dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical and geostatistical functions are implemented in the R project e and, among the available GIS libraries, GeoTools is chosen because it includes all of the necessary concepts and is written in Java, a good language to design friendly interfaces. CGAL (Computational Geometry Algorithms Library) f provides efficient and reliable geometric algorithms in the form of a C++ library. The FisPro environment offers a high level of interaction between expertise and data for designing and optimizing fuzzy inference systems. It is not designed to handle geographic data, but can be useful for instance to build composite variables by approximate reasoning or to design fuzzy partitions in the attribute space. Available on the Web since 2002, it is widely used in different fields and for various purposes (education, research, commercial).
FisPro (Fuzzy Inference System Design and Optimization) FisPro has a C++ core and a Java interface. It allows Fuzzy Inference System (FIS) design from expert knowledge or data. Among the available fuzzy software toolboxes, FisPro stands out for system interpretability, which is a necessary condition for cooperation between expert knowledge and data. FIS can be completely, and automatically, designed from data  14. In the latter case, semantics is guaranteed at each step. The necessary conditions for fuzzy partitions to be interpretable and to implement the linguistic variable concepts have been studied by several authors  15. The main points are distinguishability, a justifiable number of fuzzy sets, normalization, sufficient overlapping and coverage. These conditions are met by so-called strong fuzzy partitions (SFPs), such as the one shown in Figure  2.
A SFP described by f membership functions (MFs) on the universe U fulfills the following condition: ∀x ∈ U, f ∑ i=1 µ i (x) = 1 (1) where µ i (x) is the membership degree of x in the ith MF. The rules share the same linguistic terms and the optimization module does not modify the FIS structure and semantics are preserved after parameter tuning. FisPro's efficient approach in exploratory analysis and system modelling has been used to deal with agricultural applications  3. Special attention has been put on the dynamical behavior of a FIS following user modifications. Each variable, rule or data item can be activated/deactivated. The system parameters (operators, partitions, rule description) can be edited. All changes are dynamically handled and all current windows are updated, including the inference result ones.
GeoFIS provides a simple evolutive framework to visualize and analyze spatial data. Based on open source libraries, it is written in Java and uses GeoTools to display existing data layers or generate them from raw text files. It includes calls to R to provide one-dimensional spatial analysis. It is relatively easy to implement more geostatistical techniques through calls to R spatial packages. GeoFIS also includes an elementary zone learning module, written in C++. Add-ons will allow to introduce new learning methods into the framework, in particular soft computing ones.
One-dimensional statistical analysis All these functionalities are implemented using the R software  11  with the gstat g package. The R functions are used by a large research community and are well tested. The interface implemented here uses the Rserver h developments, which allow the direct transfer of objects between R and Java. Given the distribution, data can be automatically or manually filtered, to define a validity range, for instance one that holds 95% of the data, or by selecting the bounds, and so remove outliers. The histogram window and the map viewing one are dynamically linked, so that the valid and removed data points are plotted in the latter window in two distinct colors, and updated according to the user edits in the former one. In the case of spatial data, it is important to model the degree of spatial dependence.
Implementation details GeoFIS currently includes the two previously described modules, one-dimensional statistical analysis and zone learning. Both modules are implemented into the Java-based interfaced general framework, and the second one is also available as a stand alone C++ program. This makes it possible to use it independently for computationally intensive automated learning tasks. GeoFIS is protected by a CeCILL open source license i. The programs are hosted on the collaborative development environment MULCYBER j , in binary and source form. GeoFIS handles various input and output formats: csv files, shapefiles, raster files.
We introduced a soft computing tool allowing the users to define distances based on expert knowledge by means of fuzzy partitions and to incorporate them in a segmentation algorithm. The software functionalities are illustrated on two case studies in Agronomy, that show how they can help practitioners. This is only a first step. For instance, it is necessary to develop specific visualization tools, in order to represent a fuzzy zone, with uncertainties in two different spaces, the geographical space and the attribute space. The interpretability constraints which have been implemented in fuzzy software for ordinary data, such as FisPro, are not so easy to define for georeferenced data. There is no trivial extension of strong fuzzy partitions to a two-dimensional space. The development of approximate map comparison techniques and suitable aggregation operators, in order to monitor the temporal evolution of zones on a map, or to compare maps for different attributes, constitutes another topic of interest.
Actin Dynamics Regulates the Late Stages of Exocytosis In order to analyze how the disruption of actin polymerization affects the exocytosis, we evaluated the effect of CytoD on the amperometric parameters. Similarly to that observed with iRNADyn2 and Dyn2K44A, CytoD treatment significantly increased Q (1. 6260. 16 pC) and t 1/2 (19. 861. 98 ms) of the exocytotic events induced by 10 mM DMPP. Furthermore, compared with control cells, CytoD also increased foot duration (21. 561. 76 ms). These data confirm that cortical actin polymerization controls both, expansion of the initial fusion pore and catecholamine quantal release. We also analyzed the effects of CytoD in cells expressing pEGFP, iRNADyn2 or Dyn2K44A. Similarly to what occurs in untransfected cells, CytoD treatment in pEGFP-transfected cells increased significantly Q (1.
The expression of dynamin isoforms from BCC, bovine brain tissue and from PC12 cells was evaluated by western-blotting using specific antibodies for dynamin-1 (Santa Cruz #sc-12724) and dynamin-2 (BD#610264). The respective extracts were separated by electrophoresis on a 4-12% SDS-polyacrylamide gel gradient and then proteins were electrotranferred to nitrocellulose membranes. Chemiluminescence was developed using the Super Signal West Dura Extended Duration Substrate system (Pierce). Immunoreactive bands were detected using the image acquisition system Chemi-smart 5000 and quantified using Bio-1D software (Vilber Lourmat).
Three different investigators made independent measurements from the same images and resulting vesicle sizes were averaged. The area of chromaffin granules was determined using the public domain Image J software (NIH, Bethesda, MD, USA). Granules diameter was calculated assuming a spherical shape [diameter = 2*(area/p) 0. 5 ] and corrected using a previously published algorithm  [75]. The secretory granules were counted using an ImageJ macro that allows automatically select dark objects. Erroneously selected objects were manually removed to obtain a corrected number of granules.
TIRF Microscopy to Visualize F-actin Organization in vivo To obtain high resolution in vivo images of the cortical actin organization pattern we used TIRFM. Briefly, living plated chromaffin cells, transfected with Life-act-ruby or co-transfected with Life-act-ruby and GFP-dynamin-2 constructs, were incubated in a Krebs-Hepes solution, mounted in a chamber and maintained at 37uC on the stage of a Nikon inverted microscope (TE2000E) equipped with a 100X 1. 49 N. A oil immersion objective, a white light TIRF and an intensilight illumination system. Images were acquired using a CCD cooled camera (Nikon Digital Sight DS-2MBWc) driven by the ACT2U 1. 72 software (Nikon Instruments, Inc). The depth of the evanescence wave was estimated by the use of two independent tools: 1) 1 mm-diameter microspheres stabilized in a 1% agarose gel, and 2) a cell line transfected with a GFP tagged-membrane protein to visualize the forward transport vesicles.
De novo Actin Polymerization in Permeabilized Cells To evaluate de novo actin polymerization, cells were permeabilized during 6 minutes with 20 mM digitonin in KGEP buffer (mM: 139 K-glutamate, 20 PIPES, 5 EGTA, 2 ATP-Mg 2+ and different free calcium concentration, pH 6. 6) in the presence of 0. 3 mM Alexa Fluor 488 G-actin conjugate (Molecular Probes) and then fixed with 4% paraformaldehyde for confocal microscopy visualization (upright Eclipse Nikon 80i). Total F-actin was stained using 1 mM phalloidin-rodhamine B. Confocal images were analyzed and processed using the Image-J software (NIH, USA).
Data Analysis and Statistics Amperometric spikes were analyzed using macros for IGOR (Wavemetrics) specifically designed to filter, identify and analyze individual amperometric spikes. All used macros can be downloaded from the web-site: http://webpages. ull. es/users/rborges/. The analysis was restricted to spikes with amplitudes. 10 pA. For foot-signal, the analysis was restricted to spikes with foot amplitudes. 1 pA and foot durations. 3 ms. Data of amperometric spikes were averaged by individual cell, thus, data presented correspond to means 6 SEM of cell averages from at least three different cultures. For amperometric data and image analysis ''n'' refers to the number of tested cells. Statistical comparisons were performed utilizing the Kruskal-Wallis test for nonparametric data or ANOVA test for parametric data.
Acknowledgments We acknowledge the generosity of  Dr. A. Benmerah  ( Institut Cochin, Paris, France ) for kindly providing  GFP-Dynamin2WT ,  GFP-Dynamin2K44A , and  GFP-Eps15ED95/295  constructs. We thank  T. Thahouly  and  F. Vargas  for technical assistance. We acknowledge  Mecesup UVA0805 ''LAMAF'' and Plateforme ''Imagerie Cellulaire de Strasbourg Esplanade '' (IFR 37des Neurosciences). We also thank  Frigorı ´fico Don Pedro  for providing bovine adrenal glands.
This work has been supported by the cooperation program between France and  Chile ECOS-SUD/Conicyt  (# C08B01 ) to SG and AMC, and by grants  FONDECYT 1110552  (to AMC),  ANR-07-JCJC-088-01  (to SG),  CONICYT   AT24100005  and  21080096  (to A G-J), and  P09-022-F  from  ICM-ECONOMIA, Chile. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Phylogenomic Approaches: Assessing the Chlamydialean Contribution to Archaeplastidial Genomes We reanalyzed the contribution of Chlamydiales to the genomes of Archaeplastida using the additional data (e. g. , >60k proteins from the red algae Calliarthron tuberculosum and Porphyridium cruentum;  Chan et al. , 2011)  that have become available since previous analyses  (Huang and Gogarten, 2007; Becker et al. , 2008; Moustafa et al. , 2008). The set of predicted proteins from all available Chlamydiales genomes (28,244 proteins) was used to query a local database that contained annotated protein sequences from RefSeq release 37 and genome data from other public databases (for details, see Methods). This phylogenomic analysis returned 23,470 RAxML maximum likelihood trees with clade support values for each phylogeny based on 100 bootstrap replicates.
Changing the bootstrap cutoff for Chlamydiales-Archaeplastida monophyly to 90% still yielded 30 protein families. At the 75% cutoff, within the Archaeplastida, the Chloroplastida (green algae and land plants) contain most of chlamydial genes followed by Rhodophyta (red algae). These numbers reflect the current availability of genome data (see Supplemental Table  1  online) with animals (976,563 proteins in the database) and Chloroplastida (506,307 proteins) being data rich and other groups, such as haptophytes  (96,133 proteins), cryptophytes (38,939) , and glaucophytes (57,737 proteins), currently being relatively data poor. The data-rich fungi (451,434 proteins) contain only 14 chlamydial genes and the Amoebozoa (131,139 proteins), which are frequently infected by chlamydial species, contain only 12 genes  (Horn, 2008).
The novel glaucophyte sequences were isolated using RT-PCR. Alignments are shown in Supplemental Data Sets 3 to 5 online, and the full tree is displayed in Supplemental Figure  2  online. eukaryotic lineages that have lost a plastid, have, as a consequence, lost the majority of their genes of cyanobacterial origin. Indeed, the nuclear genome of Cryptosporidium parvum, an apicomplexa parasite that has lost the apicoplast, has only five genes of possible cyanobacterial (two genes) or plant ancestry (three genes)  (Huang et al. , 2004). In this context, the maintenance of a 50 gene signatures in the Archaeplastida genomes underlines the biochemical importance of chlamydial gene products in the maintenance of the symbiosis.
Phylogenomic Methods and TTS Prediction Analysis The complete set of Chlamydiales proteins (28,244 proteins as of January 2011) available from RefSeq  (Pruitt et al. , 2007)  was phylogenomically processed using iTree  (Moustafa et al. , 2010 ) against a comprehensive database, which was assembled from the complete RefSeq proteins and additional algal and microbial genomes from the Joint Genome Initiative. We also supplemented the database with recently published data from key lineages, such as red algae, dinoflagellates, and the glaucophyte Cyanophora paradoxa  (Price et al. , 2012). The generated phylogenetic trees were searched for trees that indicated sister relationship between Chlamydiales and Archaeplastida using PhyloSort  (Moustafa and Bhattacharya, 2008). Only trees with bootstrap values $75% were considered in subsequent analyses.
Sequence Mining and Phylogenetic Tree Reconstruction Amino acid sequences from P. amoebophila were first used as queries to look for similar protein sequences using BLASTP  (Altschul et al. , 1997)  against a local genome database containing ;300 bacterial and ;60 eukaryotic complete annotated genomes. All homologous sequences (with an e-value inferior to 1. 0e-05) were collected from each BLAST hit list and aligned together using the MAFFT software  (Katoh et al. , 2002). After automatic gaps removal in the resulting multiple alignments, maximum likelihood tree reconstructions were done using the software FastTree  (Price et al. , 2009). Trees were manually inspected to remove possible sequences duplicates, to refine sequence sampling, and to add additional sequences of interest that were not part the original set used for BLASTP.
Trees were then realigned and recomputed with the TREEFINDER software  (Jobb et al. , 2004)  using the LG + Gamma model. Protein sequences where collected from GenBank. Experimental trees (see Supplemental Figure  1  online) and candidate HGT trees (see Supplemental Data Set 1 online) were run with 100 bootstrap replicates. For the GlgA and GlgX trees (Figure  1 ; see Supplemental Figure  2  online), we used the ProtTest 2. 4 to identify the bestfit model of protein evolution for our data (the LG + I + G + F model) and applied 1000 bootstrap replicates (http://www. ncbi. nlm. nih. gov/protein), the Joint Genome Institute (http://www. jgi. doe. gov/) and on genome home project websites for Galdieria sulphuraria (http://genomics. msu. edu/ galdieria/), Cyanidioschyzon merolae (http://merolae.
biol. s. u-tokyo. ac. jp/), and Arabidopsis thaliana (http://www. Arabidopsis. org/). Isoamylase sequences from C. paradoxa and GBSS sequences from unidentified Cyanobacterium Clg1 (GenBank taxonomy ID 197335) were produced in this study. The analysis of isoamylases from P. acanthamoeba showed that the presence of two open reading frames in the genome was due to an annotation error. We therefore decided to fuse these two open reading frames together. Cloning of Isoamylase Sequences from Glaucophyta mRNA were purified from 300 mL of exponential growth culture of C. paradoxa using the FastTrack 2. 0 kit (Invitrogen). Reverse transcription reactions (200 ng of mRNA) were performed with SuperScript III and in the presence of GeneRacer oligo(dT) following the recommendations of the GeneRacer kit (Invitrogen).
The Plant Cell PERSPECTIVE ACKNOWLEDGMENTS S. G. B. was supported by the  Centre National de la Recherche Scientifique , the  Université des Sciences et Technologies de Lille , the  Région Nord Pas de Calais , and  Agence Nationale de la Recherche  grants ("starchevol" and "ménage à trois"). D. B. was partially supported by grants from the  National Science Foundation  ( MGSP 0625440  and  MCB 0946528 ). A. P. M. W. appreciates support by the  German Research Foundation  ( CRC-TR1  and  WE 2231/6-1 ). L. G. received financial support from  Kurzfristige Auslandsstipendien (KWA) University of Vienna.
Acknowledgements We thank  Jürgen A. Knoblich , the  Vienna Drosophila RNAi Center (VDRC)  and the  Bloomington Drosophila Stock Center  for fly stocks;  Emmanuel Caussinus  and  Susanne Flister  for great technical help; and all members of the  Reichert lab  for discussions and assistance. This work was supported by  SNSF-NFP63  " Stem Cells and Regenerative Medicine " grant  406340_128006/1  to Heinrich Reichert.
, 2009; Schulz et al. , 2009). It reveals an efficient and fast means to provide estimates of values otherwise available from complex and slow models. One drawback is the resources and time needed to compute the abaci that may amount to months. Another drawback is that the abaci formulation has to be adapted to the available input data source. Therefore, this paper deals with the specifics of an abaci approach for typical numerical weather prediction output characteristics as represented in the MACC reanalysis data set. A set of abaci and interpolation functions to speed up the solution of the radiative transfer equations is designed, developed and implemented. This ensemble of abaci and interpolation functions as well as functions to exploit them for aggregation in time and computation of the sun position is the model McClear.
Inputs to McClear are the solar zenith angle, the ground albedo and 6 parameters describing the optical state of the atmosphere: -total column content of ozone and water vapour, -vertical profile of temperature, pressure, density, and volume mixing ratio for gases as a function of altitude taken in the AFGL data sets (see Sect. 3. 2), -aerosol optical depth (AOD) at 550 nm, Ångström coefficient, and aerosol type: urban, continental clean, continental polluted, continental average, maritime clean, maritime polluted, maritime tropical, Antarctic, and desert (see Sect. 3. 4), and other parameters describing the geographical location and the time: -latitude, longitude and elevation above mean sea level (a. s. l. ) of the site of interest, -period of time requested, -integration period, also known as the summarization period, of the radiation: 1 min, 15 min, 1 h, and 1 day, -and time sampling step, usually identical to the integration period.
Then, Eq. (  8 ) is solved for KT for each minute of the day. The difference between the MCD43C1 and MCD43C2 products is that MCD43C2 is a snow-free version of MCD43C1. Both products exhibit gaps in time and space. Two approaches have been tested to palliate the lack of data. In the first approach, the three BRDF parameters were taken from the MCD43C2 product for the previous, current and following months for the current day. They were interpolated in time for each minute of the day, taking into account the possible absence of data. Sometimes, data may not be available for three months in a row. In these cases, the parameters were replaced by their mean values computed for the given month over the period 2004-2011. Over the ocean, fiso was set to 0. 02  (Wald and Monget, 1983)  and the two parameters to 0.
BSRN data for validation The Baseline Surface Radiation Network (BSRN) is a collection of measurements of E glo , E diff and beam normal irradiance (BNI) of high quality suitable for validation. Measurements are acquired every 1 min. Uncertainty requirements for BSRN data are 5 W m -2 for global irradiance and 2 W m -2 for direct irradiance  (Ohmura et al. , 1998). Data sets were collected for 11 stations representing a variety of climates (Table  1 ) for the period 2005-2008, except for Palaiseau,  Xianghe and Lauder (2005-2007)  and Brasilia  (2006) (2007). Following the recommendations of  Roesch et al. (2011) , only the BSRN measurements were kept which obey the following constraints:  Roesch et al. (2011)  note a percentage of missing data of 4 % for global irradiance and 13 % for direct irradiance for all studied BSRN data.
However, relative RMSE can be compared: it ranges from 13 % to 17 % in their case, and 7 % (Tamanrasset) and 9 % (Sede Boqer) in ours. Greuell et al. (  2013 ) have developed a method for the retrieval of hourly means of the SSI using Meteosat images. MACC data are inputs to their method. Comparisons were made with several BSRN stations for the year 2006 for clearsky cases and all skies. Though the period is not similar and comparisons were made on an hourly basis rather than 1 min, performances can be compared for the bias for the three common sites: Palaiseau, Carpentras, and Payerne. Both models provide similar results for both E glo and E dir. The SoDa Web site (www. soda-is. org) provides SSI from the database HelioClim-3v3 (HC3v3,  Blanc et al. , 2010; Espinar et al. , 2012). The HelioClim database derives from images of the Meteosat series of satellites using the Heliosat-2 method  (Rigollier et al.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 218793  ( MACC project ) and no. 283576  ( MACC-II  project). The authors thank all ground station operators of the BSRN network for their valuable measurements and the  Alfred Wegener Institute  for hosting the BSRN website. The authors thank the team developing libRadtran (http://www. libradtran. org)and  Laurent Saboret  for his valuable help in setting up the McClear application on the Web. They also thank the anonymous reviewers whose efforts help in bringing clarity in our work. Edited by:  G. de Leeuw.
GA is used in this study mainly because of its ease of implementation and its ability to solve optimization of nonlinear problems. Finally, from the optimum found at the previous step, a local optimization phase is carried out using the full mechanical model. In this second step optimization and simulation are decoupled and exchange information through data files. During the n iterations of the optimization procedure, n 2 simulations are performed in order to evaluate objective function and its derivatives. The local optimization in this second step is performed with a sequential quadratic programming  (Han, 1977)  algorithm (the f mincon function in matlab). This two step optimization strategy requires n 0 numerical simulations for the construction of the metamodel plus n 2 additional simulations during the second phase.