dataesr/scientific-paragraphs-categorization huggingface dataset:
Accession numbers The X ray structures presented have been deposited in the wwPDB with accession codes 5IUJ (DesK-DesR complex in the phosphotransfer state with low Mg 2+ [20 mM]), 5IUK (DesK-DesR complex in the phosphotransfer state with high Mg 2+ [150 mM]), 5IUL (DesK-DesR complex in the phosphotransfer state with high Mg 2+ [150 mM] and BeF 3 -), 5IUM (phosphorylated wild type DesKC) and 5IUN (DesK-DesR complex in the phosphatase state). Raw X ray diffraction data corresponding to each one of these structures are publicly available at SBGrid Data Bank (http://data. sbgrid. org) as dataset entries 399, 400, 401, 407 and 408.
Acknowledgements We want to thank the staffs at synchrotron beamlines Proxima 1 and 2, Soleil (France), especially  William Shepard ; and  Daniela Albanesi  for kindly providing plasmid pHPKS/Pxyl-desKSTA. The  Institut Pasteur International Network  is acknowledged for support through the  IMiZA International Joint Unit. We acknowledge funding from  Agencia Nacional de Investigacion e Innovacion (ANII) ,  Uruguay  ( FCE2009_1_2679 ;  FCE2007_219 );  Agence Nationale de la Recherche (ANR) ,  France  ( PCV06_138918 );  Centro de Biologı ´a Estructural del Mercosur  (www. cebem-lat. org) and  Fondo para la Convergencia Estructural del MERCOSUR  ( COF 03/11 ).
Abstract. Carbon dioxide (CO 2 ) and methane (CH 4 ) mole fractions were measured at four near-ground sites located in and around London during the summer of 2012 with a view to investigating the potential of assimilating such measurements in an atmospheric inversion system for the monitoring of the CO 2 and CH 4 emissions in the London area. These data were analysed and compared with simulations using a modelling framework suited to building an inversion system: a 2 km horizontal resolution south of England configuration of the transport model CHIMERE driven by European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological forcing, coupled to a 1 km horizontal resolution emission inventory (the UK National Atmospheric Emission Inventory). First comparisons reveal that local sources, which cannot be represented in the model at a 2 km resolution, have a large impact on measurements.
To our knowledge, there have been few attempts to monitor the CH 4 emissions of the London area using atmospheric measurements  (Lowry et al. , 2001; O'Shea et al. , 2014). In this context, we made quasi-continuous measurements of CO 2 , CH 4 and CO during 2012 at four sites in the London area (two inner city sites -Hackney and Poplar; one suburban site -Teddington; and one rural site outside the urban area -Detling) using sensors located at 10-15 m above ground level. We assess the ability of a kilometre-resolution transport model driven by a kilometre-resolution emissions inventory to simulate these CO 2 and CH 4 measurements. The aim is to understand whether such measurement sites are ultimately suitable for use in a flux inversion scheme based on the kilometre-resolution model. This study investi-gates the importance of different sources of discrepancies between observed and simulated GHG mole fractions (henceforth "model-data discrepancies").
GHG measurements Continuous measurements of CO, CO 2 , CH 4 and water vapour were taken between 1 June and 30 September 2012 for the Hackney, Poplar and Teddington sites and 5 July to 30 September 2012 at Detling. Each site was instrumented with a G2401 Picarro cavity ring-down spectroscopy (CRDS) instrument that logged data every 5 s and sent data files each hour to a remote server. All sensors across the network were manually calibrated on an approximately 2-weekly basis using the same gas standards, ensuring the consistency of the measurements from different sites. The sensors were calibrated for linearity, repeatability of measurements (for zero and span gases, i. e. respectively with concentrations zero and ambient concentration gas) and drift in the field and in the laboratory prior to deployment.
No water correction was applied for CO. Expressed as a percentage of the mean measured concentration throughout the measurement period, the total measurement uncertainties (root sum square, RSS, of the bias and random errors) are 0. 3, 0. 7 and 21 % for CO 2 , CH 4 and CO respectively. Data were calibrated using the standard gas cylinder values, and provided as 15 min averages by NPL. Calibration episodes were removed from the final data set. The Teddington sensor was inactive between 6 and 12 July due to sample pump failure and there were a small number of missing days at Detling (due to power outage) and at Poplar (for unknown reasons). There were few missing data at the Hackney site. The 15 min data from the measurement sites were aggregated by averaging into hourly time intervals for comparison with the hourly output from the model.
e. it is set to 0 for grid cells entirely covered by urban area, set to the value from ECMWF for grid cells with no fraction of urban area, and, in a general way, set to the product of the fraction of non-urban area in the grid cells times the value from ECMWF). The fraction of urban area within each 2 km × 2 km grid cell was derived from the land cover map of the Global Land Cover Facility (GLCF) 1 km × 1 km resolution database from the University of Maryland. This database is based on the methodology of  Hansen and Reed (2000)  and Advanced Very High Resolution Radiometer (AVHRR) data. The decreases in horizontal wind speed are balanced by an increase in the vertical component of the wind. However, the current configuration does not account for the urban heat island effect either in the ECMWF product or in the processing of this product before its use by CHIMERE.
87 ppm, according to the typical mole fractions measured at the Mace Head atmospheric measurement station in 2012  (NOAA, 2013). The top boundary conditions were set to a smaller value: 1. 67 ppm. Anthropogenic emissions of CO 2 and CH 4 were prescribed to CHIMERE within its domain using the NAEI, described in Sect. 2. 1. Three-dimensional hourly emissions for CO 2 and CH 4 were interpolated from this inventory on the 2 km horizontal resolution model grid. The derivation of the emissions for the UK based on the NAEI included injection heights for major point sources and temporal profiles (see below for details on the definition of injection heights and temporal profiles). The CO 2 emissions for the small part of France appearing in the domain were derived from the Emission Database for Global Atmospheric Research (EDGAR, 2014) at 0.
, 2009). In the modelling framework of this study, temporal profiles were derived for the three sectors of CO 2 emissions with the largest variations in time: road transport, power generation in large combustion plants, and residential and commercial combustion. They were based on  Reis et al. (2009)  using data from 2004 to 2008. These sectorial profiles were applied homogeneously in space for the whole south of England. For road transport, the temporal profiles were based on the product of monthly weights for a typical year, daily weights for a typical week and hourly weights for each day of the week (with two maxima during week days and one maximum for Saturdays and Sundays) derived from statistical data about the variations in traffic flows in the UK. For the power generation and residential and commercial combustion, only monthly variations were considered based on the consumption for typical years.
97 , -0 • 12 50. 85 ) as part of the ClearfLo project  (Bohnenstengel et al. , 2015). The limited sampling rate of the lidar was accounted for using a spectral correction method described in  Barlow et al. (2014)  and  Hogan et al. (2009). Mixing heights were calculated based on a threshold value of the vertical velocity variance, which was perturbed between 0. 080 and 0. 121 m 2 s -1 , to check the sensitivity of calculated mixing heights to the threshold, which is an approximate parameter for this computation. Mean, median, and 5th and 95th percentile values were calculated for each hour based on these perturbations, and account for both measurement and method uncertainties  (Barlow et al. , 2014; Bohnenstengel et al. , 2015). Based on the 5th and 95th percentile data averaged across all data for each hour, estimated measurement and method uncertainty was between 53 and 299 m throughout the daily cycle, with the highest uncertainties usually overnight.
At both urban sites the observed CH 4 wind roses are very similar, showing increased mole fractions towards the east of the sites (data not shown); however, mole fractions are greater in magnitude at Poplar than at Hackney. Similarly to CO 2 , the model simulates lower CH 4 mole fractions than observed, with a similar distribution at both sites. The stronger similarity between the wind roses at the two sites when considering CH 4 measurements than when considering CO 2 measurements could be explained by the absence of strong CH 4 local sources in the vicinity of the measurement sites. The NAEI does not locate any major waste treatment facility at less than 5 km from these sites and it assigns a level of emissions from the other sectors (which are characterised by diffuse sources in the inventory) for this vicinity that is similar to the general level of CH 4 emissions in the London urban area.
37 m s -1 (7 % of observation mean) and 5 • for wind speed and direction respectively (RMS model-data discrepancies of 1. 27 m s -1 and 2. 24 • for wind speed and direction respectively). Some of this discrepancy may arise from the necessity of comparing the 25-50 m average wind data from the model to the 10 m height measurements at the Heathrow meteorological station. It is highly difficult to translate such statistics of the errors on the wind into typical errors on the simulation of the GHG concentrations at the GHG measurement sites since there is a complex relationship between them, which strongly depends on the specification of the local to remote emissions, and on the spatial distribution of the errors in the meteorological parameters or in these emission estimates at the local to larger scales.
Furthermore, the random measurement error should be larger for gradients than at individual sites (since the SD of the measurement error for the difference between two data at individual sites is the RSS of the SD of the independent measurement errors for these data). Therefore, the main driver of the strong decrease in model-data discrepancies when analysing gradients instead of mole fractions at individual sites should be the strong reduction of the large scale errors from the boundary conditions and remote fluxes. The random component of the measurement errors should be uncorrelated between different sites, and thus the SD of the gradient measurement error should be √ 2 times the SD of the measurement error at individual sites. Therefore, the gradient measurement error should remain much smaller (typically equal to 0.
6 ). The selected gradients correspond to 22 % (101 over 452) of the available HAC-TED afternoon gradients and 22 % (93 over 431) of the POP-TED available afternoon gradients for either CO 2 or CH 4. There are only 17 hourly (CO 2 or CH 4 ) gradients to Detling (3 % of all available afternoon gradients to Detling) recorded wherein Detling was positioned upwind of the urban sites. Because of this low number of selected observations, gradients to Detling are ignored in the remainder of the analyses. The statistics of the model-data discrepancies for gradients to Teddington when this site is upwind the urban sites are presented in Table  2. Filtering for wind direction reduced the negative bias and the RMS of discrepancies for CO 2 HAC-TED gradients, but slightly increased the RMS of discrepancies and increased the positive bias on the CO 2 POP-TED gradient relative to the statistics on the discrepancies without wind filtering.
3. 6, and the residual discrepancies when comparing measured and modelled gradients question the validity of the assumption that the signature of the natural fluxes is not significant compared with that of the fossil fuel emissions in the measured gradient with or without wind direction filtering. In this section we thus attempt to improve the focus on the signature of the urban emissions by deriving a CO 2 fossil fuel component from both the modelled and the measured gradients. While the model directly provides the fossil fuel CO 2 gradient ( FF-CO 2 ) values, we use an empirical method based on the continuous CO measurements to extract an observation-based estimate of FF-CO 2 between the measurement sites, since CO and CO 2 are co-emitted when fossil fuels are burnt. We focus the analysis on HAC-TED and POP-TED when Teddington is located upwind of the urban sites (with a ±20 • margin for the selection of the corre-sponding wind direction), given that such a choice increases the consistency between the model and the data  (Sect.
However, the POP-TED gradients modeldata bias is significantly increased when comparing results for FF-CO 2 to those for CO 2 (Tables  2  and 3 ). Table  3. Statistics of the hourly difference between modelled and observationally based fossil fuel CO 2 gradients ( FF-CO 2 ) in parts per million (ppm) for HAC-TED and POP-TED during the afternoon periods (12:00 to 17:00 UTC) between July and September, when Heathrow measured wind direction places Teddington upwind of each urban site (for angles between the wind direction and the direction between Teddington and a given urban site smaller than 20 In this study we compared observed CO 2 and CH 4 mole fractions from four near-ground measurement sites in and around London to the simulations from a mesoscale transport model driven by temporally and spatially varying emissions estimates.
We aimed to determine whether these nearground sites would be amenable to the atmospheric inversion of the London city-scale emissions using such an atmospheric transport model. The measurements and model simulation applied to the period June-September 2012. Given the initial diagnostic of very large model-data discrepancies at the different measurement sites, this study attempted to remove or characterise the influence of some of the underlying sources of uncertainty and to isolate, in both the model and the measurements, the signal that corresponds to the London anthropogenic emissions, which would be targeted by the inversion. Focusing the analysis on afternoon data limited the impact of the model's inability to correctly predict the transitions of the mixing layer depth in morning and evening.
, 2015). Our analysis also showed that the meteorological forcing used in this study underestimated the mixing layer depth during the afternoon. Furthermore, we assessed measurement error as a potential source of model-data discrepancies throughout our analyses in this study. Practical constraints for this short measurement campaign did not allow us to design it in such a way that the measurements can be compared with each other or with other measurements within 0. 1 ppm, as recommended by WMO for the Northern Hemisphere (WMO & GAW, 2012). The random measurement error at individual sites was smaller than the model-data discrepancies by an order of magnitude and so was considered to be negligible. However, the systematic measurement error is large enough not to be neglected in the raw discrepancies, even though it does not dominate.
Acknowledgements. This work was funded and supported by  Astrium Services SAS. The authors would like to thank  Brian Sweeney  of the  National Physical Laboratory  for his efforts in keeping the measurement sites calibrated and well maintained. We would like to thank the  ClearfLo  project members for provision of the lidar data from their summer IOP at North Kensington and thanks go to  Christos Halios  for help with interpreting the lidar data. Thanks also go to  Davey Atkinson  for help with figure preparation. Edited by:  W. Lahoz.
In order to translate the use of κ in clinical context, a prerequisite to achieve meaningful results is to investigate the reliability of this index. In a preliminary part, we studied the reliability of κ by computing the intraclass correlation coefficient in a cohort of 100 subjects from the Human Connectome Project. Then, we measured intra-hemispheric κ index in the contralesional hemisphere of 20 subacute stroke patients compared to 20 age-matched healthy controls. Finally, due to the small number of patients, we tested the robustness of our results repeating the experiment 1000 times by bootstrapping on the Human Connectome Project database. Statistical analysis showed a significant reduction of κ for the contralesional hemisphere of right stroke patients compared to healthy controls. Similar results were observed for the right contralesional hemisphere of left stroke patients.
The aim of this paper is to quantify the impact of the lesion on the brain network reorganization of the contralesional hemisphere in severe stroke patients at subacute stage. For this purpose, κ index is a perfect tool to assess such reorganization by comparing nodal metrics between healthy volunteers and patients. In order to translate the use of κ in clinical context, an essential requirement to achieve meaningful results is to investigate the reliability of this index. For this purpose, we used the intraclass correlation coefficient (ICC), as it was previously assessed in several studies working with brain graphs reliability in rs-fMRI  (Schwarz and McGonigle, 2011; Wang et al. , 2011; Braun et al. , 2012; Guo et al. , 2012; Liang et al. , 2012; Cao et al. , 2014). This paper is divided into three parts: in the first part, we assessed the reliability of κ, over different graph metrics, by computing the ICC in a cohort of 100 healthy subjects using the database from the Human Connectome Project (HCP) 1.
We calculated the ICCs and their p-values, applying bootstrap and permutation techniques to check for the influence of the number of subjects and of the number of edges (cost) in brain graphs. We also explored whether there is a laterality effect by testing the graphs of the intra-hemispheric connectivity from the left and from the right hemispheres in healthy control subjects using the HCP dataset. In the second part of the paper, we used the κ index to study the reorganization that occurs in the contralesional hemisphere of 20 severe subacute stroke patients. Finally, in the third part, we tested the robustness of the results obtained in this clinical study by randomly choosing 20 subjects as "patients" and 20 subjects as "controls" from the HCP database, computing the difference in κ between them and replicating 1000 times this procedure.
Databases The dataset used to assess the reliability of the κ index was selected from a large sample of rs-fMRI dataset publicly released as part of the Human Connectome Project (HCP), WU-Minn Consortium. The sample includes 100 subjects: 99 young healthy adults from 20 to 35 years old (54 females) and 1 healthy adult older than 35. Each subject underwent two rs-fMRI acquisitions on different days. Subjects were instructed to keep their eyes open and to let their mind wander while fixating a cross-hair projected on a dark background  (Smith et al. , 2013). The clinical study, HERMES (PHRC2010) was the ancillary MRI study of a stem cells clinical trial, ISIS 2. Patients were studied using fMRI at inclusion time (5 weeks post-stroke), received standard medical care, and admitted to a stroke rehabilitation center.
Neuroimaging Data Acquisition The data of Human Connectome Project were collected on the 3T Siemens Connectome Skyra MRI scanner with a 32channel head coil. All functional images were acquired with eyes open with relaxed fixation on a projected bright cross-hair on a dark background, using a multiband gradient-echo EPI imaging sequence with the following parameters: 2 mm isotropic voxels, 72 axial slices, TR = 720 ms, TE = 33. 1 ms, flip angle = 52 • , field of view = 208 × 180 mm 2 , matrix size = 104 × 90, and a multiband factor of 8. 1200 images were acquired in a scan duration of 14 min and 24 s. For more detailed parameters, see  (Smith et al. , 2013). Two high resolution structural images T1weighted (T1w) and T2-weighted (T2w) were further collected. They were acquired with a 3D MPRAGE sequence and a 3D T2-SPACE sequence, respectively.
The main MR parameters for the T1w image were: TR = 2. 4 s, TE = 2. 14 ms, TI = 1000 ms, flip angle = 8 • , field of view = 224 ×224 mm 2 , and 0. 7 mm isotropic voxels and for the T2w: TR = 3. 2 s, TE = 565 ms, flip angle = variable, field of view = 224 × 224 mm 2 ,and 0. 7 mm isotropic voxels; rs-fMRI data were acquired in four runs of approximately 15 min each, two runs in one session and two in another session. In the case of the HERMES study, the MRI data of the patients and controls were acquired at a 3T (Achieva 3T TX, Philips, NL) at the IRMaGe MRI facility (Grenoble, France). The.
HCP Data T1w and T2w were corrected for bias field and distortions, coregistered together and registered to the MNI152 atlas using linear and non-linear registration functions. After registration to the atlas image, we segmented the individual T1w to obtain a gray matter (GM) probability map that was later used to extract the time series to compute the graphs. Functional data were corrected for distortions and subject motion. They were registered to the individual structural image and further to the MNI152 atlas space, using the transforms applied to the structural image. All of these preceding transforms were concatenated, together with the structural-to-MNI non-linear warp field, so that a single resulting warp (per time point) was applied to the original time series to achieve a single resampling into MNI space.
Wavelets Decomposition Wavelet transforms perform a time-scale decomposition that partitions the total energy of a signal over a set of compactly supported basis functions, each of them uniquely scaled in frequency and located in time  (Achard et al. , 2006). We applied the maximal overlap discrete wavelet transform (MODWT) to each regional mean time series and estimated the pairwise interregional correlations at each of the wavelet scales. The wavelet decomposition is dependent on the repetition time (TR) of the rs-fMRI acquisition protocol. The databases used in this experiment have different TR. In the HCP database the TR = 0. 72 s, while in the HERMES database the TR = 2. 00 s, providing a maximum frequency f = 1/(2TR) of f = 0. 69 Hz and f = 0. 25 Hz, respectively. Each time a dyadic wavelet frequency band is obtained, the frequency is divided by 2.
Reliability of κ in Controls The κ index is proposed as a measure to capture network disorganization in individuals or groups as compared to a reference group. The assessment of its reliability in controls is a complementary step because if it is found as a reliable index, it will increase the chance of finding differences between groups, even if it does not assure that differences between groups will be found  (Shirer et al. , 2015). We used HCP database to assess the reproducibility of κ in control subjects calculated over the five metrics explained above, for costs ranging between 10 and 75%. We used the mean of session 1 as reference to compute κ for each subject's session 1 and the mean of session 2 as reference for the κ of session 2. The between and within subjects variances were computed and ICC values for κ were derived following the formulae above.