dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP Sets and SNP Selection We explored different choices of SNP sets by using the UK reference panel. Initial exploration with large numbers of SNPs (e. g. , all 1,078 SNPs in the extended KIR region) showed that those with the highest variable-importance scores were concentrated closest to the KIR genes. We therefore focused on 305 SNPs that had genomic coordinates (GRCh37) between 55. 1 and 55. 5 Mb and were polymorphic in the UK reference panel. Using this smaller set did not reduce accuracy (data not shown). Of these 305 SNPs, four were eventually excluded because of poor clustering of genotype intensities (see below). We refer to the resulting 301 SNPs as the UKsnps set and the full 305 as the UKplus4snps set. The cross-validation analyses were done with the UKsnps set. The independent validation analysis required SNPs present in both panels.
Other SNP Genotyping Arrays To assess imputation performance on using SNP data from genotyping arrays other than the Immunochip, we fitted models by using a subset of the SNPs corresponding to those that could be typed on each specific array. Specifically, we obtained lists of SNPs for many Illumina and Affymetrix arrays (from their respective websites; see Web Resources). For each array, we took the intersection between the SNPs on the array and those in the UKsnps set (by matching on the GRCh37 genomic coordinates) and used the UK reference panel to fit a model with just those SNPs. This mimics the (bestcase) scenario, where all the SNPs in the intersection are perfectly typed in a study sample. In practice, typing for some SNPs will most likely fail, leading to possibly lower imputation accuracy.
Acknowledgments We wish to thank  Chris Johnson ,  Wei Jiang , and  Jyothi Jayaraman  for their contributions to developing the lab-based KIR typing process and data generation,  Sandra Maksimovic  for assisting with the web-based implementation,  Bente Woldseth  for technical assistance, and  Johannes Hov  for helpful discussions. This work was supported by  Australian National Health and Medical Research Council (NHMRC)   Career Development Fellowship ID 1053756  (S. L. ),  Victorian Life Sciences Computation Initiative (VLSCI)  grant  VR0240  on its  Peak Computing Facility at the University of Melbourne , an initiative of the  Victorian Government of Australia  (S. L. ),  UK Multiple Sclerosis Society  grant  894/08  (S. S. ), the  German Ministry of Education and Research (BMBF)  program e:Med sysINFLAME (A.
Calibration Nominal performance is only possible in space borne operation. Consequently, the procedures on ground will be limited to calibrate the response of photodiodes, magnetometers, thermistors, the output of control electronics, and the CCD cameras for detection system, also with respect to interspecies cross talk. The source operation will be initialized with reduced performance on ground. Subsequently, these parameters will be the preset for further optimization in space. During the first 4 months of the mission the source will be autonomously optimized with a differential evolution algorithm, beam splitter parameters will be adjusted, and systematics will be assessed. After entering the phase of nominal operation, science data will be taken during the perigee pass (0. 5 h), a null measurement at apogee (0.
In this study we consequently consider thermal boundaries and requirements as derived by the STE-QUEST mission  [17]. The high amounts of waste heat produced by the electronics and magnetic coils have to be transported to the satellite's heat sink. In order to create a robust and effective system, the TCS is designed as a complete passive system. The main challenge is the management of the cyclical instrument operation and the resulting fluctuating heat fluxes. In order to develop a TCS, which fulfills these requirements, a finite-element (FE) model is created for validation on its thermal stability. The analysis is performed with the FE-software ANSYS Classic  [47]. Due to the high complexity of the atom interferometer physics package, the FE-model can only be an idealized representation of the physics package.
Electronics Units The electronics consists of the following five functional units: 1. DMU (data management unit): This main digital control unit is based on the LEON processor developed under an ESA program -the LEON 2 processor (as e. g. used for all ESA instruments on the EarthCARE satellite) is available as a standalone device. Speeds up to 85 MHz are possible with the current version of the LEON device. The DMU also contains data acquisition electronics and housekeeping systems. It includes e. g. interfaces to photodiodes monitoring the fiber harness, to thermistors and to the CCDs. This unit controls all the other electronics units and the overall payload. 2. Magnetics Drive: This is a low voltage power supply providing low noise current drives for the magnetic field generation. These need to balance the need for low noise but high efficiency.
Architecture and design The AI software for Command, Control and Data Processing consists of two separated subsystems: the so-called OnBoard Software, being the Experiment Operations and Control Software, running on the S/C Computer, also called OnBoard Computer (OBC), and the ICU Software running on the DMU. The ICU Software is split into the Boot (or Basic) Software (BSW), in charge of the initialisation and troubleshooting of the DMU and the Application Software (ASW), based on RTEMS [54] operating system, implementing the required science computations, control and data management for the rest of AI electronics units. BSW is stored in the PROM, providing the minimum functionality necessary to: § assess and report on the overall DMU hardware health status, § establish a reliable communication link with the OBC, implementing an adequate subset of the Packet Utilization Standard (PUS) protocols ( ECSS-E-70-41A), § check and provide access to RAM and EEPROM memory (where the ASW shall be stored), § allow remote patching of Application Software.
Acknowledgements This work was supported by the  German space agency Deutsches Zentrum für Luft-und Raumfahrt (DLR)  with funds provided by the  Federal Ministry of Economics and Technology  under grant numbers  50 OY 1302 ,  50 OY 1303 , and  50 OY 1304 , the  German Research Foundation (DFG)  by funding the  Cluster of Excellence "Centre for Quantum Engineering and Space-Time Research (QUEST) ", the  French Space Agency Centre National d'Etudes Spatiales , and the  European Space Agency (ESA). Lluis Gesa ,  Ignacio Mateos  and  Carlos F. Sopuerta  acknowledge support from Grants  AYA-2010-15709  (MICINN) and 2009-SGR-935 (AGAUR). Kai Bongs  acknowledges support from  UKSA  for the UK contribution. Baptiste Battelier ,  Andrea Bertoldi  and  Philippe Bouyer thank the "Agence Nationale pour la Recherche " for support within the  MINIATOM  project ( ANR-09-BLAN-0026 ).
Laboratory canal The UPC-PAC canal (Canal de Prueba de Algoritmos de Control -Universitat Politècnica de Catalunya) is specially designed to develop basic and applied research in the field of control of irrigation canals. The canal is 0. 44 m wide and 220 m long and has zero slope. In this work the canal is configured to have three pools (see Fig. 1 ), and each pool is separated with a motorized sluice gate. The gravity offtakes are located at the downstream end of each canal pool. The SCADA system was developed in Matlab/Simulink environment, which allows the test of any control algorithm developed in Embedded Matlab language  (Mathworks, 2008). The UPC-PAC is short, completely affected by backwater. The low friction and the zero slope enhance the appearance of resonance waves. This phenomenon in the laboratory canal has been previously studied  (Horváth, 2013; van Overloop, Horváth, & Aydin, 2014).
Test cases for the control algorithms Several scenarios are established in order to test the predictive controllers during this work. Numerical simulations have been carried out by the SIC software (Simulation and integration of control for canals)  (Malaterre, 2012 ) that solves numerically using finite differences the Saint-Venant equations. For the physical tests in the experimental canal UPC-PAC, all the algorithms have been implemented using  Matlab (Mathworks, 2008). All tests start and finish with the steady state conditions as shown in Table  1. The discharge ðQ appr Þ is approximately 60 l/s. The setpoint of the water level in the first pool (Sp1) is 85 cm, in the second pool (Sp2) is 70 cm and in the third pool (  Sp3 ) is 55 cm. The gate openings to achieve the given water levels in each pool are shown for the three gates (G1, G2 and G3).
In this work, we propose to further develop this observation-based technique to estimate SWS variations in rivers, floodplains, lakes and wetlands of the GB basin for the period 2003-2007, and together with GRACE measurements, to quantify the "subsurface water storage" change (SSWS, i. e. , the sum of GWS and SMS changes). Section 2 presents the datasets used in this study. In Section 3, we discuss the methodology for deriving SWS from a combination of multi-satellite observations. In Section 4, the results are presented and discussed over the 2003-2007 period. An evaluation is performed comparing the new estimates with other external datasets such as GRACE-derived TWS, in situ river discharge observations and precipitation. Finally, conclusions and perspectives are presented in Section 5.
Dataset and evaluation 2. 1. Multi-satellite-based surface water extent dynamic (GIEMS) The complete methodology that captures the extent of episodic and seasonal inundation, wetlands, rivers, lakes and irrigated agriculture, at the global scale, is described in detail in  Prigent et al. (2001 Prigent et al. ( , 2007 Prigent et al. ( , 2012) )  and  Papa et al. (2006 Papa et al. ( , 2010a)). The technique, which we summarize here, uses a complementary suite of satellite observations covering a large wavelength range: 1) Advanced Very High Resolution Radiometer (AVHRR) visible (0. 58-0. 68 m) and near-infrared (0. 73-1. 1 m) reflectances and the derived Normalized Difference Vegetation Index (NDVI); (2) passive microwave emissivities between 19 and 85 GHz. These are estimated from the Special Sensor Microwave/Imager (SSM/I) observations by removing the contributions of the atmosphere (water vapor, clouds, rain) and the modulation by the surface temperature  (Prigent et al.
, 2001 (Prigent et al. , , 2007)). As the microwave measurements are also sensitive to the snow cover, snow and ice masks are used to filter the results and avoid any confusion with snow-covered pixels  (Armstrong and Brodzik, 2005). Because the ERS scatterometer encountered serious technical problems after 2000, the processing scheme had to be adapted to extend the dataset and monthly mean climatology of ERS and NDVI-AVHRR observations are used  (Papa et al. , 2010a; Prigent et al. , 2012). Fifteen years of global monthly water surfaces extent for the period 1993-2007 are available  (Prigent et al. , 2012). This dataset has been extensively (i) evaluated at the global scale  (Papa et al. , 2008a (Papa et al. , , 2010a;; Prigent et al. , 2007)  and for a wide range of environments  (Papa et al.
The spatial distribution of GIEMS was extensively evaluated against high-resolution (100 m) SAR images in  Prigent et al. (2007)  and in  Aires et al. (2013)  over the Amazon basin leading to an overall GIEMS uncertainties of ∼10% for GIEMS. Over the SIC (and especially GB), the spatial distribution of GIEMS basin was evaluated against static surface water dataset (Global Lake and Wetland Dataset, GLWD-3,  (Lehner and Doell, 2004) ) and other related hydrological variables (precipitation, altimeterderived river heights, river discharge) in  Papa et al. (2006)  and in  Prigent et al. (2012) , as well as using other regional surveys representing various components of wetland and open-water distributions  (Adam et al. , 2010). Seasonal and interannual variations in surface water extent and associated anomalies over the GB Basin are presented in Fig.
(2010a) ). To overcome this issue, we use here external information on flood coverage from the Dartmouth Flood Observatory database (http://floodobservatory. colorado. edu) that provides surface water extent observations derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) for the period 2002-2012. In particular, we use the MODIS-derived map of maximum inundation over the GB to set a maximum threshold on GIEMS observations: when a GIEMS pixel has a value higher than the one from the MODIS-derived map of maximum inundation, the GIEMS value is replaced by the MODIS-derived maximum inundation value.
Given the orbit configuration, there are several intersections between the satellites ground tracks and the Ganges and Brahmaputra rivers or their major tributaries. The intersections where water levels can be derived are termed as "virtual stations". Fifty-eight of them were built in the GB basin using ENVISAT RA-2 measurements and their locations are shown in Fig. 3. Twenty were already available from the Hydroweb database, providing time series of water levels of large rivers, lakes and wetlands around the world (http://www. legos. obs-mip. fr/en/soa/hydrologie/hydroweb/). Following the same procedure, we constructed this dataset with 38 other virtual stations for which we derive ENVISAT time series of water stage variations for each pass using the Virtual ALtimetry Station software (VALS, 2010, Virtual ALtimetry Station Software, Version 0.
Therefore, here we use the range values calculated by the Ice-1 retracking algorithm. Finally, river height water levels are referenced to the Earth Gravitational Model EGM2008  (Pavlis et al. , 2008)  with respect to WGS 84 reference (The World Geodetic System 1984, http://earth-info. nga. mil/GandG/wgs84/). It is important to note that, in terms of single river level height measurements, there are several other factors, beside the choice of a certain tracker to retrieve the altimetric range or the river width, that could introduce uncertainties in the height measurements: for instance, the precision of the estimated satellite orbit or the uncertainties in datasets used to correct atmospheric contributions can also have a non-negligible impact. However, uncertainties/errors due to these factors will not be evaluated nor discussed in this paper.
GRACE data The Gravity Recovery And Climate Experiment (GRACE) mission, launched in March 2002, provides measurements of the spatio-temporal changes in Earth's gravity field. At basin scale, GRACE data can be used to derive the monthly changes of the total land water storage  (Ramillien et al. , 2005; Landerer and Swenson, 2012)  with an accuracy of ∼1. 5 cm of equivalent water thickness when averaged over surfaces of a few hundred square-kilometers. In this study, we use monthly GRACE solutions from the Geo Forschung Zentrum (GFZ), the University of Texas Center for Space Research (UTCSR) and the Jet Propulsion Laboratory (JPL) from February 2003 (data are missing for January 2003) to December 2007 in order to analyze the time variations of the water mass changes in the GB basin. Unfortunately, the GRACE solutions suffer from the presence of an unrealistic high frequency noise corresponding to north-south striping that is caused by orbit resonance during the Stokes coefficients determination and aliasing of poorly modeled short-term phenomena.
Precipitation estimates from TRMM 3B43 We used the Tropical Rainfall Measuring Mission (TRMM) 3B43 v7 product which is a combination of monthly rainfall at a spatial resolution of 0. 25 • and currently available from January to December 2013. This dataset is obtained by combining satellite information from the passive TRMM Microwave Imager (TMI) and Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM), a Japan-U. S. satellite launched in November 1997, the Visible and Infrared Scanner (VIRS) onboard the Special Sensor Microwave Imager (SSM/I) and rain gauge observations. The dataset results from the coupling of the TRMM 3B42-adjusted merged infrared precipitation with the monthly accumulated Climate Assessment Monitoring System or Global Precipitation Climatology Center Rain Gauge analyses  (Huffmann et al.
Fig. 4  shows the ENVISAT-derived times series of river level height at Hardinge and Bahadurabad (estimated in Section 2. 2) and the comparison with the in situ river level measurements at each station. Note that these two datasets are independent. Since altimeter-derived water level heights are expressed with respect to a geoid model, there is no common height reference when compared to in situ water level heights, resulting in a natural difference in absolute values. A large seasonal cycle is observed with annual height variations exceeding 8 m for both rivers. For the period of common availability, times series (every 35 days for the altimeter-derived observations) show a very good agreement (Fig. 4a  and c ) and have a similar behavior in the peak-to-peak height variations. Over the Ganges, from 2006 to 2010, there are 38 dates where measurements are simultaneously available (i.
e. , plus or minus two day apart). Similarly, for the Brahmaputra, there are 29 measurements simultaneously available for the ENVISAT-derived and the in situ height observations. Fig. 4b  and d  show the relationship between the satellite-derived river height and the in situ observations, confirming the good agreement between the two data sets with a correlation of 0. 98 (p-value (hereafter, p) <0. 01) and 0. 99 (p < 0. 01) for the Ganges and the Brahmaputra, respectively. We estimated the standard error to be of ∼0. 36 m at Hardinge and ∼0. 29 m at Bahadurabad, which is typically in the range of accuracy of altimetric observations over large rivers (0. 1-0. 2 m for instance over the Amazon  (Frappart et al. , 2006; Birkett et al. , 2002) ). These results are similar to the 0. 28 m (resp. 0.
Methodology to estimate the surface water storage The method used to estimate surface freshwater storage consists of the combination of the surface water extent from GIEMS (Section 2. 1) with altimeter-derived water level heights estimated at 58 virtual stations (Section 2. 3. 1). Here we use both dataset during their coincident years of availability, the period of 2003-2007. The two-step methodology is described in details in  Frappart et al. (2011 Frappart et al. ( , 2012) )  and is briefly summarized below.
90). Furthermore, the lagged correlation between the SWS and basin-averaged precipitation also high (>0. 8), with rain leading the SWS changes by 1 month. The normalized deseasonalized anomalies (computed by subtracting the 5-year mean monthly value from the monthly time series) also show good agreement in the temporal patterns between the four variables (Fig. 8b, d  and f ). and human activities, as well as on the regional climate and biogeochemical and carbon cycles. Our new dataset now offers the opportunity to study the signatures of such droughts on the dynamics of SWS. For instance, we estimate that the amount of surface water stored in the entire GB basin during July-September 2006 was about ∼60 km 3 that is ∼30% below for the July-September months compared to 2003-2007 average, with a maximum deficit of ∼35% in SWS for the Brahmaputra River basin.
Disaggregation of GRACE-derived TWS into satellite-derived Sub-Surface Water Storage variations (SSWS) is carried out following equation (1) and using our new SWS estimates. SSWS is defined as the sum of GWS and SMS changes. We assume that changes in freshwater storage derived from ice and snow (ISS) are negligible and hence not considered here. The choice of estimating the sole sub-surface water storage variations instead of disaggregating this term into GWS and SMS changes is driven by the poor quality and the large uncertainties associated with existing satellite-derived (AMSR-E, SMOS, representative of the very first cm below the surface) and land-surface model outputs of soil moisture over the GB basin. Over Bangladesh, for instance,  Shamsudduha et al. (2012)  reported large differences in the SMS annual amplitudes in three commonly used land-surface models (CLM, NOAH, VIC) with differences up to 300% among the datasets.
Conclusion and perspectives This study presents an observation-based dataset that quantifies basin the monthly distribution and variation of surface freshwater storage for the entire Ganges-Brahmaputra basin, at ∼25 km spatial resolution over  5 years, 2003-2007. The method applied here is based on the combination of surface water extent from a multi-satellite technique (GIEMS) and water bodies level height variations from radar altimetry. A total of 58 time series of water level variations were constructed using the ENVISAT radar altimeter observations over the Ganges and Brahmaputra Rivers. A direct comparison with in situ river level variations at two stations, Hardinge on the Ganges and Bahadurabad on the Brahmaputra, confirms that ENVISAT radar altimeter can adequately monitor the river stage variations of these two rivers.
The new estimates of monthly SWS show realistic spatial distribution over the entire GB. The associated temporal variations exhibit a strong seasonal cycle and substantial interannual variability. The mean annual amplitude of ∼410 km 3 in SWS over the GB Basin contributes to approximately 45% of the annual variations of GRACE-derived TWS. Annual SWS is estimated to be ∼300 km 3 and ∼250 km 3 for the Ganges and Brahmaputra Basin, respectively. The monthly SWS variations for the period 2003-2007 are evaluated against other related hydrological variables such as satellite-derived river discharge, precipitation and GRACE observations, showing that the seasonal and interannual variations agree well among all variables. We demonstrate that this new dataset can help in quantifying the surface water deficit during an extreme hydrological year.
, 2011). This new and unique surface water storage and sub-surface water storage datasets for the GB basin over 5 years is a step toward a better understanding of hydrological and climate processes in the Indian Subcontinent region and their interactions with human activities (we note these datasets will be updated until present in the near future when global land surface emissivities become available along with the new CNES-ISRO radar altimeter AltiKa). In particular, this new dataset will help better characterize the continental processes during extreme events such as exceptional droughts in the GB basin, as well as to better quantify the causes and impacts of the actual "groundwater depletion" observed in the region. Regarding satellite observations, our new dataset will play a key-role in the definition and validation of future hydrology-oriented satellite missions such as the NASA-CNES SWOT (Surface Water and Ocean Topography) dedicated to surface hydrology  (Alsdorf et al.
INTRODUCTION Protein-nucleic acid (NA) interactions play crucial roles in a wide variety of functions ranging from transcription, translation, post-transcriptional/-translational modification and post-transcriptional/-translational regulation. An important step in understanding the recognition mechanism is to locate the functional residues on the RNA-/DNA-binding proteins (RBP/DBP) in an unbiased and systematic manner. This need is becoming even more critical with the massive outcome of biological sequence data  (1)  and the growing numbers of non-canonical protein-RNA interactions, such as chromatin regulatory factors that were not initially thought to be DBP  (2). Although computational prediction of functional residues is an established field, the question is far from being settled. The difficulty is compounded by the amazing diversity in protein recognition folds as well as in RNA conformational states.