dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This publication was made possible by grants from the  Qatar National Research Fund  under its  National Priorities Research Program  award number  NPRP 09-1174-3-291 ,  NPRP 4-640-1-096 , and  NPRP 09-1087-3-274. Its contents are solely the responsibility of the authors and do not necessarily represent the views of the  Qatar National Research Fund. We thank the  Flow Cytometry Facility  within the Microscopy Core at  Weill Cornell Medical College in Qatar (WCMCQ ) for contributing to this study. The Core is supported by the " Biomedical Research Program  at  Weill Cornell Medical College in Qatar ", a program funded by  Qatar Foundation. We acknowledge  Drs. Imaad Mujeeb  and  Raphael Lis 's assistance in providing us with human breast tumor FFPE sections and human xenograft tumor sections, respectively.
The delineation extends further into the coastal waters in such a way that the Scotian Shelf and the Gulf of Maine correspond to the North section and the Mid-Atlantic Bight and Georges Bank to the South section. The riverine data are calculated from pH and alkalinity measurements extracted from the GLObal RIver CHemistry Database (GLORICH  (Hartmann et al. , 2014) , previously used in  Lauerwald et al. , 2013) , while continental shelf values are calculated from the Surface Ocean CO 2 Atlas (SOCAT v2. 0) database which contains quality controlled direct pCO 2 measurements (http://www. socat. info/, Bakker et al. , 2014).
Values of A were not calculated for each individual month, as the dischargestream width relationship only hold true for Q ann  (Raymond et al. , 2013). Q ann was obtained using HydroSHEDS 15 s to route the gridded data of average annual runoff from the UNH/GRDC composites  (Fekete et al. , 2002). ln(B[m]) = 2. 56 + 0. 423 × ln(Q ann [m 3 s -1 ]) (Eq. 2 after  Raymond et al. , 2012) , ln(B[m]) = 1. 86 + 0. 51 × ln(Q ann [m 3 s -1 ] (Eq. 3 after  Raymond et al. , 2013) , where B is stream width [m] and Q ann is annual average discharge [m 3 s -1 ]. For each 15 s raster cell covered by lake and reservoir areas as represented in the global lake and wetland database of  Lehner and Döll (2004) , A was set to 0 km 2. A eff was then derived from A to account for seasonal stream drying and ice cover inhibiting F CO 2.
Information on altitude was derived from the HydroSHEDS elevation model. V was calculated from Q ann based on the equations of  Raymond et al. (2012 , 2013) (Eqs. 5, 6). Similarly to the stream width, the V -Q relationship only holds true for Q ann  (Raymond et al. , 2013) , and this is why only annually average values for V and k 600 could be Figure  1. Geographic limits of the study area with the location of the riverine (GLORICH database, in green;  Lauerwald et al. , 2013)  and continental shelf waters data used for our calculations (SOCAT 2. 0 database, in red;  Bakker et al. , 2014). The location of the estuarine studies used is indicated by purple squares. calculated. The k value for each month was calculated from k 600 , an estimate of the average monthly water temperature T water  (Lauerwald et al.
, 2015; Raymond et al. , 2012). k 600 [m d -1 ] = V [m s -1 ] × CS[1] × 2841 + 2. 02 (Eq. 4 after  Raymond et al. , 2012) , ln(V [m s -1 ]) = -1. 64 + 0. 285 × ln(Q ann [m 3 s -1 ]) (Eq. 5 after  Raymond et al. , 2012) , ln(V [m s -1 ]) = -1. 06 + 0. 12 × ln(Q ann [m 3 s -1 ]) (Eq. 6 after  Raymond et al. , 2013) , where k 600 is the standardized gas exchange velocity for CO 2 at 20 • C water temperature [m d -1 ], Q ann is annual average discharge [m 3 s -1 ], V stream flow velocity [m s -1 ], and CS channel slope [dimensionless]. Values of (CO 2 ) were derived from monitoring data with calculated pCO 2river (12 300 water samples, from 161 locations,  Lauerwald et al. , 2013) , and an assumed pCO 2atmosphere of 390 µatm. Lauerwald et al. (2013)  calculated pCO 2river values from pH, alkalinity, water temperature, and, where available, major ion concentrations, using the hydrochemical modeling software PhreeqC v2  (Parkhurst and Appelo, 1999).
Estuaries The yearly averaged CO 2 exchange at the air-water interface was obtained from local estimations of emission rates in seven estuaries located within the study area (see Table  1 ). The limited number of observations does not allow us to resolve the seasonality in CO 2 emissions. The yearly average local CO 2 emission rates range from 1. 1 molC m -2 yr -1 in the Parker River to 9. 6 molC m -2 yr -1 in the Hudson River estuary, for a mean value of 4. 2 molC m -2 yr -1 for the seven systems. This value was then multiplied by the estuarine surface areas extracted from the SRTM water body data set (NASA/NGA, 2003), to estimate the bulk outgassing for the North and South sections of COSCAT 827. It should be noted that the methods used to estimate the CO 2 emission rates differ from one study to the other (i.
e. , different relationships relating wind speed to the gas transfer coefficient). However, in the absence of a consistent and substantial estuarine pCO 2 database for the region, we believe that our method is the only one which allows one to derive a regional data driven estimate for the CO 2 outgassing from estuaries which would otherwise require the use of reactive transport models  (Regnier et al. , 2013b). Similar approaches have been used in the past to produce global estuarine CO 2 budgets  (Borges et al. , 2005; Laruelle et al. , 2010 Laruelle et al. , , 2013;; Cai, 2011; Chen et al. , 2013). The standard deviation calculated for the emission rates of all local studies was used as an estimate of the uncertainty of the regional estuarine F CO 2.
Continental shelf waters Monthly CO 2 exchange rates at the air-water interface were calculated in continental shelf waters using 274 291 pCO 2 measurements extracted from the SOCAT 2. 0 database  (Bakker et al. , 2014). For each measurement, an instantaneous local CO 2 exchange rate with the atmosphere was calculated using Wanninkhof's equation  (Wanninkhof, 1992) , which is a function of a transfer coefficient (k), dependent on the square of the wind speed above sea surface, the apparent solubility of CO 2 in water (K 0 ) [moles m -3 atm -1 ], which depends on surface water temperature and salinity, and the gradient of pCO 2 at the air-water interface ( pCO 2 ) [µatm]. F CO 2 = A s × k × K 0 × pCO 2 (2) The parameterization used for k is that of  Wanninkhof et al. (2013) , and all the data necessary for the calculations are available in SOCAT 2.
0 except for wind speed, which was extracted from the CCMP database  (Atlas et al. , 2011). The resulting CO 2 exchange rates were then averaged per month for each 0. 25 • cell in which data were available. Average monthly CO 2 exchange rates were calculated for the North and South sections using the water surface area and weighted rate for each cell, and those averages were then extrapolated to the entire surface area A s of the corresponding section to produce F CO 2. In effect, this corresponds to applying the average exchange rate of the section to the cells devoid of data. To refine further the budget, a similar procedure was also applied to 5 depth segments (S1 to S5) corresponding to 0-20, 20-50, 50-80, 80-120 and 120-150 m, respectively, and their respective surfaces areas were extracted from high resolution bathymetric files  (Laruelle et al.
Acknowledgements. The research leading to these results received funding from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. 283080 , project  GEO-CARBON. G. G. Laruelle is Chargé de recherches du F. R. S. -FNRS  at the  Université Libre de Bruxelles. Ronny Lauerwald  was funded by the  French Agence Nationale de la Recherche  ( Investissement d'Avenir no. ANR-10-LABX-0018 ). Jens Hartmann  was funded by  DFG -project  EXC 177. The  Surface Ocean CO 2 Atlas (SO-CAT)  is an international effort, supported by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS) , and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER), in order to deliver a uniformly quality-controlled surface ocean CO 2 database.
The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. This work also used data extracted from the SOCAT/MARCATS segmentation (Laruelle et al. , 2013), the  CCMP  wind database (Atlas et al. , 2010),  GLOBALNEWS2  (Mayorga et al. , 2010;Hartmann et al. , 2009), the SRTM water body data set (NASA/NGA, 2003), HydroSHEDS 15 s routing scheme, the average annual runoff data extracted from the  UNH/GRDC  composites (Fekete et al. , 2002), the global lake and wetland database of Lehner and Döll (2004), and mean air temperature derived from the WorldClim data set of Hijmans et al. (2005). Edited by:  K. Fennel.
Crystallisation and structure determination of Spa33 208-293 The 10 mg ml -1 Spa33208-293 was crystallised at 21°C by the vapour-diffusion sitting-drop method in 400 nl drops at a 1:1 ratio with 10% (v/v) isopropanol, 0. 1 M Na HEPES pH7. 0, 10% (w/v) PEG 4000 using an OryxNano Crystallisation Robot (Douglas Instruments). Crystals were cryo-protected with 1:4 (v/v) ethylene glycol : mother liquor and flash-frozen in liquid N 2. Diffraction images were collected at beamline I04 of the Diamond Light Source (Oxfordshire, UK) and processed as P2 12121 using the Xia2 pipeline in the 3dii mode  (Winter, 2010). Initial phases were calculated following molecular replacement using Phaser  (McCoy et al. , 2007)  with an ensemble of T. maritima FliN (pdb id 1YAB) and P. syringae HrcQ B-C (pdb id 1O9Y) chainsaw  (Stein, 2008)  models, trimmed to remove regions of poor structural alignment.
The model was rebuilt and refined iteratively using Coot  (Emsley et al. , 2010)  and autoBUSTER  (Blanc et al. , 2004)  or Phenix  (Adams et al. , 2010)  but R free stalled around 30% and the maps contained almost no ordered solvent despite the 2. 3 Å resolution. Running the data through Xtriage suggested the data may be twinned and suggested reprocessing in P2 1 which was carried out using Xia2 in the 3daii mode (Table  2 ). Two copies of the Spa33 208-293 dimer were placed using Phaser and refined in Phenix using the twin law (h, -k, -l). The model was rebuilt and refined iteratively using Coot and Phenix. Protein chemistry was validated using Molprobity  (Davis et al. , 2007)  and the final model visualised with PyMol (Schrödinger). The co-ordinates for Spa33 208-293 have been deposited in the Protein Data Bank as entry pdb id 4TT9.
Gas pressures were set at 6. 7e-3, 6. 7e-2, 4. 6 and 2. 4e-6 bar in the source, quadrupole, trap (argon at 5 ml min -1 ) and mobility cell (helium at 50 ml min -1 ) respectively. The data were recorded and analysed with MassLynx and Driftscope softwares (Waters), and the spectra were calibrated using a 100 mg ml -1 solution of cesium iodide. Theoretical collision cross-sections of pdb files and generated models were measured using the scaled projection approximation method  (Benesch and Ruotolo, 2011). When calculating CCS values for assemblies for which only a partial atomic structure was available, the mass for missing atoms m was taken into account through incrementing CCS measures by (n*m)**(2/3), where n is the number of subunits in the assembly.
Acknowledgements We thank  Dr. Pietro Roversi  and other members of the  Lea lab  for assistance with data collection and processing and  Dr. Ian Blomfield  for helpful discussions about the allelic exchange experiments. We acknowledge  Diamond Light Source  for time on I04 under proposal MX9306. M. A. M. and J. E. D. were funded by grant  083599  and  Investigator Award   100298 , both from the  Wellcome Trust  to S. M. L. S. J. was funded by grant  G0900888 , Y. F. by grant  M011984 , both from the  Medical Research Council (UK)  to S. M. L. and C. T.
The Oticon Medical CI human interface research platform (cHIRP) extends existing CI research capabilities by providing a tool that specializes in real-time binaural processing, which is not well supported by other systems. The platform named cHIRP synchronously drives two independent bilateral Oticon Medical SP or XP implants to produce fine-grained interaural time difference (ITD) and interaural level difference (ILD) cues. In addition, the platform also supports variable stimulation rate processing for studying the effect of stimulation rate on perception (e. g. , the effect of this rate on pitch). We hope this platform will enable researchers to generate data that can ultimately help improve the listening lives of all CI users.
System Overview The cHIRP platform (Figure  1 ) consists of bespoke hardware and PC-based software joined via USB (Figure  2 ). The hardware parts are responsible for (a) providing a four-channel binaural ear-worn microphone system, (b) digitizing these four audio signals synchronously, and (c) generating the final left and right synchronized coil outputs needed to drive two SP or XP implants. Although we focus here on bilateral configurations, monaural implants and the binaural SP implant are also supported but these cannot provide ITD cues. The software parts are responsible for all processing tasks including (a) performing user-defined preprocessing on the digitized audio and then (b) using a commercial or user-defined stimulation strategy to generate two synchronized electrodograms. The PC-based software is called the Master Hearing Aid (MHA) and includes a flexible PC-based programming environment.
The software architecture is open and the MHA can be extended: Any imagined processing or stimulation strategy (within the capabilities of  the target SP or XP implants) can be programmed and tested in CI users without the need to create new hardware. In summary, the cHIRP research platform can be thought of as a fully programmable binaural behindthe-ear (BTE) device for Oticon Medical CIs. It provides a complete signal processing chain for developing, implementing, and evaluating state-of-the-art preprocessing algorithms and stimulation strategies-or indeed completely new ones-in real-time.
Stimulation Hardware The cHIRP stimulation hardware enclosure (Figure  1 ) has dimensions of 20 Â 13 Â 4 cm and houses a bespoke printed circuit board (Figure  4 ). It is responsible for handling all real-world signals and communicating with the MHA processing software. The main tasks of this board include (a) digitizing the acoustic input, (b) providing a first-in-first-out (FIFO) buffer and robust communication protocol for receiving commands, (c) producing the outputs needed to drive two CIs, and (d) providing triggering and synchronization of all signals. It is powered by USB with medical-grade isolation on the output side. The board has two independent sections, each powered by a separate USB. The audio input section receives four channels of acoustic input from the supplied earworn BTEs through two standard 3.
5-mm stereo jacks (other stereo sources may also be used). These four signals are all digitized synchronously using the same 16. 667-kHz sample clock. Samples are packetized and transferred via USB to the PC-side software for processing (discussed later). The stimulation output section receives stimulation commands from the MHA. These are staged in a seven-level FIFO, validated, decoded, and sent to the coil output drivers. Two independent onboard Oticon Medical SP or XP implant coil drivers are provided. These drivers support clinical transmission power levels from 5% to 70% in 1% steps (covering the known range of SP implant users). Coil transmission power, triggering options, and start or stop commands are also sent over USB. Exact timing of the outputs to external events can be controlled by a selection of triggers discussed in the following sections.
MHA Software The PC-based MHA software part of the research platform is a real-time signal processing system  (Grimm, Herzke, Berg, & Hohmann, 2006). Originally designed for conventional hearing aid research, the MHA allows many different signal processing algorithms (e. g. , noise reduction, beamforming, etc. ) to be run in software at low latency. These algorithms can be cascaded to produce complex systems. New algorithms can be coded at any time through the MHA's plug-in architecture.
Latency and Jitter Latency between audio input and electrical output needs to be kept low in order to prevent audio to lip-reading misalignment or the deterioration of speech production for test subjects. The minimum system delay for avoiding noticeable lip-sync misalignment is $17 ms for normal-hearing listeners. Disruption to speech from delayed auditory feedback is maximum at $200 ms  (Jones & Striemer, 2007; Timmons, 1983; Yates, 1963). The total latency budget including the different stages of the platform was specified to be below 17 ms, as shown in Table  4. The actual latency caused by the signal processing performed on the MHA depends on the algorithms used. Here (Table  4 ) we report the baseline latency for running the system on a small but powerful Intel NUC computer with a Core i5 processor (NUC5i5RYK) running a lowlatency Linux kernel.
Signal Processing and Algorithm Implementation The MHA software  (Grimm et al. , 2006)  supports algorithm developers with an extensive toolbox that can be used to quickly develop new parameterizable signal processing algorithms. It comes with a collection of ready-to-use hearing aid algorithms, which can be combined with user-developed algorithms to form a complete software hearing aid that can process signals in real-time. The MHA has a layered structure as shown in Figure  7. The audio backbone is external to the MHA. It is an abstraction for a source and a sink for audio data. The MHA can use different audio backbones by selecting the corresponding IO library. The standard available audio backbones are (a) the file system, (b) sound card drivers, (c) the Jack, a low-latency sound server and router, and (d) network audio streams.
In addition to these, for the cHIRP research platform, a special USB audio interface has been implemented to receive the four-channel input sent by the research board. The MHA framework selects the MHA IO library and passes the fragmented audio input signal to the MHA plug-ins. Audio signal processing in the MHA is generally performed by plug-ins. Each plug-in forms one processing block; algorithms can consist of one or more plug-ins. Plug-ins can work on waveform data (time domain) or short-time Fourier transforms. A diversity of state-of-the-art audio processing algorithms already exists as plug-ins for the MHA (e. g. , noise power spectral density estimation and suppression, fixed and adaptive minimum variance distortionless response beamformers, gammatone filterbanks, a signal localization algorithm based on generalized cross correlation features with phase transform, and many more).
Intended Use of the Research Platform The cHIRP research platform will be made available to qualified researchers interested in investigating CI topics. Because the CI signal processing is carried out on a PC and is open, it is accessible to inspection and alteration. In addition to real-time binaural processing, the platform can perform data logging, play predetermined stored input signals, and allow alteration of existing algorithms and insertion of new ones. New algorithms for the MHA are developed as plug-ins, and the signal path through the algorithms as well as algorithm settings can be altered at run time, making it possible to compare different algorithms or algorithm settings with subjective measures such as speech intelligibility, listening effort scaling, paired comparisons, and so forth.
Conclusions The cHIRP research platform provides a flexible Clanguage programmable true binaural BTE replacement  for Oticon Medical SP and XP implants. Its synchronized four-channel acoustic input and synchronized left/right coil outputs are ideal for investigating binaural CI processing (ITDs and ILDs) in real time. The heart of the system is the MHA, a flexible real-time PC-based software platform. Any real-time algorithm can be implemented as a plug-in within the MHA allowing arbitrary combinations of processing algorithms. In addition, stimulation strategies are also generated within the MHA so these can also be programmed-within the limits of the connected implants. The cHIRP research platform fills an important gap in CI research by providing a flexible, semi-portable, binaural real-time CI research processor for the first time.
In order to easily enforce solid-wall boundary conditions in the presence of complex coastlines, we propose a new mass and energy conserving Brinkman penalization for the rotating shallow water equations. This penalization does not lead to higher wave speeds in the solid region. The error estimates for the penalization are derived analytically and verified numerically for linearized one-dimensional equations. The penalization is implemented in a conservative dynamically adaptive wavelet method for the rotating shallow water equations on the sphere with bathymetry and coastline data from NOAA's ETOPO1 database. This code could form the dynamical core for a future global ocean model. The potential of the dynamically adaptive ocean model is illustrated by using it to simulate the 2004 Indonesian tsunami and wind-driven gyres.
For tsunami simulations a properly implemented adaptive strategy has been shown to provide strong efficiency gains. Popinet and Rickard (2007)  use a quadtree approach to provide dynamic adaptivity on an A grid discretization of the shallow water equations. Berger et al. (2011)  apply their adaptive mesh refinement (AMR) code GeoClaw to model a synthetic tsunami test case in flat geometry. GeoClaw automatically handles wetting/drying and can be extended to the sphere on longitude-latitude or other logically rectangular grids  (Berger et al. , 2009). The approach of  Popinet (2011)  is perhaps the most comprehensive dynamically adaptive method for calculating tsunami propagation: it allows for wetting and drying and uses a highly efficient database system for multi-level terrain reconstruction.
, 2000; Schneider and Farge, 2002; Vasilyev and Kevlahan, 2002; Kevlahan and Vasilyev, 2005). In addition, because penalization methods enforce the boundary conditions to only first-order accuracy, adaptive methods can provide the required level of accuracy by local grid adaptation (i. e. h refinement). Previous volume penalization methods for the shallow water equations are reviewed in Sect. 2. The new penalization is derived from the porous shallow water equations in Sect. 3. The new penalization is verified for the linearized one-dimensional equations in Sect. 4. Finally, we illustrate the potential of the new method by applying it to two global ocean flows: tsunami propagation and winddriven gyres. These simulations have realistic bathymetry and coastlines from the 1 arcmin NOAA ETOPO1 global relief database  (Amante and Eakins, 2009).
The penalization defined by the variables porosity (Eq. 18) and friction (Eq. 19) is easily integrated into the dynamically adaptive second-order finite difference/finite volume scheme on the sphere presented in  Dubos and Kevlahan (2013)  and  Aechtner et al. (2014)  since it requires only straightforward modifications of the shallow water equations. The bathymetry and topographic data are from the 1 arcmin NOAA ETOPO1 global relief database  (Amante and Eakins, 2009). The raw bathymetry data from the ETOPO1 database naturally tends to zero depth near the coast. Because we have not implemented wetting and drying in our shallow water model, we impose a minimum depth H min near the coastlines: b =    b r b r ≤ -H min , -H min -H min < b r < 0, 0 otherwise. ( 32 ) In practice, H min > 2 m is usually sufficient.