dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AND METHODS Both conventional hydrological data and remote-sensing methods used here were acquired within the framework of the HYBAM program. Water levels, discharges and Suspended Sediment Concentrations (SSC) data were processed using Hydracess software  (Vauchel, 2007)  for standard hydrologic treatments and data analysis. Hydromesad allows us to combine discharge acquisition from Acoustic Doppler Current Profiler (ADCP) with SSC data. VALS software was used for remote-sensing altimetry data processing. Finally, GetModis and Mod3R allowed extraction of remote sensing reflectance from MODIS images  (Cochonneau, 2009). All these software can be downloaded at http://www. ore-hybam. org/index. php/eng/Software. In this work we used four conventional stations (LAG, PUC, REQ, PIN), six altimetric stations and three river masks defined for the assessment of MODIS reflectance at LAG, PUC and REQ (see Fig.
In this way, we were able to fit a rating curve between the Surface SSC from the observer and the cross-section Average SSC from the measurement. Using this data, Suspended Sediment Loads (SSL) were calculated using HYBAM software: Surface SSC were converted into Average SSC with the fitted rating curve, and the averaging method (SSL = Q × SSC) was used to calculate SSL, interpolating SSC when necessary. At PIN, as the period of sampling was reduced, we chose to fit a powerrating curve with parametric bootstrap method to assess the river sediment fluxes from the water discharge. At PUC, we built SSC series extracted from MODIS images using fitted relations between infrared reflectance and SSC at LAG and REQ. Consequently, we used framed SSC values at PUC (low values corresponding to REQ trend and high values to LAG trend) to calculate framed SSL values.
We assessed sedimentary production of the Cushabatay River, Pisqui River and Aguaytia River sub-basin by a regionalization method based on their Andean basin area. Based on the low SSY of the Orthon River compared to the high Andean production rates, we neglected the Fitzcarrald Arch contributions to the sediment budget. The whole water level dataset from the gauging stations has been screened and completed whenever needed by estimation through correlative methods and with continental altimetry techniques. Due to different periods of data acquisition, we chose a common period from September 2009 to August 2012.
CONCLUSIONS Results presented in this study relate to the 2009-2012 hydrological period. The denudation rates assessed are among the most rapid of the whole Andean Amazon River Basin. For the first time, a considerable retention of sediment was found in the Ucayali River basin. A sediment flux of 525 Mt year -1 is exported from the Andes in the Ucayali River floodplain, 220 Mt year -1 of which are deposited, mainly along the foreland lined by the Shira Mountains thrust system. Ucayali River sediment load represents about 36% of the Amazon River load delivered to the ocean. Resuspension of sediment previously trapped in the flood plain represents 13% of the total mass transported at the basin outlet. This information will be completed in the future with new data acquired within the HYBAM long-term monitoring programme.
In this configuration, Enhanced LIGO collected data from July 2009 to October 2010. Although no gravitational wave signals were detected with Initial or Enhanced LIGO, they produced several interesting astrophysical results; representative examples are listed in reference  [3]. Compared to Initial LIGO, Advanced LIGO is designed to provide a factor of 10 increase in strain sensitivity over a broad frequency band, and to extend the low end of the band to 10 Hz (from 40 Hz). As the probed volume of the universe scales as the cube of the strain sensitivity, this represents an enormous increase (of order 10 3 x) in the number of potential astrophysical sources detectable by these instruments. At design sensitivity, Advanced LIGO is likely to detect dozens of compact binary coalescence sources per year  [4].
To attain this performance, the GPL Linux operating system is used on the control computers, but with a LIGO custom kernel patch. This patch allows the isolation of a given CPU core from the Linux system for exclusive use by a real-time control program. Specific subsystem data acquisition and control programs are created with the aid of a real-time code generator (RCG). The RCG provides a set of custom software modules, as well as support of many MATLAB Simulink parts. Using Simulink as the GUI, code is developed as a graphical model that describes the desired execution sequence. Standard scripts then produce executable software from the model. A key RCG component is the IIR (infinite-impulse response) filter module, which is used to define control transfer functions and channel calibrations.
Acknowledgements The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory  and the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia, Hisenda i Innovació of the Govern de les Illes Balears , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Administration, OTKA of Hungary , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  National Science and Engineering Research Council Canada , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
We present a system of nonlinear ordinary differential equations used to quantify the complex dynamics of the interactions between tumor growth, vasculature generation, and antiangiogenic treatment. The primary dataset consists of longitudinal tumor size measurements (1,371 total observations) in 105 colorectal tumor-bearing mice. Mice received single or combination administration of sunitinib, an antiangiogenic agent, and/or irinotecan, a cytotoxic agent. Depending on the dataset, parameter estimation was performed either using a mixed-effect approach or by nonlinear least squares. Through a log-likelihood ratio test, we conclude that there is a potential synergistic interaction between sunitinib when administered in combination with irinotecan in preclinical settings. Model simulations were then compared to data from a follow-up preclinical experiment.
Overview We first performed in vitro experiments to investigate the cytotoxic effects of sunitinib on HT-29 cells. The same HT-29 cells have been successively implanted in nude mice and interstitial fluid pressure-a marker of vasculature normalization  10  -has been measured (see Supplementary Materials). We developed a model that consists of a system of nonlinear ordinary differential equations (ODEs) and describe tumor growth and angiogenesis. We analyzed a dataset consisting of longitudinal tumor size measurements (1,371 total observations) in 105 colorectal tumor-bearing mice. Mice received single or combination administration of sunitinib and/or irinotecan. In the combination groups, sunitinib was given once daily for 12 days, with a single dose of irinotecan at day 2 or day 15. Sunitinib is modeled as acting by reducing the carrying capacity of the tumor, while irinotecan directly reduces the size of the tumor.
In vivo experiment Experimental subjects were athymic male mice, 5-6 weeks of age, weighing about 20 g each. Mice were maintained in cages with filter paper covers, sterilized food and bedding, and acidified water. All animal experiments were conducted in accordance with the Guide for the Care and Use of Laboratory Animals (NIH publication #85 -23, revised 1985). HT-29 human colorectal adenocarcinoma cells were grown in advance, trypsinized, counted, washed, and resuspended in serum for subcutaneous injection into the flank of mice. The injection condition was 3. 0 3 10 6 cells in 200 lL. After injection, tumor measurements were made in each mouse once every 2-3 days for up to 9 weeks (Figure  1 ). Data collection consisted of three orthogonal tumor diameter measurements: length: l, width: w, and height: h, made using a slide caliper.
Parameter estimation The SAEM (Stochastic Approximation of the Expectation Maximization) algorithm was developed as an approach to solve the expectation maximization (EM) problem. Given a mathematical model, the goal is to determine the parameter values that maximize the corresponding likelihood function. When appropriate, we use a na€ ıve pooled mixed-effect approach. We utilize the SAEM algorithm implemented within the Monolix (Lixoft) software to estimate both population and individual parameters. When the data were not amenable to mixed-effect parameter estimation techniques, we used standard least squares regression with the median tumor size data to estimate typical parameter values. To obtain least squares parameter estimations, ordinary differential equation solvers were coupled with minimization algorithms from the standard library of MatLab (MathWorks, Natick, MA).
We thus assumed that when the drugs are given concurrently, k C is proportional to the cumulated exposure to sunitinib prior to the dose of irinotecan. The time of the administration of irinotecan is represented by the variable T C. The "interaction model" has the same representation as model (3) where: k C 5 k S Á exp ð T C 0 S t ð Þdt 0 @ 1 A (4) The exponential in expression 4 was chosen to produce a relation similar to that of an exponential covariate in the mixed-effect approach. While this approach is empirical in the current modeling context, should we be able to use the mixed-effect approach with a similar dataset in the future, we hypothesize that the exponential covariate would be the most likely relation between these two quantities. To estimate model parameters, we performed a new experiment described in Supplemental Table  S3.
Acknowledgments. The research leading to these results received support from  INSERM  (grant from Physicancer 2011). The work was also partly funded by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115156 , resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. The  DDMoRe  project is also supported by financial contributions from  Academic  and SME partners. This work does not necessarily represent the view of all DDMoRe partners. V C 2015 The Authors CPT: Pharmacometrics & Systems Pharmacology published by  Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Methods. We built a numerical code called COMETS using analytical formulae to perform a simple comparison of exoplanet and spot signals. We explored instrumental specificities needed to detect them, such as the required baseline length, the accuracy, and signalto-noise ratio. We also discuss the impact of exoplanet and spot parameters on squared visibility and phase: exoplanet diameter and size, exoplanet position, spot temperature, star diameter. Results. According to our study, the main improvement to achieve is the instrument sensitivity. The accuracy on squared visibilities has to be improved by a factor 10 to detect an exoplanet of 0. 10 mas, leading to <0. 5% precision, along with phase measurements of ∼5 • accuracy beyond the first null of visibility. For an exoplanet of 0. 05 mas, accuracies of ∼0.
The advantage of the visible domain lies first in the increased angular resolution for a given baseline, and second, the fact that it is the main domain used in photometry or in RV for indirect searches for planets. It is already used by several instruments that reach unprecedent angular resolution (up to 0. 3 mas): VEGA/CHARA  (Mourard et al. 2009; Ligi et al. 2013) , PAVO/CHARA  (Ireland et al. 2008)  and VISION/NPOI  (Armstrong et al. 1998). With the significant improvement in sensitivity and accuracy that visible interferometry has gained recently, it might be a good way to characterize spot and exoplanet signatures on interferometric observables and to separate the different signals. In Sect. 2, we describe a new numerical code called COMETS that allows modeling interferometric observables of a star hosting a magnetic spot and/or a transiting exoplanet.
Models of the objects COMETS (code for modeling exoplanets and spots) is a numerical code using analytical formulae developed to model the interferometric observables (visibilities, closure phases, or phases) provided by a star with a (dark) magnetic spot and/or a transiting exoplanet. The computation is made on a grid of a (u, v) plane with u and v ranging from -1 km/λ to 1 km/λ with a step du = dv smaller than 4 m/λ (refined if necessary). We use a wavelength λ of 720 nm, as used by VEGA instrument.
Conclusion We have computed the interferometric observables of a star with the new code COMETS and observed the variations induced by a transiting exoplanet and/or a magnetic spot in squared visibilities and phases. We showed that several parameters of these objects affect the baseline length required to detect their signal, such as the position, diameter, or spot temperature. Starting from the variation of the MBL as a function of the exoplanet angular diameter, we derived an analytical solution that allows calculating the baseline length required to detect the absolute exoplanet signature in squared visibilities. This directly shows the interest of using the visible wavelength over the infrared, and that visible interferometers are better adapted for exoplanet characterization. More importantly, current instruments use long enough baselines to reach the spatial angular resolution that can lead to a characterization of large exoplanets or very dark spots, but lack accuracy on squared visibility and phase (or CP) measurements.
Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM 10 (PM with aerodynamic diameter < 10 µm) limit values at individual air quality monitoring stations reporting to the Air-Base database.
However, a compact linear relation exists between the annual mean and the 36th highest daily average which is decisive for attainment of the daily limit value (see Fig. 1 , showing observations from the AirBase 1  database in 2009): a 36th highest daily average of 50 µg m -3 corresponds to an annual The limit on daily exceedances of 50 µg m -3 is well represented by an annual mean limit of 30 µg m -3. mean concentration of 29. 6 µg m -3. In a similar approach,  Stedman et al. (2007)  used a quadratic relationship between the number of days with PM 10 concentrations greater than 50 µg m -3 and the annual mean to derive an equivalent annual mean concentration of 31. 5 µg m -3. Hence we assess compliance with respect to an equivalent annual mean limit value of 30 µg m -3. As seen in Fig. 1 , within a certain range of annual mean concentrations both compliance and noncompliance with the daily limit value are possible.
Combination of the different modelling steps The different modelling steps are combined as indicated in Fig. 2. Model calculations are possible for every station in the AirBase database which fulfils a few data coverage criteria: for background stations, all stations with more than 80 % coverage of daily mean PM 10 concentration data are included. For roadside stations, in addition NO x data are required for the same station, and at least one suitable PM 10 and one NO x background station, ideally identical, are needed. All of these stations must fulfil the 80 % temporal coverage criterion. With these criteria, a total of around 1 870 PM 10 stations are covered by the model, of which 316 are traffic stations and 492 did not attain the equivalent limit value as defined in Sect. 2 in 2009 (315 if contributions from natural dust and sea salt are subtracted).
Uncertainties and caveats The simplifications needed in a Europe-wide modelling of PM down to individual street canyons lead to considerable uncertainty. A general limitation of this modelling approach is that it only provides concentration projections for monitoring stations for which AirBase data are available for 2009 and indeed only for a subset of stations for which the mentioned data criteria have been met. However, these locations are used for assessing compliance with the EU Air Quality Directive, and the model covers 80 % of the stations exceeding the limit value in 2009. Kiesewetter et al. (2014)  provided a thorough discussion of the uncertainties associated with the roadside NO 2 calculation scheme which follows a very similar approach. Hence, we only provide here a short discussion of the uncertainties specific to the PM scheme and refer the reader to the cited reference for a more general treatment.
Acknowledgements. This work was partially supported by the  EC4MACS  ( European Consortium  for the  Modelling of Air pollution and Climate Strategies ) project with the contribution of the LIFE financial instrument of the  European Community  (contract no. LIFE06 ENV/PREP/A/000006 ), as well as the Service Contract on Monitoring and Assessment of Sectorial Implementation Actions (contract no. 07. 0307/2011/599257/SER/C3 ) of  DG-Environment of the European Commission. Monitoring data used in this study were obtained from AirBase (version 5). Edited by:  F. Dentener.
Microarrays use known probes that will hybridize with the labeled sample and based on the intensity of these dyes, transcript levels are estimated. This however, implies that sequence information exists before generation of the microarray and this is a serious limitation when applied to nonmodel crops. The limited sequence availability in non-models can be overcome by the use of microarrays of closely related species or by the generation of a species-specific microarray based on known expressed sequence tag (EST) data for instance, but these analyses will be less informative  (Davey et al. , 2009; Pariset et al. , 2009). Microarray analysis is hampered by a high background noise due to cross-hybridization as well as saturation of signals. Microarrays therefore have a limited sensitivity and dynamic range.
In the gel based approach, proteins are separated and quantified via gel electrophoresis. The proteins of interest are then picked from the gel, digested and the resulting peptides identified via MS by comparing experimental versus theoretical masses present in various databases. This technique has the advantage that protein separation and analysis via (two-dimensional) electrophoresis prior to MS analysis ensures physical connectivity between the peptides and the protein and significantly reduces complexity  (Carpentier et al. , 2008b). Currently, it is still the most widely used approach in crop proteomics. Unfortunately the technique has some major drawbacks, i. e. , it has a very poor performance when analyzing hydrophobic and basic proteins and can be quite limited with respect to throughput.
To increase the chance of identifying specific tryptic peptides, it is important to ensure a good peptide separation and to keep the mixture of co-ionizing peptides as simple as possible even in fast modern MS. Vertommen et al. (2011a)  proposed a workflow for banana samples where this was achieved by using a two-dimensional RP-RP chromatography system coupled to a high accuracy MS. To identify the peptides in a gel free approach for a crop, it is important to build a species specific in-house database and to search this database in consecutive steps: a non-error tolerant manner, subsequently an error tolerant and finally de novo sequencing. This de novo approach is a crucial step, since it allows the identification of variety specific peptides via a homology search of sequences instead of a search based on m/z values  (Vertommen et al.
, 2011a). To identify and quantify peptides in a rapid, consistent, reproducible, accurate and sensitive way, data independent acquisition (DIA) protocols have been developed for label-free shotgun proteomics as an alternative to DDA  (Plumb et al. , 2006). To increase the identification rate of label free DIA experiments for samples from the apple variety Braeburn, a new workflow was developed by  Buts et al. (2014)  where a DDA database was constructed and linked to the DIA data. A ten-fold increase in peptides was identified from a single DIA run and proteins correlated to the storage quality of apples were found. While in sequenced crops, two-dimensional electrophoresis (2DE) may currently no longer be the tool of choice for highthroughput differential proteomics, it is still very effective in identifying and quantifying protein species as a result of genetic variations, alternative splicing and/or PTM.
Metabolomics In the 'omics field, metabolomics generates large datasets for the identification and quantification of small molecules. Usually, the approach is undertaken for the high throughput detection of secondary (flavonoids, sugar-phosphates, phytohormones, phytoalexins, etc. ) and primary metabolites (sugars, organicand amino acids, etc. ). Complementary MS-based LC and GC approaches are required to adequately profile this metabolic diversity  (Scherling et al. , 2010). Although it is possible to gather tens of thousands of metabolic variables, their accurate identification remains the major bottleneck in metabolomics studies to date. Like for proteins, due to the large dynamic range and the difference in physico-chemical properties, detecting the entire "metabolome" is not possible.
In contrast to proteomics, for metabolomics analyses, functional identification is not dependent on the availability of genome sequence information. However, the availability of purified standard substances and/or spectral libraries/databases is necessary. Of the various MS methods, two profiling strategies can be typically distinguished: a targeted and a non-targeted approach  (Wienkoop et al. , 2008 (Wienkoop et al. , , 2010)). The non-targeted technique allows for the quantitative evaluation of unknown, yet unidentified, variables. Nevertheless, metabolomics databases and the availability of MS-derived metabolite fragment information are increasing. Furthermore, novel strategies for pathway and structural assignments of untargeted high-throughput metabolomics data are being developed. For example, an algorithm termed mzGroupAnalyzer was developed to investigate metabolite transformations caused by biochemical or chemical modifications, and the approach led to the identification of novel molecule structures  (Doerfler et al.