dataesr/scientific-paragraphs-categorization huggingface dataset:
nilu. no/default. aspx according to the EBAS and respective project (for example ACTRIS) data policy. The Supplement related to this article is available online at doi:10. 5194/amt-10-1043-2017-supplement. Competing interests. Luka Drinovec and Griša Močnik are employed by the company Aerosol d. o. o. , which develops and manufactures aerosol instrumentation, including the Aethalometer AE33 and the drier used in the campaigns. Hans Moosmüller is a coinventor of the photoacoustic instruments (PASS and PAX) manufactured by Droplet Measurement Technologies and receives licensing fees from their sale.
Acknowledgements. The work described herein was supported by  -EUROSTARS  grant E!4825 FC Aeth; -JR-KROP grant  3211-11-000519 ; -JR-RK grant  3330-14-509063; -ACTRIS EU-FP7  grant no. 262254 ; -" PREQUALIF " project (ADEME contract no. 1132C0020 ),  DIM R2DS  ( AAP 2010 ); -" PARTICUL'AIR " project, and institutions  CNRS ,  CEA  and FEON; - National Science Foundation (NSF)  under Cooperative Support Agreement No. EPS-0814372 ; - NASA EPSCoR  under Cooperative Agreement No. NNX14AN24A ; - NASA ROSES  under grant no. NNX15AI48G ; - NSF  under grant no. AGS-1544425. We thank the  Slovenian Environmental Agency  and AMES d. o. o. for the use of their measurement sites. Edited by: W. Maenhaut Reviewed by: three anonymous referees.
The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of the assimilation impact is conducted using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observationbased estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.
e. no assimilation) simulation. Following their work, the present study tests the assimilation of both satellite-derived SSM and LAI on the continental scale. Further steps are made by the following: using the most recent SURFEX_v8. 0 Offline Data Assimilation implementation; considering a much larger domain (Europe and the Mediterranean basin) as well as a longer time period, 2000-2012; using the multi-layer soil diffusion scheme of ISBA developed by  Decharme et al. (2011). assimilating a long-term, global-scale, multi-sensor and satellite-derived surface soil moisture dataset (ESA CCI SSM,  Liu et al. , 2011 Liu et al. , , 2012;; Dorigo et al. , 2015 Dorigo et al. , , 2017) )  along with satellite-derived LAI (GEOV1, http://land. copernicus. eu/global/); using the modified version of the WFDEI (WATCH-Forcing-Data-ERA-Interim) observation-based atmospheric forcing dataset  (Weedon et al.
, 2011 (Weedon et al. , , 2014) )  from the eartH2Observe project  (Schellekens et al. , 2017) ; having a daily interactive coupling between ISBA and the CNRM (Centre National de Recherches Météorologiques) version of the TRIP (Total Runoff Integrating Pathways,  Oki et al. , 1998)  river routing model (CTRIP hereafter) to simulate hydrological variables such as the river flow  (Decharme et al. , 2010). Section 2 presents the LDAS-Monde system, i. e. (i) the CO 2responsive version of the ISBA LSM and the soil diffusion scheme, (ii) the CTRIP hydrological model and its coupling with ISBA, (iii) the atmospheric forcing used to drive the system, (iv) the equations of the SEKF, and (v) the assimilated remotely sensed observation dataset as well as the datasets used to assess the analysis impact.
SURFEX offline data assimilation The SURFEX modelling system includes the ISBA land surface model  (Noilhan and Mahfouf, 1996)  to calculate the soil, vegetation, snow energy and water budgets and is coupled to the TRIP  (Oki et al. , 1998)   et al. , 2017). The system is driven by WFDEI observationbased atmospheric forcing dataset  (Weedon et al. , 2011 (Weedon et al. , , 2014)). The main components of the LDAS (LSM, river routing system, analysis scheme and atmospheric forcing) are described in the following sections.
, 2013). A discretization with 14 layers over 12 m depth is used. The lower boundary of each layer is 0. 01, 0. 04, 0. 1, 0. 2, 0. 4, 0. 6, 0. 8, 1, 1. 5, 2, 3, 5, 8 and 12 m deep (see Fig. 1  of  Decharme et al. , 2011). The amounts of clay, sand and organic carbon present in the soil are determined by thermal and hydrodynamic soil properties  (Decharme et al. , 2016)  and are taken from the Harmonised World Soil Database (HWSD,  Wieder et al. , 2014). As for hydrology, the infiltration, surface evaporation and total runoff are accounted for in the soil water balance. The discrepancy between the surface runoff and the throughfall rate is defined by the infiltration rate. The throughfall rate is defined as the sum of rainfall that is not intercepted by the canopy, dripping from the canopy (interception reservoir), and snow melt water.
, 2010; Barbu et al. , 2011 Barbu et al. , , 2014;; de Rosnay et al. , 2013; Fairbairn et al. , 2015 Fairbairn et al. , , 2017) )  a fixed estimate of the background-error variances and zero covariances at the start of each cycle are used, leading to a SEKF. The initial state at the start of a 24 h assimilation window is analysed by assimilating the observations available over the previous 24 h assimilation window. This approach is similar to the "simplified 2-D-Var (two-dimensional variational data assimilation scheme)" proposed by  Balsamo et al. (2004) , but the increments are applied at the final timestep of the 24 h assimilation window. Draper et al. (2009)  found that the SEKF could generate flow dependence from the 24 h assimilation window and that cycling the background-error covariance (as in the EKF) gave no additional benefit.
WFDEI observation-based atmospheric forcing dataset Atmospheric forcing from the WFDEI dataset  (Weedon et al. , 2011 (Weedon et al. , , 2014) )  is used to drive the LDAS. It spans the period 1979-2012 and contains 3-hourly time intervals of wind speed, atmospheric pressure, air temperature (averaged values are used), air humidity, incoming shortwave and longwave radiations, and solid and liquid precipitation. WFDEI originates from the ECMWF ERA-Interim reanalysis  (Dee et al. , 2011) , interpolated to a spatial resolution of 0. 5.
ESA CCI surface soil moisture This study makes use of a multi-sensor, long-term and global satellite-derived surface soil moisture dataset  (Liu et al. , 2011 (Liu et al. , , 2012;; Wagner et al. , 2012; Dorigo et al. , 2015 Dorigo et al. , , 2017) )   Albergel et al. , 2013a, b; Dorigo et al. , 2015)  have highlighted the quality and stability over time of the product. Despite some limitations, this dataset has shown potential for assessing model performance  (Szczypta et al. , 2014 ; To assimilate SM data, it is important to rescale the observations such that they are consistent with the model climatology  (Reichle and Koster, 2004; Drusch et al. , 2005). The climatology of the SM dataset is defined by the specific mean value, variability and dynamical range. The ISBA model climatology for each grid point is dependent on the dynamical range, which is calculated from the wilting point and field capacity parameters (functions of soil texture types).
GEOV1 leaf area index The GEOV1 LAI is produced by the European Copernicus Global Land Service project (http://land. copernicus. eu/ global/). The LAI observations are retrieved from the SPOT-VGT and PROBA-V (from 1999 to present) satellite data according to the methodology discussed in  Baret et al. (2013). Following  Barbu et al. (2014) , the 1 km resolution observations are interpolated by an arithmetic average to the 0. 5 • model grid points, as long as 50 % of the observation grid points are observed (half the maximum amount). LAI observations have a temporal frequency of 10 days. Both SSM and LAI observed datasets are illustrated in Fig. 1 , which presents averaged values over 2000-2012. Figure  1  also illustrates the studied domain.
Evaluation datasets and strategies A common diagnostic in data assimilation is to compute Eq. (1) differences between the assimilated observations and the model background, called the innovations, and Eq. (  2 ) differences between the assimilated observations and the analysis, called the residuals  (Barbu et al. , 2011). Assuming that the system is working well, residuals have to be reduced compared to the innovations. After evaluating innovations and residuals of SSM and LAI, analysis impact is assessed using Eq. (1) agricultural statistics over France, Eq. (2) observed river discharge over Europe, Eq. (  3 ) satellite-derived estimates of terrestrial evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM,  Martens et al. , 2017)  and Eq. (  4 ) spatially gridded estimates of upscaled GPP and evapotranspiration from the FLUXNET network  (Jung et al.
Yearly statistical surveys over France are provided by the Agreste portal (http://agreste. agriculture. gouv. fr/). This has enabled a database of annual straw-cereal grain yield (GY) values to be established. The GY estimates are available according to administrative unit (département) and per crop type. Following  Calvet et al. (2012) ,  Canal et al. (2014)  and  Dewaele et al. (2017) , the GY values for rainfed straw cereals over 45 départements are used, which include barley, oat, rye, triticale and wheat. Simulated and analysed annual maximum of B ag are compared to GY estimates following the methodology from  Dewaele et al. (2017). Although SURFEX does not directly represent GY, it is assumed that the regional-scale simulations of above-ground biomass from a generic LSM can provide the inter-annual variability as a proxy for GY  (Calvet et al.
, 2011). It is a useful validation tool given that such quantities are difficult to measure directly on large scales. The global evaporation model in GLEAM is mainly driven by microwave remote sensing observations, while potential evaporation rates are constrained by satellite-derived SM data. It is a well-established dataset that has been widely used to study trends and spatial variability in the hydrological cycle (e. g. Jasechko et al. , 2013; Greve et al. , 2014; Miralles et al. , 2014a; Zhang et al. , 2016)  and land-atmosphere feedbacks (e. g. Miralles et al. , 2014b; Guillod et al. , 2015). This study makes use of the latest version available, v3. 0. It is a 35-year dataset spanning from 1980 to 2014 and is derived from a variety of sources, namely vegetation optical depth (VOD) and snow water equivalents (SWEs), satellite-derived SM, reanalysis air temperature and radiation, and a multisource precipitation product  (Martens et al.
, 2017). It is available at a spatial resolution of 0. 25 •. Martens et al. (2017)  provide a full description of the dataset, including an extensive validation using measurements from 64 eddy-covariance towers worldwide. The upscaled FLUXNET GPP and evapotranspiration were derived from the FLUXNET network using a model tree ensemble (FLUXNET-MTE hereafter) approach as described in  Jung et al. (2009). It is a machine learning technique that can be trained to ascertain land-atmosphere fluxes, providing a way of benchmarking LSMs on large scales  (Jung et al. , 2009 (Jung et al. , , 2010;; Beer et al. , 2010; Bonan et al. , 2011; Jung et al. , 2011; Slevin et al. , 2017). The machine learning algorithm is trained using a combination of land cover data, observed meteorological data and remotely sensed vegetation properties (fraction of absorbed photosynthetic active radiation).
The algorithm uses model tree ensembles to provide estimates of carbon fluxes at FLUXNET sites with available quality-filtered flux data, after which the trained model can be implemented globally using grids of the input data  (Jung et al. , 2009 (Jung et al. , , 2011)). It is limited to a 0. 5 • spatial resolution and a monthly temporal resolution over a 20-year period. It can be found in the Max Planck Institute for Biogeochemistry Data Portal (https://www. bgc-jena. mpg. de/ geodb/projects/Home. php).
Consistency between the model and observations Consistency over time is crucial when assimilating long-term datasets. Several authors assessed the consistency of the ESA CCI soil moisture product with respect to re-analysis products (e. g. Loew et al. , 2013; Albergel et al. , 2013a, b)  and in situ measurements  (Dorigo et al. , 2015 (Dorigo et al. , , 2017)). Lambin et al. (1999)  found that the GEOV1 LAI dataset is also consistent over time and can be used, for example, for detection of change and for providing information on shifting trends or trajectories in land use and cover change. To verify the results from literature for the spatial and temporal domain considered in this study, a consistency evaluation both for SSM and LAI against the open-loop experiment has been performed. As observed SSM climatology is matched to the model climatology (see Sect.
Model sensitivity to observations The Jacobians, H (Eq. 4), are dependent on the model physics. The examination of the Jacobians provides useful insight into explaining the data assimilation system performances  (Barbu et al. , 2011; Fairbairn et al. , 2017). Median values over 2000-2012 are presented in Table  2. The model equivalent of SSM is the second layer of soil (w 2 between 1 and 4 cm depth). It is then expected that the sensitivity of SSM to changes in soil moisture of that layer is higher than those of the other layers of soil. Sensitivity of LAI to changes in soil moisture (Table  2 , bottom rows) suggests that control variables related to soil moisture will also be impacted by the assimilation of LAI. The model sensitivity to SSM decreases with depth, as presented in Table  2  revealing that the assimilation of SSM will be more effective in modifying soil moisture from the first layers.
Results are illustrated by Figs. 12e-h and 14e, f. They are similar to those obtained using GLEAM estimates: over the whole domain most of the pixels present negative values for differences in RMSD (70 %), and most of the pixels present positive values for differences in correlation (79 %). As for evapotranspiration, GPP from both the open loop and the analysis are compared to monthly GPP estimates from FLUXNET-MTE dataset. Figure  12  illustrates averaged carbon uptake by GPP over land for 2000-2011 from the open loop (Fig. 13a ), FLUXNET-MTE (Fig. 13b ) and the analysis (Fig. 13c ) as well as differences between the analysis and the model (Fig. 13d ). Also, Fig. 14c  and d   Evensen, 1994) , to date SEKF is the most mature technique developed for land surface data assimilation within SUR-FEX.
Future work will investigate having spatially and temporally variant observations errors, based on statistical methods already applied to the ESA CCI SSM dataset such as triple collocation  (Dorigo et al. , 2015)  or error decomposition  (Su et al. , 2016). Having LAI estimates every 10 days while using 24 h assimilation window may also trigger analysis discrepancies, as between two LAI assimilations the system relies only on SSM assimilation. When a large analysis update occurs on LAI (from the assimilation of LAI), it then tends to go back towards the model states in the successive days before being constrained again by the next observations. For instance, in winter most of the ∂LAI t ∂LAI 0 Jacobians are equal (or close) to zero and therefore the analysis update caused the LAI to return almost instantaneously to the incorrect LAI minimum value.
show RMSD and correlation differences between the open loop or the analysis and FLUXNET-MTE dataset (analysis minus open loop). Finally Fig. 15 presents seasonal scores over the same period (Fig. 15a: RMSD values; Fig. 15b: correlation values). Compared to the FLUXNET-MTE estimates, the open loop tends to underestimate GPP over the Scandinavian countries, the northwestern part of France, the UK and Ireland, and north of the Caspian Sea, while an overestimation is visible over most of the Iberian peninsula, eastern Europe and the north-eastern part of the domain (Fig. 14a and b). From Figs. 14d, e and 15 one may notice that after.
Acknowledgements. The work of  Simon Munier  was supported by  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603608 ,  "Global Earth Observation  for integrated water resource assessment" (eartH2Observe). The work of  Hélène Dewaele  was supported by  CNES  and by  Météo-France. The work of  Emiliano Gelati  was supported by  French   REMEMBER  project ( ANR 2012 SOC&ENV 001 ) within the  HYMEX initiative. Wouter Dorigo  is co-funded by the " TU Wien Wissenschaftspreis 2015 ", a personal grant awarded by the  Vienna University of Technology. The authors acknowledge  ESA ' s Climate Change Initiative for Soil Moisture  (Contract No. 4000104814/11/I-NB  and  4000112226/14/I-NB ) and the  EU   FP7   EartH2Observe  project (grant agreement number  331 603608 ) for supporting the development and evolution of the  ESA CCI SM  product.
Results RT Abnormalities in HGPS. We measured the RT programs of progeroid and normal fibroblasts and characterized changes in RT upon reprogramming to iPSCs and redifferentiation back to fibroblasts. Overall, we generated 61 genome-wide RT datasets of fibroblasts, iPSCs, and redifferentiated cells derived from progeroid patients and healthy donors (Fig. 1A  and Table  S1 ). We first confirmed the known HGPS cellular abnormalities  (13, 14) , such as altered nuclear morphology and increased number and size of γH2AX foci associated with DNA damage (Fig. 1  B and C). Since earlier studies observed similar alterations in cells undergoing in vitro-induced senescence  (15, 16) , we also analyzed cells entering replicative or oncogene-induced senescence (OIS). Consistent with previous findings demonstrating that RT is a highly stable epigenetic property  (17) , we found strong genome-wide correlation of RT among all samples (Fig.
2B ). Moreover, we identified a specific RT signature containing regions that replicate early only in progeria (E-progeria regions) but replicate later in cells from all healthy donors, including cells from naturally aging donors, and in cells entering cellular senescence (Fig. 2 B-D ). Although several genomic regions replicate later during early S phase in progeria than in the age-matched controls (Fig. 2A ), we did not identify an RT signature of chromosomal segments that replicates late during S phase in progeria but early in all normal cells (Fig. 2B ). The progeria-specific RT signature includes only 1. 6% of the genome (Dataset S1). Additional RT signatures identified differences between fetal cells and all postnatal fibroblasts (Fig. 2  B and E and Dataset S1); however, all the fetal datasets were derived from a single cell line (IMR90), so their significance is uncertain.
3A ). Remarkably, multiple GO terms associated with TP63 match the progeroid pathophysiological symptoms, suggesting that TP63 is associated with the disease phenotype (Fig. 3A ). TP63 alterations have not been previously observed in progeroid patients but have been observed in other disorders characterized by developmental abnormalities. TP63 replicates early only in progeria cells but replicates late in fibroblasts from all healthy donors (Fig. 3B ); since RT is often correlated with transcriptional activity  (11, 12) , we reasoned that abnormal TP63 RT might be associated with altered gene regulation. Consistently, analysis of datasets obtained from a previous study  (18)  showed depletion of H3K27me3 throughout the TP63 locus relative to healthy donors (Fig. 3C ). Importantly, these datasets were derived from one of the patient cell lines analyzed here for RT and gene expression, cell line HGADFN167.
The highest expression was detected in skin, and ΔNp63 was the predominant family of isoforms (Fig. 3E ). Since GTex data derive from heterogeneous cell populations within each tissue, we analyzed transcriptome data from earlier studies  (7, 18)  by extracting specific microarray probes that hybridize to each isoform. We found a slight but significant unbalanced expression of TP63 in HGPS (not previously reported), with TAp63 expressed at higher levels than ΔNp63 isoforms (Fig. S2 ). To validate these findings in the cells from this study, we analyzed isoform-specific expression by qRT-PCR. We detected similar expression levels of both TP63 isoforms in cells derived from young healthy donors but higher levels of TAp63 isoforms in cells derived from HGPS patients (Fig. 3F ). Our results demonstrate that abnormal RT of TP63 is correlated with an altered ratio of TP63 variants in the progeroid diseases.
Materials and Methods Primary fibroblasts obtained from Coriell Institute for Medical Research), the Progeria Research Foundation, and from donors from the Centre Hospitalier Régional Universitaire (CHRU) Montpellier cohort (Table  S1 ) were reprogrammed to iPSCs and redifferentiated back to fibroblasts. All human cell lines were managed according to the guidelines in the Declaration of Helsinki, and donor privacy and confidentiality were ensured by the cell banks' blinding of samples with a numerical identification code. Experimental details are described in SI Materials and Methods. All datasets generated in this study are deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (https:// www. ncbi. nlm. nih. gov/geo/; GEO series GSE98475) and on The Florida State University ReplicationDomain database at www.
ACKNOWLEDGMENTS. We thank  Ruth A. Didier  for assistance with flow cytometry,  Jared Zimmerman  for data deposition in public repositories,  Nard Kubben  and  Tom Misteli  for sharing the progerin-GFP and lamin A-GFP cell lines,  Armando Aranda-Anzaldo  for critical reading of the manuscript, and la  Région Languedoc Roussillon/Occitanie  for a Ph. D. Student Fellowship. This work was supported by  NIH  Grants  GM083337  and  GM085354  (to D. M. G. ), la  Ligue Nationale Contre le Cancer Grant  " Programme Labellisation Equipe 2015 " ( EL2015. LNCC/JML  to J. -M. L. ),  INGESTEM National Infrastructure in Biology and Health , and le  Centres Hospitaliers Universitaires de Montpellier.
Recently, new approaches which make use of sparse representations of MEG/EEG signals have increasingly become the focus of attention, for example in several variations of the matching pursuit (MP) algorithm originally published by  Mallat and Zhang (1993)  in their seminal work. MP is an adaptive approximation procedure applied to the data of interest, for example time series in MEG or EEG recordings. MP iteratively decomposes these data such that a linear expansion of so-called atoms-each of which represents a distinct component of the underlying data-and an unexplained residual assumed to represent noise are obtained. The atoms are elements of a large set of functions commonly referred to as dictionary. Most often Gabor functions are used, since they offer the best trade-off between time and frequency resolution.
We termed this approach spatio-temporal matching pursuit (STMP), since-contrary to the aforementioned approaches in which spatial and temporal decompositions of the MEG signal are performed sequentially-STMP simultaneously realizes a spatial and temporal decomposition of the MEG signal in each iteration by means of a spatio-temporal dictionary. STMP is a source localization strategy for MEG/EEG data in which each signal component represents the activity of exactly one dipolar source. The STMP dictionary is composed of combinations of the elements of a large set of dipolar fields (spatial subdictionary), each originating from an ECD located on the surface of the tessellated cortex, to address the spatial aspect of the MEG data to be parametrized, and of the elements of a large set of chirplets (temporal subdictionary) to address the temporal aspect.