SDR measurement  [23].
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. TRIPOLI ® is a registered trademark of CEA. We gratefully acknowledge EDF for their long term partnership and AREVA for their support. The authors warmly thanks the researcher of  UNED  ( J. P. Catalan ,  J. Sanz ,  P. Sauvan ) for their support on the ITER SDR benchmark and  E. Dumonteil  who started the implementation of the R2S scheme in TRIPOLI-4 ®.
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique that allows the non-invasive study of brain function. It is based on the hemodynamic variations induced by changes in cerebral synaptic activity following sensory or cognitive stimulation. The measured signal depends on the variation of blood oxygenation level (BOLD signal) which is related to brain activity: a decrease in deoxyhemoglobin concentration induces an increase in BOLD signal. The BOLD signal is delayed with respect to changes in synaptic activity, which can be modeled as a convolution with the Hemodynamic Response Function (HRF) whose exact form is unknown and fluctuates with various parameters such as age, brain region or physiological conditions. In this paper we present PyHRF, a software to analyze fMRI data using a Joint Detection-Estimation (JDE) approach.
Oxygenated and deoxygenated blood has different magnetic properties. As a result, the above causes a BOLD signal increase. Thus, the BOLD signal is an indirect measure of cerebral activity based on physiological changes in oxygen consumption, cerebral blood flow and cerebral blood volume. Fig. 1 : fMRI BOLD signal  [OLKT90]. The BOLD signal measures the local changes in blood oxygenation ocurring during brain activity. fMRI data is acquired by repeated imaging of the brain while the subject or patient executes a task or receives a sensory stimulus during repeated epochs separated by periods of rest. This data is analyzed by correlating the measured time-varying BOLD signal in each image location with a predicted BOLD signal, obtained by convolving the known function representing the stimulus with a Hemodynamic Response Function (HRF) modeling the delay in the vascular response.
For instance, the vascular response giving rise to the BOLD signal is altered in normal ageing [FGM + 14] C o n f e r e n c e R e a d y Most used open source libraries for the analysis of fMRI data (e. g. , SPM 1 , FSL 2 ) consider the HRF as constant in all the brain and the same for all subjects. However, several works (see  [BVC13]  for a survey) show that the HRF changes across different regions of the brain and across individuals, increasing thus the possibility of obtaining false negatives and decreasing the reliability of the results. The software PyHRF [VBR + 14] was developed to overcome the above limitation by analyzing fMRI data using a Joint Detection-Estimation (JDE) approach. In the JDE approach, the detection of the cortical activation is achieved together with the estimation of the unknown HRF response by analyzing non-smoothed data.
This detection-estimation is calculated on different parcels of interest paving the cerebral volume. Therefore, PyHRF allows to navigate throughout the brain and to focus on regions of interest during the experiment in order to visualize the activations and their temporal behavior through the estimated HRF. Over the last years, efforts have been made in terms of image processing, user-friendliness and usability of the PyHRF package to make it more easy to use by non experts and clinicians. Next, we present the PyHRF package. Then we illustrate its use on real fMRI data. Finally, we conclude by discussing directions of current/future work. An online Jupyter notebook containing the results presented here can be found at http://www. pyhrf. org/scipy2017_notebook.
PyHRF PyHRF (http://www. pyhrf. org) is an open source tool implemented in Python that allows to jointly detect activation and estimate (JDE) the hemodynamic response function (HRF) [MIV + 08], which gives the temporal changes in the BOLD effect induced by brain activity. This estimation is not easy in a voxelwise manner [PJG + 03], and a spatial structure was added to JDE  [VRC10]  in order to get reliable estimates. In this regard, HRF estimation in JDE is parcel-wise and an input parcellation is required. However, the use of the Markov Chain Monte Carlo 1. SPM official website: http://www. fil. ion. ucl. ac. uk/spm/software/ 2. FSL official website: https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/ (MCMC) method for estimation added a huge computational load to the solution, leading to the development of a faster method to deal with the parameter estimation.
Thus, a Variational Expectation Maximization (VEM) solution [CVF + 13] was implemented. JDE aims at improving activation detection by capturing the correct hemodynamics, since using the wrong HRF function could hide existing activations. The use of a canonical HRF is usually sufficient for activation detection. However, HRF functions have been found to have different shapes in different regions  [HOM04] , and to have different delays in specific populations  [BVC13]. They change depending on pathologies such as stenosis. Fig. 2  shows some HRF functions estimated by PyHRF from BOLD data on a healthy adult. This data was acquired in a block-design setting with visual, auditory and motor experimental conditions. The parcels correspond to regions of the brain that are known induce evoked activity in response to these experimental conditions.
4  we show the SPM and PPM maps for a visual experimental condition in the same data used for Fig. 2. We used the package Nilearn (http://nilearn. github. io) to generate the brain maps presented in this document. In Fig. 3  we present the inputs and the outputs of PyHRF for the analysis of BOLD data. It needs as inputs the data volume (BOLD), the experimental paradigm, and a parcellation of the brain. After running the JDE algorithm, the output will consist of HRF functions per parcel, BOLD effect maps per experimental condition, and PPMs per condition. In the next section, we will describe in more details these elements and how to use PyHRF.
Getting fMRI BOLD Data First of all, we need to get our fMRI BOLD data. In our running example, we will analyze the dataset used in [GSB + 13]. This dataset (  ds000114 ) is open shared and it can be downloaded from https://openfmri. org/dataset/ds000114/. For convenience, we implemented the method get_from_openfmri that uses the library fetchopenfmri (https://github. com/wiheto/ fetchopenfmri) to download datasets from the site openfmri. For instance, the following code downloads the dataset ds000114 to the folder ~/data. Briefly, in this dataset ten healthy subjects in their fifties were scanned twice using an identical experimental paradigm. This paradigm consists of five task-related fMRI time series: finger, foot and lip movement; overt verb generation; covert verb generation; overt word repetition; and landmark tasks.
fMRI BOLD Preprocessing Once we have the BOLD volumes, we need to apply some transformations to the images in order to correct for possible errors that may have occurred along the acquisition. For instance, a BOLD volume (e. g. , a whole brain) is usually not built at once but using a series of successively measured 2D slices. Each slice takes some time to acquire, so slices are observed at different time points, leading to suboptimal statistical analysis. We used the library Nipype (https://github. com/nipy/nipype) to define and apply our preprocessing pipeline. This library allows to use robust tools, such as SPM and FSL, in an easy manner. The proposed workflow (see Fig. 6 ) starts by uncompressing the images since they are in a nii. gz format (gunzip_func and gunzip_anat nodes). After, it applies a slice timing operation in order to make appear that all voxels of the BOLD volume have been acquired at the same time.
We then apply a realignment in order to correct for head movements. Moreover, we apply a coregistration operation in order to have the anatomical image (high spatial resolution) in the same space as the BOLD images. Finally, we normalize our images in order to transform them into a standard space (a template). The pipeline described above was run for the images of all subjects from the dataset (i. e. , 10 subjects) on multiple processors, since Nipype uses the library joblib (https://github. com/ joblib/joblib). We used the acquisition parameters presented in [GSB + 13] to parameterize each preprocessing operation. For instance, the number of slices for the volume, the time for acquiring all slices (TR), and the order in which they were acquired (e. g. , interleaved). In the following snippet, we show a portion of the code to define the slice timing task with Nipype.
So far, we have prepared our functional and structural images for BOLD analysis. It is important to note that PyHRF receives nonsmoothed images as input, thus we excluded this operation from our preprocessing pipeline. For the sake of simplicity, in our running example we only analyze the 4th subject from our dataset. Additionally, we use the package Nilearn to load and visualize neuroimaging volumes. Fig. 7  shows the mean of the functional images of the 4th subject after preprocessing. Fig. 7 : Mean of all preprocessed functional images (over time) of the 4th subject of the dataset ds000114. As we explained before, the JDE framework estimates HRF parcel-wise. This means that PyHRF needs a parcellation mask to perform the estimation-detection. The package provides a Willard atlas [RAM + 15] (see Fig.