dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction and real-time quantitative PCR analysis Total RNA was extracted from seedlings using the RNeasy MiniPrep kit (Qiagen) according to the manufacturer's instructions. First-strand cDNA was synthesized from 2 mg of total RNA using Improm-II reverse transcriptase (A3802, Promega) according to the manufacturer's instructions. 1/25th of the synthesized cDNA was mixed with 100 nM solution of each primer and LightCycler Õ 480 Sybr Green I master mix (Roche Applied Science) for quantitative PCR analysis. Products were amplified and fluorescent signals acquired with a LightCycler Õ 480 detection system. The specificity of amplification products was determined by melting curves. AtPTF2 was used as internal control for signal normalization. Exor4 relative quantification software (Roche Applied Science) automatically calculates relative expression level of the selected genes with algorithms based on ÁÁCt method.
Histochemical b-glucuronidase assays After 15-min fixation in 80% cold acetone, complete seedlings were stained in Eppendorf tube, and b-glucuronidase (GUS) activity was assayed using 5 mM ferri/ferrocyanide as described  (19). After 2 h at 37 C, samples were washed in 70% ethanol for 10 min, and then cleared using chloral hydrate solution (8 g of chloralhydrate, 1 ml of glycerol and 2 ml of water). Images were captured on a microscope Axioskop (Zeiss) with a camera Spot RT slider (Diagnostic instrument) and enhanced using Adobe Photoshop software.
Accession numbers The GenBank accession numbers of the genes and proteins discussed in this article are MIPS1: At4g39800; ATXR5: At5g09790; ATXR6: At5g24330; DCL1: At1g01040; MPK4: At4g01370; and DDM1: At5g66750. Lines used in this study were T-DNA lines SALK_023626 (mips1-1) and Flag_605F08 (mips1-2) for mips1, SALK_130607 and SAlL_0004263 for atrx5 and atrx6 and EMS line for ddm1  (24).
ACKNOWLEDGEMENTS The authors are grateful to  Herve´Vaucheret (INRA, Versailles)  for providing the ddm1 mutant and to  Scott D. Michaels  ( Department of Biology, Indiana University ) for the atrx5,6 double mutant and for ATXR6 expression vector. The authors thank  Gilles Sante  for taking excellent care of our plants and  Se´verine Domenichini  for her expert help in microscopy.
FUNDING  Centre National de la Recherche Scientifique; UniversiteṔ aris Sud ;  Agence National de la Recherche  ( MAPK-IPS ANR-2010-BLAN-1613-02 );  Program Saclay Plant Sciences (SPS ,  ANR-10-LABX-40 ). Funding for open access charge:  Universite´Paris Sud ,  CNRS  and  ANR.
Introduction Le monitorage de la profondeur de l'anesthésie reste un défi pour les anesthésistes. L'adoption, de plus en plus répandue du monitorage fondé sur l'analyse de l'électroencéphalogramme (EEG) cortical, encouragé par plusieurs recommandations émanant des sociétés savantes nationales, dont la Société francaise d'anesthésie et de réanimation  [1] , a permis l'accumulation d'une expérience clinique et la création de bases de données qui incluent de nombreux patients. Avec l'expérience clinique accumulée autour du monitorage de l'EEG cortical, les cliniciens se sont posés la question du bénéfice clinique de la surveillance de la profondeur de l'anesthésie en routine. Les domaines d'intérêt ont été : • la prévention du surdosage anesthésique estimé par la diminution des doses cumulées d'hypnotiques ; • la prévention du sous-dosage anesthésique, estimé par la diminution de l'incidence de la mémorisation explicite per-anesthésique ; • l'analyse de la relation entre le surdosage anesthésique (estimé par des valeurs basses de d'indice bispectral  [BIS]  et la mortalité à distance).
Quelques données chiffrées Cette étude, a analysé des données recueillies de manière prospective, dans une base de données électroniques, créée à partir des dossiers informatisés d'anesthésie, dans un seul centre, la Cleveland Clinic. L'hypothèse de travail était la suite logique des travaux qui ont retrouvé une association statistique entre une anesthésie « trop profonde » (valeurs basses de BIS et de pression artérielle moyenne) et un risque accru de mortalité à distance. L'étude triple low ajoute un troisième paramètre : les concentrations alvéolaires minimum « basses » d'halogénés, ajustées, de manière imparfaite, sur la présence (mais pas les concentrations en temps réel) de protoxyde d'azote, la présence de fentanyl (dont les concentrations au site effet ont été prédites en tant que variable moyennée sur la durée de la procédure, à partir des doses administrées en bolus par les praticiens) et celles de propofol utilisées pour l'induction  [2].
It can be used for other biomass sources but, in this case, will probably have to be modified slightly or completed. The main objective of this reading grid is to help to identify the main points of each study and of its results in order to determine the advantages and/ or limitations and decide those that are comparable. This type of grid can also be used to produce the specifications for a new biomass resource study. Lastly, it can be used to structure a metadatabase holding the studies conduced in France or Europe on this subject.
Multiple and Heterogeneous Mobilised Data The biomass availability calculation mainly depends on the available data and the means used to process them. Analysis of forest resource studies is a striking example due to the number of years these studies have been conducted (20 years), and even longer if we consider the forest management plans that use the same calculation principle consisting in planning the forest harvest over the next 20 to 30 years in order to optimise distribution over this period. Forest inventories were first available as paper publications, which made analysis considerably more difficult since data had to be entered on computer, and which also limited the volume of data available. Development of national inventory databases provided access to more detailed and complete data while improving tree compartmentalisation as well as the total availability and technico-economic calculations.
It is in fact limited to that collected more or less regularly and exhaustively by the Bureau Commun des Pailles (Straw & silage office), created on the initiative of the cereal industry and backed by agricultural entrepreneurs and representatives of the sector. Similarly, unlike grain harvests whose yields are estimated every year by the Statistique Agricole (SSP (2) ) for each department, no data is available for the yield in straw or dedicated crops, forcing the authors of studies to imagine indirect evaluation methods, thereby increasing the uncertainty on the results. To determine the resource evaluation methodology, the statistical quantitative databases required for the models have therefore been listed and described, based on a descriptive grid composed of the following ten criteria: -identification of the producer of the data considered; -owner of the data; -subject of the data with a brief description; -geographic area covered by the data; -spatial resolution level of the data; -type of data referring to how it was obtained (e.
Statistical Data Mobilised for the Agricultural Model Two databases are absolutely essential to calculate the resource: -crop rotations for France are obtained from the ASP (3) available under certain conditions at the scales of the department (NUTS) and the canton. These data, taken from the CAP ( 4 ) declarations, are likely to be less accurate due to the simplification of the declarations (split between agricultural subsidies and the type of crops grown) and their access is likely to be restricted in the medium term for data protection reasons; -data on the grain yield of the major crops most widely found in France are communicated by agricultural statistics (Statistique Agricole -AGRESTE). They are used in particular to estimate the biomass yields (straw, new energy crops) by conversion factors determined on the basis of experimental results.
Balance While prospective analyses are far from being absent from "biomass" resource and procurement studies, they are faced with numerous supply and demand assumptions and therefore require a certain degree of simplification in order to remain understandable. The three types of prospective study each meet a particular requirement for a cost/satisfaction ratio acceptable. The first approach could satisfy the company and the second the public authority which wants to check its renewable energy policy framework. The approach adopted in VALERBIO  [16] , certainly the most elaborated and the most promising, is also the most difficult to implement and involves a significant amount of data on the sources as well as on the costs of various sectors. The development of databases storing this type of parameter could answer the questions, more directly operational, raised by decision-makers.
CONCLUSION The supply of biomass to the bioenergy sectors is a key but complex issue. Evaluation of quantities, an essential starting point to investigate the other aspects, is now reaching a level of maturity where the successive estimations made are tending to converge and can act as reference. This is the case in particular of forest resource estimations, which date back for many years. Agricultural resource estimations are making significant progress, although they have not yet reached a comparable degree of maturity. We must nevertheless: -make sure that these estimations remain transparent in order to compare them, create metadatabases to store the studies and acquire databases of results to build available biomass repositories; -reconcile the national evaluations with the European studies often conducted by third-party country organisations which do not have the same diversity of crops, practices and biomass types as France; -continue methodological research studies and pooling of approaches.
The purpose of this contribution is to inspect the first year of IGS daily coordinate time series for spectral features that could confirm the role of subdaily EOP tidal errors, clarify the origin of GPS draconitic harmonics, and reveal other indications of tide mismodeling or analysis errors. Not only is this relevant for the fullest geophysical interpretation of Global Navigation Satellite System (GNSS) results, but understanding short-period technique errors is a precondition for the development of sensitive GNSSbased systems to detect sudden natural hazards, such as earthquakes and tsunamis [e. g. ,  Blewitt et al. , 2009]. While the IGS products have previously relied solely or primarily on GPS data, by early 2011, four ACs began to include significant volumes of GLONASS data also. The combined data usage is referred to as GNSS.
The second harmonic should be at 4. 0 days and the third at 2. 67 days. There are faint indications of both harmonics in the GNSS AC dU spectra. The underlying error source could be any effect that gives rise to geometrically repeating orbit errors, such as due to a strongly nonuniform distribution of GLONASS tracking stations. Indeed, major gaps in the network coverage occur over most of the Pacific Ocean, North Africa and the Mideast, most of the U. S. , and western South America; concentrations of GLONASS stations occur in Europe, Canada, and Australia. [12] Two IGS ACs, one GNSS, and the other GPS only, were not included in the Figure  2  groupings. GRG (GPS + GLONASS) spectra are shown in Figure  S1  of the supporting information. They do not show evidence of the 8. 0 days peak, which is probably because of the fact that they weight GLONASS data much more weakly than GPS, but there is a very pronounced peak at about 3.
66 days in all three components. There is no matching feature in their orbit results compared to the IGS combination, so it is evidently not satellite linked. However, a connection to tide model errors seems remote. It might not be coincidental that this period is very close to half of the GRG processing week (3. 625 days), which consists of seven overlapping arcs of 30 h each or 174 h. That could happen if GRG has a subtle station constraint or coding bug related to their weekly processing batches. Curiously, there is no matching peak at 7. 25 days. In any case, this strong peak is unique to some aspect of the GRG data analysis and not a general GNSS feature. [13] JPL (GPS only) was also not included in the Figure  2  groups for reasons explained in section 2 (see supporting information).
A broad excess of power centered roughly at 5. 5 days stands out, most prominently for dN and dU. This feature was previously noticed by Amiri-Simkooei [2013] who claimed to see "many small peaks indicating no clear, sharp, and unique peak for. Normalized periodograms for the 183 daily JPL stations that are at least 80% complete for the period 1998. 0 through 2012. 0 have been computed after annual and semiannual fits have been removed for each. The periodograms were then stacked and averaged, and the results shown were multiplied by 3 for dE and by 9 for dU. these signals" and concluded them likely to be quasiperiodic and perhaps station dependent. We suggest an alternative explanation. Recalling that JPL processes global network GPS data in 30 h overlapping arcs, their tidal aliases will differ from most other IGS ACs that use 24 h data sets.
Because the JPL orbit modeling is quite different from all other ACs except EMR (both ACs use the same software system, but EMR data arcs are 24 h), it is possible that the propagation of tidal errors is also different and does not affect station heights much. On the other hand, the much lower resolution EMR daily spectra (not shown) match other IGS ACs, not JPL, in having distinct dU fortnightly power but no 5. 5 days band. In any case, JPL and GRG both show signs of unique spurious effects probably derived from aspects of their data processing. [20] The indications of 8 days errors for those ACs (COD, ESA, GFZ) that include GLONASS data is interesting. While aliasing of subdaily EOP tide errors as the underlying source is a possibility, that should be less effective than for GPS because the shorter orbital period means less efficient coupling via orbital parameters.
A quantitative simulation is needed similar to that by  Griffiths and Ray [2013]. Meanwhile, GLONASS orbit errors are definitely larger than for GPS in IGS products by about a factor of 3, based on internal AC agreement, and geographical correlation is likely. So this is our preferred hypothesis. [21] In the context of GLONASS-related errors, we observed large PM rate discrepancies in early 2013 for the same GNSS ACs with the 8 days coordinate residual peak. In a private intra-IGS email exchange (17 February 2013), G. Gendt (a member of the GFZ AC team) showed results with and without GLONASS that demonstrated a GLONASS data connection. T. Springer (a member of the ESA AC team) confirmed (email, 18 February 2013) the same result in their independent with and without GLONASS solutions. While the mechanism for the GLONASS degradation of the EOP estimates remains unknown, a relationship with the coordinate errors should be kept in mind.
The meshes are obtained from the FreeFrem Ó software, which allows to define anisotropic heterogeneous metrics. Figure  10  plots isolines of the overpressure w T h À w 0 h at the final time of the simulation. The two methods yield the same results for fine meshes (8 760 triangles for DG, 16 446 for DDFV). Concerning the convergence of the solution as h tends to 0, SWIP needs further refinement than DDFV when isotropic meshes are used. Therefore, DDFV is less sensitive than SWIP to the choice of the mesh when an anisotropic permeability is considered. Figure  11  and Figure  12  present results on mass conservation. Multiplying Equations (  6 ) and (  7 ) by the water density q, and summing over the time intervals in ½0; ndt leads to: qðV n À V 0 Þ ÁM n ¼ X n i¼1 qdtU i N M i in þ X n i¼1 qdtU i D M i out þ X n i¼1 qe i E i ÁM n is the total mass variation over the time interval ½0; ndt and the quantities P n i¼1 M i in , P n i¼1 M i out and P n i¼1 jE i j are respectively the total water inflow, the total water outflow and the total mass balance defect cumulated at time ndt.
Strong Interval Trees Our interest in trees counted by leaves is spawned by strong interval trees. They are in a size preserving bijection with permutations. This particular representation of permutations is a very effective data structure for algorithms in reconstruction of genome evolution scenarios, as we briefly mentioned in Section 1. Our analysis builds subclasses that are in fact each a simple variety of trees, and hence are very well understood, particularly given the generic analysis we have completed in Section 2.
Acknowledgements We are indebted to  Cedric Chauve  for his guidance and access to the mammalian genome data set, prepared by  Bradley Jones  and  Rosemary McCloskey. Furthermore ,  Ms. McCloskey  wrote the code for the Boltzmann generator, amongst other extremely useful things. We thank  Carine Pivoteau  for demonstrating her interest in our project at several stages, and for her careful proofreading. We also thank the referees for their usefull suggestions, and for pointing out reference [8] to our attention.
In the quest for an alternative way of recovering information that is partially recorded in the GIXD images due to technical problems during the experiment or less acquisition time than the optimum, we turn to image processing theories. In image processing, finding an efficient and compact representation of the data under consideration is of major importance in several distinct tasks, such as, compression, denoising and restoration, to name a few. In the quest for a suitable transform, sparsity of the representation was recognized as a key requirement in seeking simplifying operations  [5, 6]. Specifically, the design of over-complete redundant representations is now at the core of many stateof-the-art algorithms used in image compression  [7] , denoising  [8] , deconvolution  [9]  and restoration  [10].
For instance, it has been shown that MCA can be used to separate the texture from the piecewise smooth component of a given image, by noticing that the former is characterized well using local cosine functions, while the latter may be well represented using curvelets  [27]. The MCA algorithm has been employed successfully for the analysis of data in several areas, such as, video analysis [28], astrophysics  [29]  and medical imaging [30,31], while it was extended recently in the framework of "image inpainting" [32], enabling the treatment of problems where parts of the image are missing or corrupted. Motivated by the success of MCA in signal and image processing, the purpose of this study is to exploit the effectiveness of modern over-complete transforms and extend the applicability of MCA to the analysis of 2-D GIXD data.
To the best of our knowledge, this is the first study that bridges the gap between the analysis of diffraction data and state-ofthe-art image processing techniques. More specifically, first we show that the geometrical structures, which are present in GIXD images for the specific type of thin films considered in this study, can be represented efficiently by means of a combination of over-complete transforms, namely, the UDWT and the curvelet transform, resulting in a simple and compact description of their inherent structures. Then, we tackle the important problem of recovering the missing information in GIXD images, e. g. , due to potential malfunction of the detectors, by applying MCA in an inpainting framework. The experimental evaluation using high-resolution GIXD images shows that the proposed approach is highly efficient in recovering missing information in the form of either randomly burned pixels or whole burned rows, even at the order of 50% of the total number of pixels (which is anyway an extreme practical scenario).
This missing information may inhibit the post-processing of GIXD images and data evaluation. Data analysis is mainly based on reduction of the 2-D images into 1-D intensity patterns. This is done either by integrating the diffracted intensity (throughout the whole image or on specific sectors) or by plotting the intensity across the meridian and/or the horizon. In case of missing information due to burned pixels, or inaccurate statistics related to a small number of measurements due to reduced acquisition times, incomplete intensity 1-D plots will be derived. This entails the danger of allowing for data misinterpretation and derivation of false scientific outcomes, which are highly sensitive to the form of the intensity patterns. The added value of our approach stems, thus, from its application in healing potential problems related to detector performance during image recording.
In order to analyze the GIXD data several "linecuts" are performed. Usually, the 2-D images are radially averaged around the centre of the primary beam, which gives the 1-D plot of the intensity as a function of q. In a second approach, linecuts across the meridian and/or the horizon are extracted, which provide the intensity plots across the out-of-plane (q z ) and in-plane (q xy ) directions, respectively (Fig. 1b ). Finally, azimuthal scans can be performed around a q-range of special interest, herein at around q = 0. 37 Å-1 (the highest intensity ring apparent in the test images shown in Fig. 2 ). In this case, the diffracted intensity is plotted as a function of the polar angle χ, which is defined with respect to the normal to the substrate (Fig. 1b ). The GIXD images presented herein were collected from polythiophene-based films.
The difficulty in addressing (  2 ) is that this optimization problem is highly non-smooth and non-convex, while it has been also proved to be NP-hard in terms of computational complexity  [33]. On the other hand, the relaxation of (  2 ) by replacing the 0 -pseudo-norm with the 1 -norm reduces to a linear program, and hence it can be solved in polynomial time: min c∈R L c 1 subject to X = T -1 {c} (3) where the 1 -norm of c is given by c 1 = L i=1 |c i |. Moreover, it was shown that under certain conditions, the solutions of (  2 ) and (  3 ) are identical  [34, 35]. Several pursuit algorithms with empirical success have been proposed for the solution of (3), among them, the greedy Matching Pursuit (MP)  [36] , the Basis Pursuit (BP)  [37]  and their variants  [43]. Nevertheless, in applications involving large data sets, such as, the high-resolution GIXD images we deal with, MP or BP algorithms are computationally intense.
In contrast, MCA solves a minimization problem in terms of the morphologically distinct components directly, whose dimension (1) Despite the fact that 0 is a pseudo-norm, the improper term "norm" is often used, too. is less than or equal to the dimension of the corresponding transformed version. The following sections introduce in brief the main concepts of MCA, as well as its extension in the framework of image inpainting, to be exploited for recovering the missing information in our GIXD data due to detectors malfunction.
EXPERIMENTAL EVALUATION In this section, we evaluate the efficiency of the MCA framework for the recovery of missing information in highresolution GIXD images, and subsequently, for the analysis of 1-D intensity patterns (linecuts), which are of interest for characterizing the structure of thin films. More specifically, our test set consists of two 2 048 × 2 048 GIXD images, X1 and X2, shown in Figure  2 , whose acquisition details were described in Section 1. As already stated, the analysis of GIXD data is based on the reduction of the 2-D images into 1-D intensity patterns. From the several linecuts mentioned in Section 1, herein we focus on three distinct types of linecuts (Fig. 1b ): • Intensity as a function of the scattering vector q (I versus q); • Intensity as a function of the out-of-plane component of the scattering vector (I versus q z ); • Intensity as a function of the polar angle (I versus χ).