dataesr/scientific-paragraphs-categorization huggingface dataset:
1a  and 2a ). In contrast, at 2. 35 A ˚resolution (PDB entry 1guv) the multiple conformations of many of these residues were unresolved. In the 1. 0 A ˚resolution apo structure the catalytic glutamate shows a double conformation: Glu140-confA, a slightly rotated nonplanar rotamer (16% occupancy), and Glu140-confB, a major planar rotamer (84% occupancy) (Figs. 1a  and 1b ). In addition, the middle aspartate (Asp138) adopts two conformations  (Figs. 1a, 1c, 1d and 1e). Asp138-confA is oriented towards Asp136 (45% occupancy) and forms a short hydrogen bond to it (2. 55 A ˚), suggesting the presence of a low- barrier hydrogen bond (LBHB; Figs. 1d  and 1e ). Asp138-confB (55% occupancy) is flipped towards Glu140, to which it forms a short hydrogen bond (2. 57 A ˚, again suggesting an LBHB), stabilizing its conformation (Fig.
This is mainly owing to limitations owing to resolutions of lower than 2 A ˚and to the use of molecules such as glycerol or ethylene glycol in the cryosolution, which are capable of interacting with the catalytic glutamate  (Yang et al. , 2010; Fusetti et al. , 2002). Interestingly, an Asp 2 double conformation has been described before in other apo structures of GH18 members, such as ChiA from Serratia marcescens  (Papanikolau et al. , 2001)  and chitinase D from S. proteamaculans  (Madhuprakash et al. , 2013) , with resolutions of 1. 55 and 1. 49 A ˚, respectively. Besides, mutation of Asp 2 has been shown to abolish the enzymatic activity  (Papanikolau et al. , 2001; Bokma et al. , 2002). Indeed, the X-ray crystal structure of the mutant archaeal chitinase complexed with NAG 4 (PDB entry 3a4x), in which Asp 2 was substituted by an alanine, showed an altered conformation of the catalytic glutamate.
Effectively, co-crystals with 4-MU-NAG 3 at concentrations of 0. 3, 1 and 2. 5 mM reached X-ray data resolutions of 1. 10, 1. 05 and 1. 10 A ˚, respectively (Table  1  and Fig. 4 ). However, all of the structures (solved by molecular replacement using the apo structure as an initial model) revealed CHIT1 complexed to a dimer of N-acetylglucosamine (chitobiose) located in the À1 and À2 subsites. This indicates that hydrolysis occurred in the drop and thus allows us to analyze the post-hydrolysis three-dimensional structure of CHIT1. All of our 2mF o À F c electron-density maps of the three structures show the À2 NAG in a chair conformation, while the À1 NAG adopts a boat conformation (Fig. 4b ), which disagrees with the previously published structure of CHIT1-chitobiose at 2. 78 A resolution in which the À1 NAG was modelled in a chair conformation (PDB entry 1lg1;  Fusetti et al.
Acknowledgements We thank the  IGBMC Structural Biology and Genomics Platform  staff for technical assistance. We are grateful to  Dr Irene Yujnovsky  and  Dr Sergey Melnikov  for helpful discussions. The crystallographic experiments were performed on the X06DA (PXIII) beamline at the  Swiss Light Source synchrotron, Paul Scherrer Institut, Villigen, Switzerland. We thank, in particular,  Ezequiel Panepucci  and  Vincent Olieric  for their help with data collection. This work has been funded by the  CNRS , the  INSERM, Strasbourg University, Biostruct -X (FP7, contract  283570 ),  Instruct (European Strategy Forum of Research Infrastructures; ESFRI)  and the  French Infrastructure for Integrated Structural Biology (FRISBI).
Patient population For the prevalence of CD in the UK, we used data from representative populations similar to the UK population. The prevalence of CD was estimated to range between 0. 0057% and 0. 037%, translating to between 3,512  6   20  in which the authors conducted a more active detection of cases, we inferred the prevalence of CD to be 13,324. We considered this latter figure to be a more realistic estimate of the prevalence of CD and, therefore, adopted this figure for our base case prevalence assumption; 23,037 patients were used as the upper limit of the range (Ipsen Biopharm Ltd, unpublished data, October 2012). Of the estimated 13,324 patients receiving any treatment for CD, we assumed that 80% of these would receive BoNT-A treatment at any point in time, due to the delay in onset to correct diagnosis or due to other reasons that not all patients will get BoNT-A (Ipsen Biopharm Ltd, unpublished data, 2012).
Cost data used for our analysis were retrieved from the BNF. 18 The dosages of the BoNT-A injections were, however, based on the suggested clinical doses reported in the SPCs, and the variation around their estimates were either based on the corresponding SPC for each BoNT-A or expert clinical opinion. Resource use for BoNT-A treatment arms were essentially the same, except for the costs of the BoNT-A. Cost differences arise between treatment with BoNT-A and treatment without BoNT-A -the proportional use of anticholinergics, dopamine antagonists, beta-blockers, antiepileptics, muscle relaxants, and the need for selective peripheral denervation and laboratory tests. In general, data on proportional resource use by patients was unavailable in the published literature. We therefore used data based on clinical expert opinion.
On the other hand, the dosage data was derived from the BNF. 18 urther details are provided in Table  4. It should be noted that as the market share evolution involved changes only to BoNT-A, and not to best supportive care without BoNT-A, the budget impact will not depend on best supportive care costs. However, cost comparisons between patients receiving treatment with and without BoNT-A were possible. analysis Base case analyses were conducted using the epidemiological, market share, cost, and resource use figures given earlier. One-way sensitivity analyses were conducted to determine which parameters were most sensitive to change in the model. Model parameters were varied between high and low values based on the ranges identified from the literature, when data were available. For data where the ranges could not be specified, a predefined ±20% range was used to test the parameters.
Acknowledgments This study was funded by  Ipsen Pharma. The abstract of this paper was presented at the 18th  International Congress of Parkinson's Disease and Movement Disorders ,  2014 , as a poster presentation with interim findings. The poster's abstract was published in the " Poster Abstracts" in Movement Disorders Volume 29 , June 2014 Abstract Supplement: http://www. mdsabstracts. com/abstract. asp?MeetingID=801&id=112338. The actual paper, however, has never been published. No additional data are available.
Fails et al. [FKSS06]  presented a visual-query interface and result-set visualization tool for searching and discovery of temporal patterns within multivariate and categorical datasets. The results can be explored through coupled ball-and-chain and tabular visualizations. Bruckner and Möller  [BM10]  proposed a system for the visual exploration of a simulation parameter space to assist in the generation of effects such as smoke and explosions. They split each simulation sequence into a number of representative segments. Then they compared the simulationspace similarities at different points within the temporal evolution of each simulation. They depict subsets of the clusters' members in a timeline at different temporal compression levels. Another example is the work of Krstajic et al. [KBK11].
Visualization systems with applications in weather forecast and climatology There has been extensive work done in the area of visualization systems and visual analytics applied to weather forecast and climatology. In the scope of this paper, only a few examples are summarized here as a background for our work. The University Corporation for Atmospheric Research (UCAR) presented a list of post-processing tools for weather visualization. Among them are: Integrated Data Viewer (IDV)  [MMWE03] , Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers (VA-POR)  [CMNR07] , and the Grid Analysis and Display System (GrADS)  [TD98]. The latter was selected by the CIMA institute to perform their visualizations. GrADS is a visualization system that uses a mix of GUI and commandline scripts to derive post-processed data visualizations.
Command-line scripts and programming features can be seen as an advantage in terms of flexibility, but also make the user experience and interaction difficult. Instead, visual interfaces are proven to be more effective in terms of cognitive productivity  [War13]. The Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) [WDP * 13] are a powerful system jointly developed by several institutions, universities, and private companies. The tool set integrates data analytics, ensemble analysis, uncertainty quantification, metrics computation, and visualization components for big data climate analysis. Also, the work of Song et al. [SYS * 06] presented a visualization system that performs analysis of multi-dimensional atmo-spheric data sets using physics-based atmospheric rendering, illustrative particles, and glyphs.
Implementation Our solution is built as a system with a visual frontend and a server backend. The frontend presents a visual interface implemented as a web application with multiple linked views. It was developed using HTML5, JavaScript, and jQuery technologies. Additionally, TeeChart charting components were used to implement the meteogram, and vis. js was utilized for the timeline. The backend uses the GPU computing engine and core libraries for processing weather forecast information. It was developed using C++ and OpenCL technologies for the GPGPU. Bing Maps services were used for the mapview component. The backend storage is based on a PostgreSQL geo-spatial database management system and database processes to import and to store the inputs provided by the WRF model. Our data comes from a customized regional WRF model implemented over a region centered at latitude 38 • S and longitude 63 • W.
3D VOLUME QUANTIFICATION The 3D volume calculation was based on the original serial confocal acquisitions. The "VolumeJ" program was designed by Denis Ressnikoff (SFR Lyon-Est, CNRS UMS3453-INSERM US7, Centre Commun de Quantimétrie) based on the 3D object counter plugin [Fabrice Cordelieres (fabrice. Cordelieres@curie. u-psud. fr); Jonathan Jackson (j. jackson@ion. ucl. ac. uk)] and the 3D volume viewer  [Benjamin Schmid (Bene. Schmid@gmail. com) ]. The program is available at https://www. labex-cortex. com/en/users/delphine-delaunay and as. txt in "Supplementary Material. ".
2D AREA QUANTIFICATION Serial sections of metaphase cells from dissociated mouse cortical precursors and from in situ monkey VZ precursors, were acquired from 0. 2 to 0. 6 μm intervals from the top to the bottom of the cells (back to back) in order to measure the entire spindle apparatus. Only metaphase cells presenting equal sized centrosomes were taken into consideration. The area of each spindle pole was measured using Image J on maximal stack projections based on the alpha-tubulin staining. The area of each spindle pole was reported as the percentage difference between the two spindle areas. The folded normal distribution and Permutation test "utilFuncs. R" script as well as data test and instructions are available at https://www. labex-cortex. com/en/ users/delphine-delaunay and as. txt in "Supplementary Material.
Using the ImageJ software, for each cell, optical sections were transformed into maximal intensity stack projections (Figures  3E, I ). The resulting left and right spindle pole domains were then manually drawn as ROI (Figures  3F, J ) and their respective surface area estimated. Let A L and A R , respectively denote the left and right surface areas, the 2D spindle-pole difference is defined by = A L -A R A L + A R × 100. Examples of symmetric vs. asymmetric spindle are illustrated Figures  3G, K. In particular, a spindle-pole difference ≥ 20 was often measured in highly asymmetric cells (Figure  3K ). To analyze SSA evolution during cortical development, the spindle-pole difference was evaluated at five distinct developmental stages between E10 and E18. 322 metaphases cells were analyzed and the SSA variations reported using a folded normal distribution (see Methods, "2D area quantification").
The folded normal distribution represents the distribution of the absolute value of a given variable [the probability measure of the normal distribution on (-∞,0) is folded over to (0,∞)]. The probability density function is reported for each developmental stage and reflects SSA magnitude. This task was performed using an R script specifically designed in our laboratory and freely available (see Methods for details). From E10. 5 to E16. 5, spindle-pole difference was found to follow the neurogenesis kinetics, with the mean and standard deviation increasing up to E14. 5 (neurogenesis) and then decreasing (as illustrated in Figure  3L ). The significance values were confirmed using a permutation test. Under the permutation hypothesis, it is assumed that is distributed evenly across ages, so that randomly permuting the labels of the ages across the data set should not change observed differences.
We randomly permuted 10,000 times the group membership labels between the control stage (E10. 5) and E14. 5 (Figure  3M ). This analysis reveals a significant shift in SSA magnitude at E14. 5 compared to earlier stages. These results are in accordance with previously published data using the 3D quantification method  (Delaunay et al. , 2014). To ensure that the spindle orientation had no effect on SSA measurement, we plotted the spindle angle deviation against the SSA magnitude. The spindle angle measurement was carried out on dissociated precursors (used in  Delaunay et al. , 2014)  as described in  Toyoshima and Nishida (2007). The angle between the axis of the metaphase spindle and the substrate surface was measured using the linear distance (X) and the vertical distance (Y) between the two poles of the metaphase spindles revealed by the pericentrin staining.
The program is downloaded as a stand-alone program and run in ImageJ version 1. 47T Optical Stacks are taken with a X63 objective, with a minimal pixel resolution of 90 × 90 nm (format 512 × 512, bidirectionnal) and with z-value of 0. 5 μm. • Under ImageJ software, open the α-tubulin channel and rename it with a simple name. • Start the "volume quanti 1_0. ijm" program. • To avoid background, make a ROI selection close to the spindle, and select "Ok. " • The macro create two sets of picture: one named ". tif_ROI," (visualization picture) and the other named ". tif_mask" (3d skeleton). • On ". tif_mask," check the correlation between the spindle pixelation and the observed α-tubulin channel (on ". tif_ROI"). • If it match, click apply to the "threshold" windows. • Click "ok" on the macro windows to observe simplification of the pixelated shapes.
General case The equivalence between the spheroidal and conical models in terms of the relationship between and V motivates a generalization: we now assume that the left and right spindle poles are solids of revolution with generating curves obtained by scaling the value and the argument of an arbitrary function f : [0, 1] → R +. For each cells, a primary reconstruction is made to verify that the centrosome are of equal sizes (E,I, yellow dots). (F,J) The SSA intensity is determined on maximal intensity stack projection reconstructed under the ImageJ software. Each spindle pole is manually drawn and the corresponding area (1 and 2) calculated. Arbitrarily, the bigger area will be defined as the Left spindle and the smallest as the Right spindle. (G,K) The difference between the Left (green) and the Right area (yellow) expressed in percentage will be the unit of measurement, delta ( ).
ACKNOWLEDGMENTS We thank  Cedric Manesse  for technical help with the monkey data set acquisition,  Ken Knoblauch  for computation and statistical expertise as well as for the R manuscript and  Henry Kennedy  for critical reading of the manuscript. This work received support from  LABEX CORTEX  ( ANR-11-LABX-0042 ),  LABEX DEVWECAN  ( ANR-10-LABX-0061 ) of  Université de Lyon  ( ANR-11-IDEX-0007 ),  ANR PRIMACOR.
Although sophisticated process-based soil erosion models exist (e. g. ,  Mitas and Mitas ˇova, 1998) , they require high-resolution input data that restrict their use to small catchments and short time periods, far below the spatio-temporal scales involved here. Instead, it was chosen to use the simple Revised Universal Soil Loss Equation model that predicts long-term average annual soil loss under specified landcover and climate conditions (RUSLE,  Renard et al. , 1997). RUSLE is based on a set of semi-empirical equations and is better suited for the application to large areas. It was successfully tested over a wide variety of environments for which a considerable amount of literature exists, can be easily implemented with GIS softwares (e. g. Yang et al. , 2003) , and does not require heavy computing power.
Materials and methods RUSLE was spatially resolved to predict current and past rates of soil erosion by water on the Charente catchment under realistic climate and land-cover scenarios. All geospatial data were cropped, converted and/or resampled to a series of raster layers with same extent and cell resolution of 25 m. Raster layers were projected in Lambert93 geodetic system (EPSG code 2154). Sediment load (tÁa À1 ) at the catchment outlet was computed as SL ¼ X n i¼0 A i Á SDR Á ar (1) where, for the ith raster cell, A i is the mean annual gross soil erosion by water (tÁha À1 Áa À1 ) predicted by RUSLE as A i ¼ R i Á K i Á L i Á S i Á C i Á P i (2) SDR is the average Sediment Delivery Ratio  (Walling, 1983) , which accounts for catchment efficiency to export eroded material towards streams (see section 3.
Revised universal soil loss equation The components of equation (  2 ) are briefly presented here (see  Renard et al. , 1997 for details). R describes the removal effect of raindrop impacts on the soil surface, expressed in MJÁmmÁha À1 Áh À1 Áa À1. Renard and Freimund (1994)  regression equations suited for yearly rainfall data were applied (see section 3. 2 for data sources). R ¼ 0:04830 Â P 1:610 ; if P < 850mm; (3a) R ¼ 587:8 À 1:219 Â P þ 0:004105 Â P 2 ; if P ! 850mm (3b) K describes the sensitivity of a soil to erosion depending on its properties, expressed in tÁhÁMJ À1 Ámm À1. K was extracted from 500-m cell resolution K factor dataset interpolated from European Soil DataBase  (Panagos et al. , 2014). The protective effect of surface stone cover (stoniness) was taken into account. L and S are dimensionless factors that account for sediment gravity flows resulting from topography.
Scenarios Current and past rates of Charente river sediment supply were predicted from the coupled RUSLE-SDR equation 1. The K, L, and S factors were considered time-invariant since 1500 in order to focus on centennial to decadal variability arising from climate and land-cover changes (i. e. R and C factors). Sediment supply was modelled annually in three time intervals representative of sedimentological conditions in Marennes-Oleron bay (Fig. 1d ). Intervals include the second half of 20th century for validation and comparison with existing data (siltdominated bay), the 18th century (mixed sand-silt sedimentation), and the 16th century (sand-dominated bay).
Land cover (C factor) C-factor maps were built from a combination of data. ''Water'' and ''Wetland'' land cover categories were extracted from Corine Land Cover 2000 dataset (CLC2000,  Steenmans et al. , 2002)  and kept constant across scenarios. In the absence of historical knowledge on the dynamics of pasture areas on the Charente catchment, the ''Pasture'' category was also kept as of CLC2000 as a conservative approach. ''Forest'' and ''Urban'' categories varied in time: ''Forest'' category: the 20th-century forest cover was extracted from CLC2000 dataset. The 18th-century cover was extracted from  Vallauri et al. (2012)  dataset, which consists in geo-referenced contours of forests represented on Cassini historical maps (1747-1790). The 16th-century cover was created from the previous layer, resized to match forest surface estimates of  Mather et al.
(1999)  using a buffer method; ''urban'' category: 20th-century urban cores were extracted from CLC2000 dataset. Urban areas represented 405. 3 km 2 , in which ca. 586 000 people lived in 2000, i. e. 1% of total French population  (INSEE, 2014). This ratio and the corresponding average population density of 1445. 8 inhabitants per square kilometer of urban area were supposed constant across scenarios. 18th and 16th centuries urban cores were resized in order to reproduce the decrease in population living on the catchment. French national demographic data were compiled from Dupaquier (1995) time series (estimated Charente population of 265 000 in 1770 and of 152 000 in 1550). The buffer distance was the square root of features area in order to shrink large cities in priority.
Sediment Delivery Ratio The Sediment Delivery Ratio was estimated by comparing the monitored SPM concentration with predicted gross soil erosion on associated up-stream sub-catchments. The SPM concentration was monitored since 1976 in 217 water-quality gauge stations. Stations located in man-made waterways or within the Charente estuary turbidity maximum were discarded. Up-stream subcatchments associated with the remaining 185 stations (Fig. 2 ) were delineated from 25-m cell resolution Digital Elevation Model. The Sediment Delivery Ratio was calculated as SDR j ¼ ðSPM j Á MF j Á aÞ=A j (6) where SPM j is SPM concentration at the jth gauge station (mgÁl À1 ). MF j is water flow at the jth gauge station (m 3 Ás À1 ). The water flow was monitored in 40 stations only (Fig. 2 ). Data could not be reliably used, as most stations were located kilometres down-or upstream from SPM-gauging stations and yielded highly irregular time series.
As an alternative, MF j was estimated from Mandal and Cunnane (2009) equation as MF j ¼ ða Á e bÁlna j Álnr j Þ=c (7) where a j is area of the jth up-stream catchment (km 2 ), r j is average annual rainfall on the jth up-stream catchment (mm), extracted from WorldClim climate dataset (average rainfall 1950-2000, 30 arc-seconds cell resolution,  Hijmans et al. , 2005)  a is an appropriate conversion factor to (tÁa À1 ), and A j is total annual gross soil water erosion on the jth up-stream sub-catchment (tÁa À1 ). A j was predicted with equation (2) using the 20th-century C-factor and R-factor maps derived from WorldClim dataset. A single Sediment Delivery Ratio representative of the whole Charente catchment was extrapolated from SDR values calculated for the 185 sub-catchments. Values were first tentatively correlated with a set of explanatory variables, according to known relationships (e.
Model output There is little difference in spatial patterns of soil erosion rates across the three scenarios. Globally, erosion greater than 20 tÁha À1 Áa À1 prevails to the south, where high LS values (Fig. 3c ) are combined to extensive crop lands with a high C value. Erosion is lower than 1 tÁha À1 Áa À1 on pasture land and forests (e. g. , 0. 255 AE 0. 217 tÁha À1 Áa À1 ). Changes over time were most significant to the east, on terrains higher than 100 m, where the contribution of high LS  Based on the R factor computed from WorldClim dataset and estimation of Sediment Delivery Ratio, the predicted sediment load for the 20th century land cover scenario is 32 141. 5 tÁa À1 (1-s range 12 541. 3-82 374. 0). The sediment load predicted with the rainfall dataset of  Pauling et al. (2006)  is 16.