dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample labeling, microarray hybridization, scanning and raw data acquisition were performed at HudsohAlpha Institute for Biotechnology (Alabama, USA). Data preprocessing, normalization and differential expression was performed using the R-statistical software v12. 0  [63]. Log2-transformed data was normalized using the quantile normalization method  [64]. Using the probe detection p-value calculated by GenomeStudio, only those transcripts present in 3 or more samples were considered for differential expression analysis. After filtering, a total of 25,539 transcripts were finally used to determine the associated gene expression profiles. Differential gene expression between cases and controls was tested using the t-test method implemented in the Bioconductor multtest package  [65]. Correction for multiple testing was performed using the Benjamini and Hochberg False Discovery Rate method  [66].
In order to contextualize the gene networks associated with psoriasis, we performed a comprehensive search for previous studies having found evidence between the network genes and the disease. Using each gene annotation and the term 'psoriasis' we performed systematic queries using NCBI Pubmed database (October 2013). In order to reduce the presence of false positives only genes with absolute FCH > 1. 2 were included. A total of 70 overexpressed genes and 22 underexpressed genes were queried.
Functional annotation of expression modules Gene Ontology (GO) category enrichment analysis was performed using DAVID online tool (i. e. Database for Annotation, Visualization and Integrated Discovery, http://david. abcc. ncifcrf. gov/, Bethesda, MD, USA)  [29]. Briefly, GO enrichment is calculated comparing the frequency of genes from each different GO present in the set of differently expressed genes to the frequency of genes for this same GO in the human genome background.
Gene set enrichment analysis GSEA is a software method to analyze and interpret microarray data using biological knowledge  [30, 31]. Using the differential gene expression result between cases and controls and a list of predefined biological sets of genes, GSEA tests the overrepresentation each gene set in the group of most associated genes compared to the group of genes showing lower -or none-evidence of association. In the present study we used GSEA version 2. 0. 12 (www Broad Institute at MIT), with the C2 curated canonical pathway gene set (i. e. c2. cp. v3. 1. symbols. gmt download), collapsing of dataset to gene symbols, weighted enrichment statistic and real gene list sorting. Maximum size of gene sets was set to 500 genes and minimum gene set size was set to 15. Empirical significance values were calculated using 10,000 permutations.
Acknowledgements We thank  Devin Absher  and the  HudsonAlpha Institute for Biotechnology  for their support. We thank  Natalia Figueras Sanchez  for technical assistance. This study was funded by of the  Spanish Ministry of Economy and Competitiveness , grant numbers:  PSE-010000-2006-6  and  IPT-010000-2010-36. The study sponsor had no role in the collection, analysis or interpretation of the data.
The Workshop covered a range of topics but provided a facility to focus on some key areas of research. Highlights from this included the following: • The events of late July into early August 2010 involved the first major active region complex of the new solar cycle, releasing at least four coronal mass ejections on 01 of August 2010 alone. The timing and location of these events, which were mostly Earth-directed, ensured that key observations could be made, and compared from missions such as SDO, STEREO, the Advanced Composition Explorer (ACE), Wind, the Solar Mass Ejection Imager (SMEI) instrument onboard Coriolis, Venus Express (VEX), Mars Express (MEX), and the analyses and interpretation of these datasets was a major element of the Workshop. At the heart of these studies were issues such as the impact of these events at Earth and their predicted arrival times, the interaction of CMEs with one another, methods for tracking and modelling the propagation of the CMEs, and studies of their onsets.
He became a Co-Investigator on the Heliospheric Imager (HI) instruments onboard the twin NASA STEREO spacecraft and also a Member of the Solar and Space Weather Key Science Project for LOFAR. During 2007 and 2008 he was one of the leading organisers for the International Heliophysical Year. Andy also achieved his final academic position of Senior Lecturer at Aberystwyth University in 2006 (although popular opinion suggests that he surely should have been made a professor in more recent years prior to his death). IPS research today is just as prevalent as at its discovery as detailed in the first IPS paper in 1964, which happened to be the year of Andy's birth -perhaps it was fated in some sense that Andy took up that field with such vigour for most of his research career. There are active research groups around the world taking and using IPS data for scientific research: at the University of California San Diego (UCSD), CA, USA; at the Solar-Terrestrial Environment Laboratory (STELab/STEL), Japan; at Ooty, India; at Aberystwyth University, Wales, UK; at the Pushchino observatory, Russia; at Michoacán, México (MEXican Array Radio Telescope -MEXART); and also with LOFAR across northern Europe.
Acknowledgements It is a pleasure to acknowledge the Editors and staff at  Solar Physics  for their work in pulling this Topical Issue (TI) together, and thanks again to the team of referees for their time and effort in making this a very successful TI, covering a broad range of topics and studies related to observing and modelling the inner heliosphere. Special thanks also go to colleagues and friends of the late  Andy Breen , but especially to  Shelley Upton  (a dear and long-time friend of Andy's),  Andrew Rawlins  (a very close friend.
Note that this description involves only the data part. In the imperfect CSI case, we consider that channel estimates are obtained via the use of pilot symbols, and the description will be refined accordingly. These details are provided in the next sections. We study two relaying protocols: DMF relaying and DF relaying as shown in Figure  1. In DMF relaying, two receivers first demodulate the corresponding signals y 1r , y 2r to get the estimated codewords c r 1 , c r 2. Then the estimated codewords are interleaved before being network-encoded to get c r = π(c r 1 ) ⊕ π(c r 2 ), where π(. ) denotes interleaving operations and ⊕ denotes bitwise XOR operations. In DF relaying, two soft-input hard-output (SIHO) decoders decode y 1r , y 2r to get the estimated information messages u r 1 , u r 2.
Imperfect CSI As far as the imperfect CSI case is concerned, we restrict our attention to only block fading channels with F blocks and quasi-static fading with F = 1. The reason is that channel estimation is assumed to be obtained via a pilotbased approach for  [23] , which is clearly not compatible with fully interleaved Rayleigh fading. The channel gain is assumed to be constant over one block and is assumed to change independently from block to block. In our setting, a codeword covers F blocks, and the relay estimates the error probability of the whole codeword based on the knowledge of the channel gains of all blocks (see section 5 below). These channel gains are estimated via a pilot message, which is inserted at the beginning of each block, and transmitted via BPSK modulation. Let L d be the length in bits of the coded data part and L p the length of the overhead.
Acknowledgements The research work is supported in part by the  European Commission  under the auspices of the  FP7-PEOPLE MITN-CROSS   FIRE  project (Grant  317126 ) and in part by the  European Commission  in the framework of the  FP7 Network of Excellence in Wireless COMmunications NEWCOM#  (Grant agreement no. 318306 ). Received: 8 April 2013 Accepted: 23 October 2013 Published: 11 November 2013.
2 ). To elucidate primary effects of the wat1 mutation, we carried out comparative microarray analysis profiling mRNA global expression of 10-day-old in vitro-grown wild-type and wat1-1-mutant seedlings. This revealed 40 genes that exhibited significantly altered expression levels in wat1-1, with 13 genes displaying lower and 27 higher mRNA levels compared with the wild type (Supplementary Fig. S1 ). The two most downregulated genes, besides WAT1 itself, were IAA19 and an auxin-responsive GH3 family member both known to be associated with auxin responses. Among the 13 downregulated genes, at least 7 are induced by auxin in wild-type plants according to the Genevestigator database  22  (Supplementary Fig. S1 ). In contrast, among the 27 genes upregulated in wat1, 5 are repressed by auxin in wild-type plants (Supplementary Fig.
S1 ). These results revealed that the wat1-1 mutation modulates the expression levels of several auxin-responsive genes. Finally, to determine whether there is a causal link between lower auxin content and the fibre phenotype in wat1 stems  19  , we locally applied auxin (2,4-D and NAA) to growing stem segments of wat1. Ten days after auxin application, cell wall thickness of wat1 interfascicular fibres was restored to wild-type levels (Fig. 3 ). These results confirm the role of auxin in promoting xylem fibre differentiation  23, 24. WAT1 is a tonoplast-localized auxin transporter. The WAT1 gene encodes a plant-specific protein of 389 amino acids  19  predicted to have 10 transmembrane domains, and it has been classified as a member of the Plant Drug Metabolite Exporter family (Transport Classification Database (http://www.
Finally, WAT1 did not facilitate 3 H-Trp uptake (Fig. 6e ). WAT1-mediated IAA influx was further confirmed by testing it in an independent heterologous expression system: Xenopus oocytes (Fig. 6f ). As compared with water-injected oocytes, WAT1 complementary ribonucleic acid (cRNA)-injected oocytes incorporated significantly more 3 H-IAA. WAT1 did not facilitate Trp import or export in oocytes: influx and efflux of  15  N-labelled Trp in WAT1-expressing oocytes were 100. 02% ± 0. 13 (n ¼ 11) and 101. 13% ± 1. 09 (n ¼ 8) that of control oocytes, respectively (mean±s. d. ). These data confirm that WAT1 catalyses a net influx of IAA, in agreement with data obtained in yeast. These results demonstrate that WAT1-mediated IAA transport activity does not require any additional, plantspecific components.
To our knowledge, this is the first report demonstrating a role for the vacuole in the regulation of auxin homoeostasis. WAT1mediated auxin transport across the tonoplast membrane uncovers an additional level of intracellular complexity in auxin signalling pathways in plant cells. Paradoxically, the presence of auxin and its metabolites in vacuoles has never been demonstrated, although in earlier physiological studies it has been inferred  32, 33. 14 C-IAA transport assays carried out on isolated vacuoles showed that WAT1 exports IAA from the vacuole to the external medium, the equivalent to the cytoplasm in plant cells. This directionality is strongly supported by the overall decrease in auxin response gene expression in wat1 seedlings and heterologous expression data in yeast, whereby WAT1 facilitates auxin uptake from the extracytoplasmic space into the cytosol.
0) and 1 mM each of potassium ferri/ferrocyanide. After the reaction, the GUS staining was fixed for 1 h in 3% paraformaldehyde, 0. 5% glutaraldehyde and 50 mM sodium phosphate buffer (pH 7. 0). Microarray analysis. Microarray analysis was performed with a 22-k CATMA Arabidopsis array. Two independent biological comparisons were carried out. RNA integrity check, cDNA synthesis, hybridization and array scanning were done as described  48. Statistical analysis and identification of differentially expressed genes were done as described  49. Expression data from this article were deposited according to MIAME ('Minimum Information About a Microarray Experiment') standards in the GEO ('Gene Expression Omnibus') database (http://www. ncbi. nlm. nih. gov/geo) under accession number GSE10716 and at CATdb ('Complete Arabidopsis Transcriptome database', http://www.
urgv. evry. inra. fr/CATdb/) under the project name 'RS06-04_Noduline-like'. Coexpression analysis was performed using the Arabidopsis Coexpression Data Mining Tools from the University of Leeds  21. WAT1 cDNA cloning for expression in yeast. WAT1 cDNA amplified from 10-day-old in vitro Arabidopsis Col-0 plantlets using N21-PstI/S (5 0 -AAAACTGC AGATGGCGGATAACACCGATAA-3 0 ) and N21-SalI/AS (5 0 -ACGCGTCGACT CAAACATTGTCCGTTGACT-3 0 ) primers was cloned into the pDR196 yeast expression vector. For subcellular localization, the GFP sequence was fused in carboxy terminal to WAT1 before cloning into pDR196 as well. 3 H-tryptophan and 3 H-IAA uptake assays in yeast. Yeast cells were grown (OD 600 ¼ 1) in liquid minimal medium plus dextrose, collected by centrifugation (6,000 g, 5 min), washed (10 mM sodium citrate, pH 4.
Acknowledgements We are grateful to  R. Daniel Gietz  ( University of Manitoba ) for advice on yeast selection and to  Wolfgang Koch  ( Tu ¨bingen Universita ¨t ) for the gift of the pDR196 vector. We also thank  Barbara Bassin ,  Rita Saraiva  and  Maja Schellenberg  ( Universita ¨t Zu ¨rich ) for their help in performing vacuole transport experiments,  Bartel Vanholme  for training (VIB, Gent),  Ruth Evangeline Timme  ( University of Maryland ) for giving us access to her unigenes sequence database and  Monique Erard  ( Institut de Pharmacologie et de Biologie Structurale, Toulouse ) for valuable discussions on WAT1 predicted topology. We also thank  Markus Grebe  ( Umeå University, Sweden ) for his critical comments on the manuscript. This work was funded by two  ANR  grants:  ANR-07-GPLA-014  'Walltalk' to Ph.
R. and D. G. , and  ANR-06-GPLA-012  ' Genoplante Transportome ' to C. C. -F. , B. L. and J. -B. T. O. D. and W. B. acknowledge funding from the  Stanford University GCEP (Towards New Degradable Lignin Types)  and K. M. acknowledges the  Ghent University BOF  Grant  174PZA05. K. L. and O. N. acknowledge the  Swedish Governmental Agency for Innovation Systems (VINNOVA) , the  Swedish Research Council (VR)  and  Kempestiftelserna, O ¨rnsko ¨ldsvik, Sweden for funding and O. N. acknowledges the Ministry of Education, Youth and Sports of the Czech Republic  ( LK21306 ). X. J. , J. F. and U. F. thank the  DFG  ( Fi1668/1-1 ) and Bio4Energy for funding.
Hydrological signatures The variations of three hydrological signatures (runoff coefficient, precipitation elasticity of streamflow and first/last deciles of streamflow) are studied at sub-basin scale using 16 ORE HYBAM gauge stations (table 1) to characterize the Table  1. List of ORE HYBAM gauge stations over the Amazon River basin (see figure  2  for their localization on a map). For each station, mean annual streamflow (Q mean ), runoff coefficients (R coeff ) and streamflow elasticity to precipitation (ε p ) are computed from observations (ORE HYBAM database) and present-time simulation ORCH4, on average over the 1980-2000 period (except for the observed streamflow at the river mouth whose value is from  Callede et al (2010) , for the 1972-2003 period average). The colors indicate the localization of the stations in the basin (black: mainstream; red: southeastern/southern region; green: western region; blue: northwestern/northern/ northeastern region).
A high (low) runoff coefficient points out that a large amount of water leaves the basin as streamflow (as ET). R coeff = Q P. (3) Three runoff coefficients are computed for each subbasin: • R OBS coeff is computed from ORE HYBAM discharge data (except for the river mouth discharge whose value is from  Callede et al (2010) , for the 1972-2003 period average) and from the precipitation dataset used to correct NCC forcing (see section 2. 2), on average over the 1980-2000 period. • R ORCH4 coeff is computed from simulated precipitation and streamflow values of the present-time simulation, ORCH4. • R ENSEMBLE coeff , is computed from the averaged simulated precipitation and streamflow values of the different future simulations, for each scenario and time horizon.
By computing elasticity coefficients, we also show that the southern basins with low runoff coefficient are generally more responsive to precipitation change than the western basins with a high runoff coefficient. This result is in agreement with the findings by  Chiew (2006)  and  Paiva et al (2011a). Some uncertainties relative to the hydrological parameterizations of the LSM can be addressed. The present-time ET underestimation  (Guimberteau et al 2012)  and the infiltration parametrization are sources of uncertainties in future land water budget. Moreover, the routing module of ORCHIDEE does not represent the river dams and the backwater effect likely to affect the extreme stream flows in some tributaries of the Amazon  (Meade et al 1991 , Tomasella et al 2011). An advanced large-scale river routine module which represents backwater effect would represent more realistically the simulated water level in the rivers  (Paiva et al 2011b , Yamazaki et al 2011 , 2012).
Acknowledgments This work was financially supported by the  GIS  ( Groupement d'Intérêt Scientifique) REGYNA (REGioNAlisation des précipitations et impacts hYdrologiques et agronomiques ) and the  EU-FP7  AMAZALERT (Raising the alert about critical feedbacks between climate and long-term land-use change in the Amazon) projects. Simulations with ORCHIDEE were performed using computational facilities of the  Institut du Développement et des Ressources en Informatique Scientifique (IDRIS, CNRS, France ).
Numerical analysis Numerical temporal traces of the different neurons (Figs. 1, 6, 8, 9)  were reproduced by implementing in MATLAB (available at http://www. mathworks. com) the original models as described in the associated papers. The phase portraits in Figures  1  and 3  were hand-drawn using the Open Source vector graphics editor Inkscape (http://inkscape. org). The phase portraits in Figure  6  were numerically drawn with MATLAB by implementing the planar model in  [10]  and subsequently modified with Inkscape. The bifurcation diagrams in Figures  6, 7 , and 8 were drawn by implementing the algorithm of Table  2.
The Large Yield Radiometer (LYRA) is an XUV-EUV-MUV (soft X-ray to mid-ultraviolet) solar radiometer onboard the European Space Agency PROBA2 mission that was launched in November 2009. LYRA acquires solar irradiance measurements at a high cadence (nominally 20 Hz) in four broad spectral channels, from soft X-ray to MUV, that have been chosen for their relevance to solar physics, space weather and aeronomy. In this article, we briefly review the design of the instrument, give an overview of the data products distributed through the instrument website, and describe the way that data are calibrated. We also briefly present a summary of the main fields of research currently under investigation by the LYRAconsortium.
Voltages are then switched to frequencies by a voltage-to-frequency converter (VFC). Regularly, those VFC are connected to stable calibrated onboard voltage sources of 5 volts, 2. 5 volts, and 0 volts. These measurements are interleaved in the telemetry, to be used as reference when processing the data. At the end of the acquisition chain there is a counter that counts the number of pulses received over the duration of the integration period. This number, which is expressed in counts, constitutes the raw LYRA data that are downloaded as part of the telemetry.
Data Products LYRA produces time series of spectral irradiance in its four bandpasses in a quasi-uninterrupted way. Interruptions might happen during calibration campaigns (once every two weeks on average), or during the Winter occultation season, when the spacecraft transits the Earth's shadow. Nevertheless, since PROBA2 is flying on a polar, dawn-dusk heliosynchronous orbit, such occultations are limited to a three-four months winter period (depending on the channel) and last for maximum 25 minutes every orbit (one orbit is 100 minutes long). As mentioned in Section 2, LYRA includes three units, which are similar from the spectral point of view. The four channels of a unit are operated in parallel, acquiring irradiance measurements at a nominal cadence of 20 Herz, but that could go up to 100 Herz.
oma. be). They consist of uncalibrated and calibrated data, as well as various quicklook datasets (see Table  2 ). Once downloaded, raw LYRA data in counts are converted back to counts ms -1 , dividing them by the integration time, and distributed without further processing through the instrument website as level-1/engineering data, in standard files -either from the nominal unit (. std) or from an additional 'backup" unit (. bst), that can be either unit 1 or 3. Information about the acquisition context (temperature, pointing, status of covers, status of LEDs, usage of a backup unit. ) compose the ancillaries stored in the metadata (. met) file. Additional files gather the data acquired during calibration campaigns (dark currents, LED signals), or the rejected data (outliers, data acquired during transitions between acquisition modes).
Therefore, only later data points, representing calibration campaigns after 23 June 2010, are used to estimate the future development. The loss caused by degradation is estimated relative to the first light individually for each channel. The degradation is corrected by adding this loss to the current levels. Correction by addition (as opposed to multiplication by a correction coefficient) has advantages, because degradation is a function of the wavelength and does not uniformly affect the broad LYRA channels over SOLA: SOLA1866R3. tex; 27 February 2013; 2:23; p. 10 their whole spectral range. In particular, there is no apparent sensitivity loss in the X-ray range. Flares of similar intensity in GOES/XRS scale now peak to the same count rates in aluminum and zirconium channels as they did at the beginning of the mission, while the measurements of the overall EUV background in the same channels has significantly decreased.
This out-of-band radiation constitutes a source of measurement error that might be important in the case of the Lyman-α channel (see purities in Table  1 ). Since LYRA channels cover broad spectral ranges, it is not possible to directly invert Equation (1) to retrieve the spectral irradiance [E(λ, t)] from the measured photocurrents  [i]. For an absolute radiometric calibration, we compare data acquired at any time [t] to a pre-degradation reference LYRA measurement (the first light data of 6 January 2010), for which this conversion into irradiance units is known. This comparison is performed after correction for the degradation, as detailed in Section 3. 2. 2. It assumes that the relationship between LYRA count rate and irradiance in physical units is linear. E cal = i uncal -i d + corr i FL uncal -i FL d E FL cal ( 2 ) where: SOLA: SOLA1866R3.
Flat-Field Effects As described in Section 3. 2, the calibration process does not yet correct the data for variations associated with pointing fluctuations. An analysis of PROBA2 attitude over several orbits reveals that pointing is stable up to 90 arcseconds. Spacecraft jitter introduces fluctuations in the LYRA signal of less than 1 %. Nevertheless, it often happens that PROBA2 is off-pointed in the framework of calibration or scientific campaigns, introducing signal fluctuations of which the amplitude depends on the new pointing (see Figure  9 ). Such campaigns are noted in the public calendar of LYRA activities linked from the website of the instrument http://proba2. oma. be/about/operations. It is intended to correct for those fluctuations in a future version of the calibration routine.
Radiometric Accuracy From Equation (4), the maximal uncertainty on the calibrated LYRA data is described by ∆E cal E cal = ∆i i + ∆ λ Edλ λ Edλ + ∆A A + ∆ λ EF Ddλ λ EF Ddλ (5) where i = i uncal + i d + corr. We will discuss the terms of this equation one by one.