dataesr/scientific-paragraphs-categorization huggingface dataset:
GenericProxy Service We have defined a general purpose interface Gener-icProxy that provides four generic methods. These methods are described in Figure  5. Each implementation of this interface is associated with a component for which the first method getProperties() returns the list of the properties of the component, the getProper-tyValue() returns the value of a property, the setProp-ertyValue() changes the value of a property and the invoke() method invokes a given method on the associated component and returns the result. The transformations that render a component monitorable use a GenericProxy Component provided by our framework. It implements the GenericProxy Interface and the (proxy) services of that component. The byte-code of this implementation is generated dynamically by our framework.
Monitoring Transformations Monitoring process consists in informing the interested component about the changes of required properties or notifying it on a regular way or for each variation. In  (Mohamed et al. , 2012)  we have presented an approach for adding monitoring capabilities to components in the cloud in which we have considered two types of monitoring: monitoring by polling and monitoring by subscription. To complete CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience the monitoring of any component from only the name and type of a property, the interested component often uses an appropriate interface that provides the method getPropertyValue(propertyName) to request the current state of a property. If the component does not define its properties as monitorable, we need to transform it to make them to be monitorable, this can be done dynamically by our framework by adding to the byte code of the component an implementation of the predefined Gener-icProxy interface defined above and the needed byte code to send notifications (PCM, MCM or TEM).
The byte-code of a GenericProxy component is generated dynamically. For this required byte-code level manipulation we used the Java reflection API and the open source software JAVA programming ASSISTant (Javassist) library  (Chiba, 2010). The Java reflection API provides classes and interfaces for obtaining reflective information about classes and objects (Java 2 Platform API Specification, 2010). Reflection allows programmatic access to information about the fields, methods and constructors of loaded classes, and the use of reflected fields, methods, and constructors to operate on their underlying counterparts on objects. Javassist is a class library for editing Java byte-codes; it enables Java programs to define a new class and to modify a class file when the Java Virtual Machine (JVM) loads it.
In our case, we used OpenNebula which is a virtual infrastructure engine that provides the needed functionality to deploy and manage virtual machines (VMs) on a pool of distributed physical resources. To create a VM, we can use one of the three predefined templates offered by OpenNebula i. e. SMALL, MEDIUM and LARGE, or we can specify a new template. During our experiments, we used our specific template with the following characteristics: 4 cores (2. 66 GHZ each core) and 4 Gigabytes of RAM. To perform our tests, we defined two scenarios that reflect the objectives that we want to highlight in our experiments. The details of these experiments are as follows: • Compare micro-container memory consumption before and after adding monitoring facilities to estimate the overhead of the monitoring module on the micro-container consumption.
All nodes listen for monitoring packets on the agreed multi-cast address to collect and maintain monitoring data for all other nodes  (Massie et al. , 2004). Each cluster can be represented with one node, since all the nodes contain a complete copy of the cluster monitoring data. Aggregation of monitoring data is done by polling child nodes at periodic intervals. Monitoring data is exported using a TCP connection to the node being polled followed by a read operation of its monitoring data. Ganglia Monitoring is implemented by a monitoring daemon, which is organized as a collection of threads, each assigned a specific task: • Collect and publish thread: collects local node information and publishes it on a well known multicast channel. It sends periodic heartbeats. • Listening threads: listen on the multi-cast channel for monitoring data from other nodes and updates monitoring data storage.
, 2004 ; http://www. machinevision. nl). These traditional methods provide the most accurate measurements of the root system but have a major disadvantage in being extremely time consuming. The second group of methods utilizes advanced semiautomated software for RSA measurements like EZ-Rhizo  (Armengaud et al. , 2009). EZ-Rhizo also uses digital two-dimensional images of plants grown on vertical plates (similar to the classical methods above) but is faster and produces different traits and basic statistics. The method works best when root features do not physically overlap, but we have found root overlap to be common when working with Arabidopsis plants older than 10 d. Other recent programs also provide semiautomated analysis of RSA, including RootReader2D (http://www. plantmineralnutrition.
Unlike recent methods that collect information on different root traits to describe the RSA, RootScape places user-defined root landmarks on a two-dimensional grid to measure root architecture as a single integrated root system. The method employs rapid manual placement of root system landmarks. This manual step avoids one of the most problematic steps in automated image analysis (recognition of the root surface), providing a simple tool that does not require image processing. This method uses simple, two-dimensional digital images of the root system and a 20-point landmark model created in AAMToolbox, a freely available MATLAB plugin. While in-depth developmental analysis of root systems will often require knowing the contribution of individual traits, Root-Scape is a rapid method to access the holistic contribution of many individual root traits to RSA and to capture the overall property of the spatial configuration of roots in the soil  (Fitter and Stickland, 1991).
To demonstrate its utility, we used RootScape to quantify the root plasticity of Arabidopsis plants (Columbia [Col-0]) grown on four different media and compared the RootScape results with conventional measurements of individual root traits captured using the Optimas6 image-analysis software or Image J  (Abramoff et al. , 2004). This analysis showed that by measuring integrative root traits using RootScape, we could capture the vast majority of the individual trait variation, as verified by multiple regression analysis. Additionally, we tested the ability of RootScape to quantify the plasticity response in Arabidopsis mutants defective in hormone signaling. For this analysis, wild-type Col-0 and three hormone signaling mutants (auxin-resistant4 [axr4], abscisic acid insensitive4 [abi4], and cytokinin response1 [cre1]) were treated with auxin, cytokinin, or abscisic acid (ABA) versus controls.
RESULTS RootScape: Adapting Software Platform to Measure RSA Allometric methods have been applied previously to measure plant organs such as leaves  (Langlade et al. , 2005; Bensmihen et al. , 2008). To implement an allometric method for quantifying RSA, we created a 20point landmark template (model) in the AAMToolbox software, a publicly available MATLAB plugin (http:// cmpdartsvr1. cmp. uea. ac. uk/wiki/BanghamLab/index. php/Software) originally developed for face shape recognition in lip reading  (Matthews et al. , 2002). This 20-point template captures the main characteristics of the RSA. The six "primary" landmarks (green points in Fig. 1A ) are defined by recognizable developmental landmarks on the root. These included four of the primary landmarks, placed on the transition between shoot and root (point 1), the position of the first lateral root (point 2), the position of the last lateral root (point 6), and the apex of the primary root (point 12; Fig.
These treatment conditions were selected to create a variety of well-documented and stereotypical root architectures  (Nibau et al. , 2008). Plants were initially grown on a single growth medium and then transferred to either a control plate or one of the three hormone treatment conditions (for details, see "Materials and Methods" and Supplemental Fig. S1 ). After 5 d on the different hormonal treatments or control plates, the root system was imaged using a document scanner, and the unprocessed images were used for subsequent analysis using the 20-point allometric template of RootScape (Supplemental Fig. S2 ). This landmark data set was used to create the RSA morphospace and was named the "allometric Col-0 plasticity space. " These data were further processed by the AAMToolbox software in a principal component analysis (PCA) that captures the main trends of variation in the root architecture.
First, the cre1-2 interaction with CK captured in PC1 RS and PC5 RS is consistent with previously published phenotypes for this mutant, fitting with its role as a CK receptor  (Inoue et al. , 2001). This analysis also identified an interaction of the cre1-2 mutation with ABA. This is reminiscent of another cre1 allele (cre1-1) that was shown to have lower root growth in response to low levels of ABA  (Inoue et al. , 2001). The abi4-1 mutant had only one interaction with CK in PC1 (Table  III ), which may be related to interactions found by another study in which CK treatment increased the transcript level of ABI4 (Shkolnik-Inbar and Bar-Zvi, 2010). Thus, RootScape is a sensitive and robust tool to characterize root architectural phenotypes using a rapid and simple protocol.
DISCUSSION This paper presents RootScape, a rapid method for the allometric and integrative quantification of RSA applied to Arabidopsis. The RootScape method uses a 20-point landmark template created in AAMToolbox (a MATLAB plugin), which can rapidly and accurately characterize RSA variation in different genetic backgrounds or treatments. RSA variation was generated experimentally by supplying Arabidopsis plants grown in full Murashige and Skoog (MS) medium with three different hormones known to affect different aspects of root development (IAA, CK, and ABA). The landmark data from the root templates applied to this data set were then used in a PCA. These PC values were then used for correlation and multiple regression analyses in order to compare how the RootScape method is related to standard measurements of individual root traits.
RootScape, Integrative and Allometric Quantification of RSA The same plants quantified by the individual trait analysis were also measured by RootScape. Three insertion mutations (axr4-1, cre1-2, and abi4-1; 10-15 plants per treatment per genotype) were quantified by this method. Twenty landmarks (six primary and 14 secondary) were fitted along the length of the primary (main) and lateral (secondary) roots by placing the landmarks at key and recognizable positions of RSA: the start and end of the primary root, the first and last lateral roots on the main root, and the widest points of lateral roots on each side of the main root (Fig. 1 ). The PCA model of root shape and size was created from the 20-point model in the wild-type Col-0 treatment data set. In this wild-type plasticity space, we projected the model shapes for the three mutant lines and obtained PC values for later statistical analysis.
Statistical Analysis One-way ANOVA for treatment in the wild-type Col-0 data set for the 12 individual traits was performed in JMP 9. In JMP, we also performed two-way ANOVA, PCA of individual traits, correlation, and multiple regression analysis. RSA shape and size images (RootScape) were obtained in AAMToolbox in MATLAB. PCA for RootScape was also performed in AAMToolbox in MATLAB. Pairwise MANOVA comparison was done in R.
1 This work was supported by the  National Science Foundation (Arabidopsis 2010 Genome  grant no. MCB-0929338  to G. C. , K. D. B. , and S. R. ), by the  National Institutes of Health  (grant no. R01-GM078270  to K. D. B. ), by an  International Fulbright Science and Technology Doctoral Award for Outstanding Foreign Students  (to D. R. ), by a European- FP7 - International Outgoing Fellowship, Marie Curie (AtSYSTM-BIOL ; grant no. PIOF-GA-2008-220157  to G. K. ), and by the  Human Frontier Science Program  (to U. R. ).
The CoRoT satellite has allowed us to conduct a large scale survey of photometric variability of NGC 2264. Thanks to the accurate high-cadence photometry and large field of view of CoRoT, we could study rotation and activity of about 8000 stars in a 3. 4 sq. degree region. The entire starforming region fits into a single CoRoT field of view, and the campaign resulted in continuous 23-day light curves for 301 known cluster members brighter than V≈ 16 (M=0. 3-0. 4 M⊙). The resulting optical broad-band light curves are the first accurate, highest cadence (32 or 512 seconds), longest duration, data set available for ≈ 3 Myr old stars. So far, the CoRoT NGC 2264 data have been used to study the correlation between optical and X-ray variability in young stars  (Flaccomio et al. 2010) ; asteroseismological properties of two high mass cluster members  (Zwintz et al.
COROT OBSERVATIONS The first short run CoRoT observations (SRa01, P. I. F. Favata) lasted from March 7 to  March 31, 2008 (23 days)  and was devoted to the observation of the very young (∼ 3 Myr) stellar open cluster NGC 2264, which covers most of the mass sequence from ∼ 3 to ∼ 0. 1 M⊙. A total of 8150 stars were observed, with right ascension (RA) between 99. 4 • and 100. 9 • and declination (DEC) between 7. 6 • and 10. 3 • and R magnitudes from 9. 2 to 16. 0. The sample includes 301 cluster members (see Sec. 3 for details regarding membership criteria). We used the so called N2 data delivered by the CoRoT pipeline  (Samadi et al. 2007)  after correction of the electronic offset, gain, electromagnetic interference, and outliers. The pipeline includes background subtraction and partial jitter correction.
g. Mathews et al. 2010; Dent et al. 2013). Single PACS scan maps at 70 µm and 160 µm (OB-SID 1342188485) were obtained on 2009-12-23 using the medium scan speed (20 ′′ s -1 ). The maps consisted of 8 scan legs with 3 ′ lengths and 5 ′′ cross-scan steps. The on-source exposure time for each map was 72 sec. We also obtained three PACS spectroscopy observations in chop-nod mode. The first was a RangeSpec observation that included 6 small wavelength ranges centered on the following transitions: CO 73 µm, H All PACS data were calibrated with HIPE v8. 2, using pipeline calibration scripts  (Ott 2010). Final scan maps for each wavelength were generated with two different pixel scales: one with 1 ′′ pixels for use in resolved imaging analysis (Section 3. 1) and one with the native pixel scale of the PACS detectors (3.
For each wavelength, the background was estimated in a sky annulus 60 ′′ -90 ′′ from the star center. Aperture corrections were applied and the fluxes color-corrected for a F ν ∝ ν 2 point-source spectrum. 15 The total 49 Cet fluxes at 250, 350, and 500 µm are F 250 µm = 0. 372 Jy ± 0. 027 Jy, F 350 µm = 0. 180 Jy ± 0. 014 Jy, and F 500 µm = 0. 086 Jy ± 0. 009 Jy. The flux uncertainties include statistical and calibration uncertainties; since 49 Cet is relatively bright, the latter dominate. For use in the 49 Cet SED and to correct the Spitzer IRS long-low spectrum, we also calculated the continuum flux at 24 µm from archival Spitzer MIPS imaging taken as part of the MIPS-GTO program DISKLEGACY (PI: G. Rieke, AOR# 21942016). The post-BCD image was downloaded from the Spitzer Heritage Archive and aperture photometry performed using the aperture size and correction from  Su et al.
Assuming local thermal equilibrium (LTE), x u is given by x u = (2 J u + 1) e -Eu/kTex Q Tex , (3) 18 NIST Atomic Spectra Database: http://www. nist. gov /pml/data/asd. cfm where J u is the angular momentum quantum number of the upper level, E u is the energy of the upper level, T ex is the excitation temperature, and Q Tex is the partition function for the given excitation temperature. Figure  3  shows a plot of the C II mass as a function of the assumed excitation temperature. The lower limit on the mass is M CII 2. 15 × 10 -4 M ⊕ , valid for T ex between 1 K and 2000 K. This value is close to the total CO mass calculated from the observed sub-mm CO emission (M CO = 2. 2 × 10 -4 M ⊕ ;  Hughes et al. 2008 ). Our upper limits on the CO 73 and 90 µm line fluxes do not provide useful checks on the CO abundance or excitation temperature.
The data used were the B-and Vband fluxes from the Tycho-2 Catalog, the J, H, and K s fluxes from 2MASS, and the color-corrected WISE Band 1 flux. No interstellar reddening was applied to the models. The best-fitting model had an effective temperature T eff = 9000 K and also matched the short wavelength portion of the IRS spectrum well. This temperature is typical of an A1V star but slightly cooler than the 9500 K effective temperature in  Montesinos et al. (2009). However, this fitting procedure could not provide a useful constraint on the stellar gravity. Using the new effective temperature and age estimate, we placed the star on a log glog T eff H-R diagram, forcing it to lie precisely on the 40-Myr isochrone. We used the evolutionary tracks and isochrones with solar metallicity from the Yonsei-Yale group  (Yi et al.
CONCLUDING REMARKS The Herschel far-IR imaging and spectroscopy of 49 Cet has provided an extremely rich dataset for better characterizing both the dust and gas. Our primary findings are as follows. 1. In our 70 µm image, dust continuum emission from the outer disk is spatially resolved for the first time. The disk orientation agrees with that determined from spatially and spectrally resolved submm CO emission, suggesting the gas and dust are co-spatial. The deconvolved image does not show a central clearing in the dust; the Gaussian HWHM along the disk major axis is ∼ 200 AU. , giving an ionized carbon gas mass M CII 2. 15 × 10 -4 M ⊕. With various assumptions, this suggests a lower limit on the total atomic gas mass of M a 0. 15 M ⊕. 3. Simple modeling of the new SED confirms the twocomponent structure of the disk.
We find blackbody temperatures of 175 K for the inner dust and 62 K for the outer dust, implying characteristic radii of 11 AU and 84 AU. 4. Preliminary thermochemical modeling of the combined gas and dust disk indicates that a "primordial-like" protoplanetary disk model cannot simultaneously reproduce all the gas observations. The gas may instead be secondary material coming from the destruction of comet-like ices. The next step in our on the 49 Cet system is fitting the SED with an advanced dust disk modeling code (GRaTeR;  Augereau et al. 1999). This code includes realistic grain properties and can take into account the powerful constraints provided by resolved imaging, as was done in our modeling of the HD181327 debris disk  (Lebreton et al. 2012). The new dust disk model will provide a more accurate dust mass and can be input to ProDiMo for further exploration of the disk parameter space.
Herschel is an  ESA  space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from  NASA. Support for this work was provided by the  NASA Herschel Science Center  through an award issued by  JPL/Caltech. A. Roberge  also acknowledges support by the  Goddard Center for Astrobiology , part of the  NASA Astrobiology Institute. J. -C. Augereau  thanks the  CNES-PNP  for financial support. C. Eiroa ,  G. Meeus , and  B. Montesinos  were partly supported by  Spanish  grant  AYA 2011-26202. Facilities: Herschel (PACS, SPIRE), Spitzer (IRS, MIPS), Hipparcos, CTIO: 2MASS , WISE, IRAS, JCMT, SMA.
and the ice dynamics through a water-dependent friction law. The hydrological part of the model follows a double continuum approach which relies on the use of porous layers to compute water heads in inefficient and efficient drainage systems. This method has the advantage of a relatively low computational cost that would allow its application to large ice bodies such as Greenland or Antarctica ice-streams. The hydrological model has been implemented in the finite element code Elmer/Ice, which simultaneously computes the ice flow. Herein, we present an application to the Haut Glacier d'Arolla for which we have a large number of observations, making it well suited to the purpose of validating both the hydrology and ice flow model components. The selection of hydrological, under-determined parameters from a wide range of values is guided by comparison of the model results with available glacier observations.
, 2012; Hewitt et al. , 2012; Werder et al. , 2013). In our approach, a sediment layer is used to model the inefficient drainage system (IDS), and, rather than actually modelling a network of channels to represent the efficient drainage system, we use an equivalent porous layer (EPL). This approach, known as the dual continuum porous equivalent approach in hydrogeology has been developed for karstified aquifers  (Teutsch and Sauter, 1991). Karstified and glaciological hydrological systems share some distinctive features that motivates this approach. Specifically, they both consist of systems with an inefficient drainage component and a more efficient one which is activated only under some water head conditions  (Hubbard and Nienow, 1997; White, 2003). The model is tuned and validated by performing a series of three experiments of increasing complexity using data obtained on the Haut Glacier d'Arolla.
Back Close Full In this paper, the hydrological model and its coupling to an ice flow model are validated by performing a series of three applications of increasing complexity using the dataset of Haut Glacier d'Arolla. A first application aiming for a steady state configuration, corresponding to the winter state, is used to decrease the range of possible values for the three most poorly constrained parameters of the model, i. e. the transmitivity of both drainage systems and the leakage factor. In the second experiment, the evolution of the drainage system during the spring season is studied, starting from the previously obtained winter steady-state. Again, the model sensitivity to the three most poorly constrained parameters is tested. The third and last application couples the hydrological and ice flow models and results are compared to observed glacier speeds.
Statistical analysis This animal protocol of nat. U contamination used two groups each of 10 rats. Health diagnostic values were reported as means ± standard errors of the means (SEM). The control and contaminated groups were compared with student's t test in normal populations or the rank sum test in nonnormal populations. Statistical significance was defined by a P value \ 0. 05. Statistical analyses were performed with Sigmastat statistical software (SPSS, Paris, France).
Sample preparation Urine samples were diluted immediately before analysis. After centrifugation for 15 min at 11,000 rpm and 4 °C, 100 lL of thawed urine was mixed and shaken for 1 min with 400 lL of deionized water. After a second centrifugation for 5 min at 3,000 rpm, 50 lL of supernatant was transferred into HPLC vials for analysis. To check for data quality, a blank sample (deionized water) and a pool sample (a mixture of all samples) were extracted/diluted and analyzed repeatedly along with the sample series.