dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Dr. Mike Murphy  for providing mitoQ and  Dr. Harold A. Burgess  for providing Flote software and advice with the kinematic analysis. We are also grateful to  Dr. A. Sánchez-Chardi  and the staff at the  Servei de Microscopia of the Universitat Autònoma de Barcelona  for their support in TEM processing and observation, and to  Claudia Rivetti  for her relevant support in the chemical analysis. The F59 antibody, developed by  Frank E. Stockdale , was obtained from the  Developmental Studies Hybridoma Bank , created by the  NICHD  of the  NIH  and maintained at The  University of Iowa, Department of Biology, Iowa City ,  IA 52242. This work was supported in part by the  US Army ERDC-IRO  ( W912HZ-13-BAA-01 ; D. R. , N. G. R. , and P. J. B. ) and  Environmental Quality Research Program  (N.
Ethics approval was granted by the Research Ethics Board of the Royal Ottawa Mental Health Center, the University of Ottawa Faculty of Social Sciences and Humanities, and experimental administration of the drugs was approved by Health Canada (REB#2008-019). Written, informed consent was obtained from all participants. The study ran from June 2010 to January 2014. Polysomnographic data for the entire sample of participants (irrespective of genotype) was previously published in a report on the effects of galantamine and buspirone on sleep. 16.
Statistical analysis All variables were assessed for normal distribution via normality plots and measures of kurtosis and skew using the SPSS statistical program (IBM Corporation, Armonk, NY, USA). Variance stabilizing transformations were done if the plot showed significant deviation from a normal distribution (logit and natural log transforms were used) and if the z scores of the skew or kurtosis were higher than 1. 96. The nonnormal variables were: sleep onset latency, Wake%, N1%, Awakenings, and SE. These variables were normalized with a natural log transform except for SE, which required a logit transform. These data were submitted to a mixed design, repeated measures multivariate analysis of variance (MANOVA), with independent genotype groups and repeated measures over drug conditions.
Acknowledgment This research was supported by the  University of Ottawa Institute of Mental Health Research. The authors would like to acknowledge and thank  Dr Paul Albert  and technician  Mireille Daigle  from the  Ottawa Hospital Research Institute  ( ON, Canada ) for their expertise in genetic sequencing,  Dr Roseanne Armitage  for help in the preparation of this manuscript, and  Lorelle Weiss  and  Elena Drozd  for their help in collecting the data.
ORCHIDEE ORCHIDEE is a global land surface model, which can be used coupled to the Institut Pierre Simon Laplace (IPSL) climate model or driven offline by prescribed atmospheric forcing  (Krinner et al. , 2005). ORCHIDEE computes all the soil-atmosphere-vegetation-relevant energy and water exchange processes in 30 min time steps. It combines a soilvegetation-atmosphere transfer model with a carbon cycle module, computing vertically detailed soil carbon dynamics. The high latitude version of ORCHIDEE includes a dynamic three-layer snow module  (Wang et al. , 2013) , soil freeze-thaw processes  (Gouttevin et al. , 2012a) , and a vertical permafrost soil thermal and carbon module  (Koven et al. , 2011). The soil hydrology is vertically discretized as 11 numerical nodes with 2 m depth  (Gouttevin et al.
Intercomparison setup and simulation protocol In order solely to compare model representations of physical processes and to eliminate any other source of uncertainty (e. g. , climate forcing, spatial resolution, soil parameters), model simulations were driven by the same atmospheric forcing and soil properties at site-scale. Driving data for all site simulations were prepared and distributed uniformly. Site observations were converted into continuous time series with minor gap-filling. Where the observed variable set lacked the variable needed by the models, extended WATCH reanalysis data  (Weedon et al. , 2010; Beer et al. , 2014)  were used to complement the data sets. Soil thermal properties are based on the sand, silt, and clay fractions of the Harmonized World Soil database v1. 1  (FAO et al.
Acknowledgements. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7 2007-2013)  under grant agreement no. 238366. Authors also acknowledge the  BMBF  project  CarboPerm  for the funding. Nuuk site monitoring data for this paper were provided by the  GeoBasis program  run by  Department of Geography, University of Copenhagen and Department of Bioscience, Aarhus University, Denmark. The program is part of the  Greenland Environmental Monitoring (GEM) Program  (www. g-e-m. dk)and financed by the  Danish Environmental Protection Agency ,  Danish Ministry of the Environment. We would like to acknowledge a grant of the  Swiss National Science Foundation  (Sinergia  TEMPS  project, no. CRSII2 136279 ) for the COUP model intercomparison, as well as the  Swiss PERMOS network  for the Schilthorn data provided.
Authors also acknowledge financial support from  DE-FROST , a  Nordic Centre of Excellence (NCoE)  under the  Nordic Top-level Research Initiative (TRI) , and the  Lund University Centre for Studies of Carbon Cycle and Climate Interactions (LUCCI). Eleanor Burke  was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) and the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 282700 , which also provided the Samoylov site data. The article processing charges for this open-access publication were covered by the  Max Planck Society. Edited by:  T. Zhang.
Specific corrections are applied over cloudy tropical regions and high latitudes in the Northern Hemisphere where the low number of available observations compromises the reliability of estimates. Artefacts over evergreen broadleaf forests and areas of bare soil are corrected based on the expected limited seasonality. The GEOCLIM data set is demonstrated to be consistent, both spatially and temporally. GEOCLIM shows absolute differences lower than 0. 5 compared with MODIS (GIMMS3g) climatology of LAI for more than 80% (90%) of land pixels, with higher discrepancies in tropical and boreal latitudes. ECOCLIMAP systematically produces higher LAI values. The phenological metric for the date of maximum foliar development derived from GEOCLIM is spatially consistent (correlation higher than 0. 9) with those of MODIS, GIMMS3g, ECOCLIMAP and MCD12Q2 with average differences within 14 days at the global scale.
2013). The European Copernicus Global Land Service delivers global LAI, FAPAR, and FCOVER products from SPOT VEGETATION data from 1999 to the present with a spatial sampling close to 1 km. The products, known as GEOV1 products, have benefitted from the development and validation of existing products  (Baret et al. 2013). Camacho et al (2013)  demonstrated that GEOV1 products were more accurate and precise than current products. Some land-surface models (LSMs) for simulating terrestrial water and carbon cycles use the spatiotemporal variation of LAI or FAPAR, described by different lookup tables depending on the type of vegetation  (Viterbo and Beljaars 1995). The availability of satellite data in the last two decades describing the state and evolution of vegetation has allowed a better integration of biophysical variables into LSMs.
2010; Verger et al. 2014a). Despite the significance of global phenology for earth system monitoring and modelling, there are few data sets that explicitly describe the annual vegetation cycle at global scale. Boussetta et al. (2013)  derived a monthly LAI climatology from 2000-2008 MODIS observations to be used in a numerical weather prediction model as indicator of the leaf development stage. The ECOCLIMAP programme  (Faroux et al. 2013; Masson et al. 2003 ) is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters (including LAI, FAPAR and FCOVER) that are primarily mandatory in meteorological modelling for soil/vegetation-atmosphere transfer schemes. Other studies focus on the time variation of vegetation indices to propose a global normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) reference data set for land-surface phenology using 13 years of  VEGETATION observations (Verhegghen et al.
2014). The climatology should simulate the data as closely as possible, i. e. it does not use existing land-cover maps or a model to describe the seasonal dynamics, thereby preventing the introduction of possible artefacts due to the lack of realism of the model used. GEOV1 time series from 1999 to 2010, corresponding to 12 years of estimates of biophysical variables at a spatial resolution of 1 km and a frequency of 10 days are used. The climatology is computed for each pixel as the average for a given date in a year across all years of the time series. The associated standard deviation characterises the interannual variability. We will first describe the methodology and the data sets used to produce GEOCLIM. We will then evaluate GEOCLIM based on its main spatiotemporal features and its performance relative to other climatology data sets derived from AVHRR and MODIS data, with special emphasis on seasonality and the derived phenology.
ECOCLIMAP product ECOCLIMAP is a database at 1/112º resolution on a Plate-Carrée grid resolution that includes a classification of ecosystems and a consistent set of associated land-surface variables, including LAI, at 10-day temporal sampling  (Faroux et al. 2013). We used the latest Open-ECOCLIMAP v1 The ECOCLIMAP LAI data set at the original 1/112º resolution was aggregated at 0. 5º spatial sampling and averaged at monthly temporal sampling.
GIMMS3g climatology The GIMMS3g LAI product derived from AVHRR data is available at 15-day temporal steps and 1/12º spatial resolution for the period July 1981 to December 2011. The principles used for the generation of this LAI data set are based on the use of neural networks which were trained first with GIMMS NDVI3g and MODIS LAI products for the overlapping period 2000-2009. The trained neural network algorithm is then applied using the land-cover class, the latitude and longitude coordinates, and the NDVI3g as the input data to generate the full temporal coverage of the GIMMS3g LAI data set. Further details on the algorithm for GIMMS3g retrieval can be found in  Zhu et al. (2013). We derived the GIMMS3g climatology as the interannual mean of GIMMS3g LAI time series at 15-day temporal step for the period 1999-2010.
Zhang et al (2006)  compared the MCD12Q2 parameter to in-situ measurements and found that it corresponded to the time at which 85-90% of the individual leaves reached their final size. The MCD12Q2 500 m sinusoidal product was projected onto a 0. 5º Plate-Carrée projection using the MODIS re-projection tool (www3). Yearly MCD12Q2 values from 2001 to 2010 were then averaged to provide a typical phenology for comparison with the phenological metrics derived from the LAI climatology data sets.
Comparison with ECOCLIMAP, MODIS and GIMMS3g climatologies The map of annual mean differences between GEOCLIM and the LAI climatologies derived from AVHRR and MODIS data (Figure  6 ) shows LAI differences of ±0. 5 for 54%, 83% and 91% of the land pixels as compared with ECOCLIMAP, MODIS and GIMMS3g, respectively. GEOCLIM produced systematically lower values than ECOCLIMAP for the remaining 46% of pixels, with larger differences for dense forests (northern boreal and tropical forests) but with significant differences also for crops (e. g. USA and eastern Asia) (Figure  6a ). These systematic negative bias of GEOCLIM as compared to ECOCLIMAP was evident across latitudes and along the year (Figure  7 ). GEOCLIM produced also systematically lower values than MODIS (Figure  6b ) over tropical forests with differences ~0.
[Figure 6] Despite the large discrepancies in the magnitude of LAI between the different datasets, due in part to the differences in sensors and retrieval algorithms, seasonality and its phasing generally agreed well (Figure  7 ). Seasonality was inverted in the Southern Hemisphere relative to the Northern Hemisphere (compare Figures  7a  and 7c ). In the Northern Hemisphere, LAI seasonality decreased in the length of season (active growth period) with latitude (compare Figures  7c  and 7d ). In the tropical latitudes (-20-10º) characterized by very limited seasonality GEOCLIM and GIMMS3g systematically showed lower values than MODIS and ECOCLIMAP (Figure  7b ). The largest differences in terms of seasonality were in the Northern Hemisphere at high latitudes  (40-70º)  where ECOCLIMAP produced longer growing seasons as compared to other LAI datasets and higher values in the period of active growth (Figure  7d ).
[Figure 7] Histograms of the LAIs (Figure  8 ) indicated very similar distributions between the different LAI datasets for shrubs/savannah/bare soil. Some similarities in the position of the maximum frequency were also observed for crops and grassland. Some discrepancies, however, were observed: ECOCLIMAP produced low frequencies for LAIs of zero at the expense of higher intermediate values, while GEOCLIM, MODIS and GIMMS3g produced a smoother transition. Forests had the largest discrepancies between the different data sets. GEOCLIM and GIMMS3g produced a bimodal distribution for deciduous broadleaf forests, with a peak for low values (LAI near 1) corresponding to the dormant period of the vegetation in winter and a second mode for the period of active growth with values higher than 6 in few occasions.
The high spatial and temporal resolution of forthcoming Sentinel2 sensors should improve the monitoring of vegetation in these problematic areas. GEOCLIM was indirectly validated based on the comparison with AVHRR and MODIS derived climatologies of LAI. Multitemporal ground data would be preferable for validating GEOCLIM but unfortunately were rarely available. GEOCLIM showed a high agreement with MODIS (GIMMS3g) climatology of LAI and absolute differences were higher than the Global Climate Observing System (GCOS 2010) requirements for accuracy, i. e. 0. 5 LAI, only in northern boreal and tropical forests representing less than 20% (10%) of land pixels. GEOCLIM systematically produced lower values than MODIS over evergreen broadleaf forest as also observed in the comparison between GEOV1 and MODIS  (Camacho et al.
2013). Further, the strong bidirectional effects of surface-reflectance at very high latitudes are not well simulated by the radiative transfer models currently used for product generation  (Yang et al. 2006a ). In addition, the understory and foliage clumping are not well accounted for  (Jiao et al. 2014; Pisek et al. 2010 ). LAI values were systematically higher for ECOCLIMAP than for GEOCLIM, MODIS and GIMMS3g. Boussetta et al (2013)  reported similar higher LAI values for ECOCLIMAP than for MODIS. Garrigues et al (2008)  also reported large positive biases for ECOCLIMAP compared with CYCLOPES, MODIS and GLOBCARBON and with ground measurements. The differences in the temporal period, input data and sensors (VEGETATION for GEOCLIM and AVHRR and MODIS for ECOCLIMAP, Section 3. 1) can partially account for the significant discrepancies between GEOCLIM and ECOCLIMAP although the relatively good agreement of GEOCLIM with MODIS and GIMMS3g AVHRR derived LAI climatologies indicates that the major source of discrepancies are related to the retrieval algorithms.
The linear relationship between NDVI and LAI used to retrieve the ECOCLIMAP product for pixels out of Europe  (Masson et al. 2003 ) may have introduced some overestimation because the LAI-NDVI relationship is exponential and saturates at medium to high values (e. g. Myneni et al. (2002) ). Since ECOCLIMAP assumes low spatial variability within each class of land cover, it is limited to capture the LAI spatial variability as compared to other LAI datasets  (Garrigues et al. 2008 ). Nevertheless, identifying the source of the differences between ECOCLIMAP and the other LAI datasets being analyzed would require further attention and it is out of the scope of this paper. Further research should focus on the development of improved LAI datasets with due attention to areas (boreal and tropical latitudes) and periods (winter time) where higher uncertainties exist  (Fang et al.
Conclusions This article has presented and provided a first quality assessment of GEOCLIM-a global climatology of LAI, FAPAR and FCOVER-from the multiannual time-series of 10-days and 1-km GEOV1 products built from 1999 to 2010. Results showed GEOCLIM was temporally consistent for the seasonality across biomes and latitudes. GEOCLIM showed a high agreement with MODIS and AVHRR climatologies of LAI: differences within the Global Climate Observing System requirements, i. e. ±0. 5 LAI, in more than 80% (90%) of GEOCLIM land pixels compared with MODIS (GIMMS3g). ECOCLIMAP systematically produced higher LAI values. Further research should focus on tropical and boreal latitudes where higher uncertainties exist in the LAI datasets. The phenology of the timing of maximum foliar development derived from GEOCLIM constituted an intermediate solution between those of GIMMS3g, MODIS, ECOCLIMAP and MCD12Q2: differences within 14 days and spatial correlation >0.
9 at the global scale. The GEOCLIM data set is continuous, both spatially and temporally, and it can be used for a wide range of land-biosphere applications. It may contribute to a better characterisation of the seasonal variability of vegetation in global land-surface models. It provides the baseline characteristics of the seasonal cycle of LAI, FAPAR, and FCOVER for the identification of anomalies and trends in global vegetation. for maximum foliar development derived from GEOCLIM and mean differences as compared to the phenological metrics derived from ECOCLIMAP, MODIS, GIMMS3g and MCD12Q2.
This section assesses the performance of GEOCLIM. The validation data sets are first described. The spatial and temporal consistencies of GEOCLIM are discussed and evaluated by comparison with climatologies derived from both AVHRR and MODIS data. For the sake of brevity, the results focus on LAI, because of the three variables LAI, FAPAR, and FCOVER, LAI is used most by the scientific community. The comparison was performed at 0. 5º spatial sampling on a Plate-Carrée grid. The 0. 5º spatial resolution corresponds to the typical resolution of global models and reduces computation time. For comparison purposes, the different LAI data sets were averaged at monthly time step because one-month corresponds to the lowest temporal sampling among the validation datasets.
version, available since June 2014 at www2. It combines the global database of the first version, ECOCLIMAP-I, and an upgraded version, ECOCLIMAP-II, for Europe. ECOCLIMAP-I contains 215 ecosystems obtained by combining existing land covers, climatic maps, and NDVI seasonal profiles from AVHRR data acquired between April 1992 and March 1993 (Masson et al. 2003). For each class of vegetation, the maximum and minimum LAI values are fixed based on in-situ knowledge, and the annual cycle of LAI is constrained by the NDVI AVHRR temporal profiles using a linear relationship between NDVI and LAI. The second version, ECOCLIMAP-II, contains 573 ecosystems across Europe based on more recent land-cover maps, and the annual LAI profiles are derived from MODIS Collection 5 for the years 2002-2006 (Faroux et al.
Acknowledgements This research was partially supported by the European Earth observation programme Copernicus (388533),  Pôle Thématique Surfaces Continentales THEIA  ( CNES 140570 ) and the  FP7 geoland2  ( 218795 ), GIOBIO ( 32-566 ) and LONGLOVE ( 32-594 ) projects. This research was also supported by the  Spanish Government  grant  CGL2013-48074-P , the  Catalan Government  grant  SGR 2014-274 , and the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. Aleixandre Verger  is the recipient of a  Juan de la Cierva postdoctoral fellowship  from the  Spanish Ministry of Science and Innovation. The authors are indebted to  Ranga Myneni ,  Taejin Park , the  Boston University, MODIS ,  CNES ,  VITO , and  CNRM-GAME  ( Météo France, CNRS ) teams for providing access to the data.
WWW sites www1: GEOV1 Biophysical Products. http://land. copernicus. eu/global (last accessed 22 June2015) www2: ECOCLIMAP code and data https://opensource. cnrm-game-meteo. fr/projects/ecoclimap (last accessed 22 June2015) www3: MODIS Reprojection Tool https://lpdaac. usgs. gov/tools/modis_reprojection_tool (last accessed 22 June2015).
MATERIALS AND METHODS Ascertainment of SOX11 deletion (2p25. 2 deletions) and mutation cases Individuals with deletion of chromosome 2p25. 2, which included the SOX11 gene, were identified through the DECIPHER collaboration. Deletions were confirmed by FISH. None of the deletions identified were present in the recently published CNV map of the human genome, which integrates CNV data from healthy individuals from multiple data sets such as the database of genomic variants. 11 Two individuals with SOX11 mutations were identified in the deciphering developmental disorders (DDD) study (data freeze of 1133 children). DDD methodology has been described. 12 A third individual with a SOX11 mutation was identified by exome sequencing via the Genetics of Structural Brain Abnormalities and Learning Disabilities Study (Wales Research Ethics Committee 12/WA/ 0001).
Cell transfection and luciferase reporter assays The SOX11 open-reading frame clone was purchased from Promega (Tokyo, Japan) and SOX11 mutants (c. 150G>C; p. Lys50Asn and c. 359C>A; p. Pro120His) generated by sitedirected mutagenesis with the KOD-Plus-Mutagenesis Kit (Toyobo, Osaka, Japan). Wildtype (WT) and mutant SOX11 cDNAs were PCR amplified and cloned into the p3xFLAG-CMV-14 mammalian expression vector (Sigma, St Louis, Missouri, USA). The GDF5 promoter 5 0 -flanking sequence (-448/+319) was PCR amplified and cloned into the pGL3-basic vector (Promega). All constructs were verified by Sanger sequencing. Human SOX11 cDNA can be obtained from GenBank/EMBL/DDBJ nucleotide core database under the accession code AB028641. 1. Transfection and luciferase reporter assays were performed as previously described.
This was also predicted to interfere with DNA binding. Both variants are found at evolutionary conserved amino acids. Neither variant was found in the exAC database, nor was the c. 87C>A, p. Cys29* mutation. In vitro assessment of effect of SOX11 mutations on transcriptional activity Luciferase reporter assays in HeLa cells indicate that both the p. Lys50Asn and p. Pro120His variants display reduced ability to activate the GDF5 promoter compared with WT protein (figure  3 ).
Funding Funding for the project was provided by the Wellcome Trust. We declare that those who collected data and deposited it in the DECIPHER database bear no responsibility for its use and interpretation in the current work. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). Department of Health's National Institute for Health Research Biomedical Research Centres funding scheme. We also thank the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics (funded by Wellcome Trust grant reference 090532/Z/09/Z and Medical Research Council Hub grant G0900747 91070) for generating the sequencing data for case 10.