dataesr/scientific-paragraphs-categorization huggingface dataset:
This can be partially attributed to the precipitation that drives the models. For example ERA-Interim is known to have significant problems representing the West Africa Monsoon  [82]. The best remote sensing product in this region is LPRM, which gives high correlation values for the four networks, while the performances of other products are more variable. In terms of bias, the best datasets are the two SMOS-based ones, AMSR-E Reg and NN. AMSR-E Reg also gives the lowest STDD for two of the four networks. In these regions, the surface-atmosphere coupling is strong  [4] , but there are few in situ observations. The positive results of the remote sensing products show the interest of assimilating these data into numerical weather prediction models. All of the products perform very well for the OzNet networks in Australia, correctly reproducing the temporal evolution of the in situ time series (Pearson correlations 0.
7-0. 8), in particular when comparing with sensors sampling the 0-5-cm depth. Correlations remain high when comparing with the 0-8-cm sensors, except for AMSR-E NN and Reg. In North America, the dataset showing the best correlations is ERA-Land. Both models show higher correlations than remote sensing data for mountainous networks (SNOTEL and Reynolds Creek) and SCAN. In Little Washita, AMSR-E NN and CCI show a correlation close to ERA-Land (0. 70-0. 72 versus 0. 76), while in Little River LPRM, AMSR-E Reg, NN and ERA-Land show very similar correlation. AMSR-E NN shows the lowest mean STDD for most networks.
Conclusions A methodology to retrieve SM from AMSR-E has been discussed and evaluated. The method consists of searching for a global non-linear relationship linking AMSR-E T b 's and SMOS L3 SM to recompute an SM dataset from AMSR-E observations with no significant bias and a similar dynamical range to that of the SMOS L3 dataset. This study differs from that of Al Yaari et al. [24]  in that they calibrated point per point a simple linear regression using a physical-based equation. Here, a single global relation was studied. It has been shown that by using neural networks, it is possible to find such a single relationship mapping the space from AMSR-E T b 's to SMOS L3 SM. Therefore, in spite of the different frequencies and sensing depths of AMSR-E and SMOS, the latter SM dataset and the former T b 's are in good agreement.
To our knowledge, this is the first time that a global SM dataset derived from remote sensing observations has been used as a reference to train a neural network using past satellite mission observations. It has been shown that the new AMSR-E dataset compares well with the SMOS L3 SM dataset (within the typical error interval of the SMOS L3 SM). In particular, the bias is smaller than 0. 04 m 3 /m 3 by construction, with no need of a third-party dataset to perform a bias-correction or to apply any a posteriori statistical correction. This SMOS-based AMSR-E has been evaluated globally, along with other AMSR-E SM datasets and the CCI SM dataset, the MERRA-Land and ERA-Land models. The NN SM dataset show similar features with respect to models as the other remote sensing datasets. All of the AMSR-E SM datasets show a significant bias for boreal forest regions, in particular the LPRM one.
LPRM shows the best agreement with MERRA in terms of correlation over Australia. The three AMSR-E datasets show a better agreement in terms of correlation with ERA-Land over North America. The two SMOS-based AMSR-E datasets (NN and Reg) show a lower STTD and bias than LPRM in many regions. The AMSR-E SM NN dataset has also been evaluated against in situ measurements of soil moisture in a large number of sites (over 400). The same evaluation has been performed for the CCI SM, MERRA-Land, ERA-Land, AMSR-E LPRM and AMSR-E Reg. Models compare better to in situ measurements over Europe, in particular ERA-Land. This could be due to the large number of measurements constraining the numerical weather prediction models in this region and to the fact that the remote sensing observations could be polluted by artificial emission.
In contrast, in the Sahel area, remote sensing datasets show better statistics than models, in particular AMSR-E LPRM, in terms of correlation and AMSR-E NN in terms of bias and STDD. These results suggest the potential added value of assimilating remote sensing data into models (numerical weather predictions models, reanalysis, hydrological models) in this region due to the lack of in situ observations and the convective nature of precipitation. Moreover, this region has been also linked with a strong land-atmosphere coupling, which provides a further motivation to better constrain soil moisture evolution using remote sensing data. Over Australia, all SM products show a similar very good agreement with in situ measurements. In North America, MERRA and ERA models show higher correlations with in situ measurements over mountainous regions, but remote sensing datasets, in particular AMSR-E LPRM and NN, show similar statistics for the other networks.
In summary, the approach studied in this paper is a promising method to re-compute an SM dataset from a passive radiometer using as reference data an SM dataset produced with a different radiometer to obtain longer, consistent by construction, datasets. Acknowledgments: This research has been partly funded by ESA European Space Research Insitute (ESRIN) under Contract IPL-PSO/FF/vb/13. 886 EXPRO+ and by the TOSCA (Terre Ocean Surface Continental Atmosphère) program of "Centre National d'Etudes Spatiales" (CNES, France). The authors thank Patricia de Rosnay and Gianpaolo Balsamo from ECMWF for providing the ERA-Land data and useful comments, respectively. SMOS L3 data were obtained from the "Centre Aval de Traitement des Données SMOS" (CATDS), operated for the CNES (France) by "Institut Français de Recherche pour l'Exploitation de la Mer" (IFREMER) (Brest, France).
4. 4 Figure 2 shows a schematic representation of the methodology used in this study to retrieve an SMOS-like SM dataset from AMSR-E. Data from the 2010-2011 period with the filtering discussed in Section 2. 8 for the training phase were used for the NN models to learn the non-linear mapping between AMSR-E T b 's and SMOS L3 SM. The neural network training itself is described in Section 4. 1. Different neural network configurations have been tested. In a second step, one of the neural networks giving the best results is used to retrieve SM from AMSR-E T b 's in the 2003-2011 period (Section 4. 3). Finally, the retrieved dataset trained on SMOS L3 SM (AMSR-E NN SM) has been evaluated with respect to SMOS L3 SM in June 2010-October 2011 (Section 5. 1) and with respect to other AMSR-E SM datasets (Section 5.
Recently, this limitation has partly been overcome by processing less precise but numerous DEMs derived from optical stereo-imagery acquired by the Advanced Spaceborne Thermal Emission and Reflection (ASTER) sensor onboard the TERRA satellite. This multi-temporal ASTER DEM strategy has been pioneered over the Everest area  (Nuimura et al. , 2012)  and the Northern Patagonian Icefield  (Willis et al. , 2012a) , and later applied to the Southern Patagonian and Cordillera Darwin icefields  (Willis et al. , 2012b; Melkonian et al. , 2013) , to the Juneau Icefield in Alaska  (Melkonian et al. , 2014)  and to two glaciers in New-Zealand  (Wang and Kääb, 2015). Among these studies, only  Nuimura et al. (2012)  had some reference Global Navigation Satellite System (GNSS) data to evaluate the accuracy of their elevation change measurements on glaciers.
DATA ASTER DEMs Since December 1999, numerous stereo-images have been acquired by the ASTER sensor onboard the TERRA satellite platform. The Global Land Ice Measurement from Space (GLIMS) project has been coordinating the data acquisition requests from the glaciological community and has been suggesting the best acquisition parameters for ASTER images acquired over glaciated regions. Fine tuning the gain of the optical sensor is crucial to avoid saturation of images over bright snow-covered areas  (Raup et al. , 2000; Korona et al. , 2009). As a result of these efforts, a vast archive of ASTER stereo-images spanning over 16 years is available, with typically tens up to hundreds of images over a given site. We used the AST14DMO products available through the ECHO/REVERB website (http://reverb.
For example, in eastern Nyainqêntanglha Shan at the eastern termination of the Himalaya, recent estimates vary from 1. 7 m  (Gardelle et al. , 2013)  to 7-10 m  (Kääb et al. , 2015). This is an issue of high scientific importance because the SRTM DEM is widely-used and any error in the correction of this penetration depth (or even worse not accounting for it) directly leads to systematic errors in dh/dt and ultimately in geodetic glacier mass balances  (Berthier et al. , 2006; Fischer et al. , 2015). By temporally extrapolating dh/dt ASTER to the acquisition date of the SRTM DEM, we infer the SRTM C-Band penetration depth over Mont-Blanc glaciers. This method has two advantages over a similar method applied to ICESat data  (Kääb et al. , 2012). (1) From the sparse ICESat tracks, only a mean penetration depth over a sufficiently large region (typically thousands km 2 of glaciers) can be inferred whereas here values for individual glacier elevation band (typically larger than 1 km 2 ) are calculated.
ACKNOWLEDGMENTS We thank  Fanny Brun  for her comments on an earlier version of the manuscript. The constructive suggestions from the Associate Editor ( Francisco Navarro ) and two reviewers significantly improved the manuscript. Field measurements in the Mont-Blanc area were funded by the  GLACIOCLIM (Les GLACiers comme Observatoire du CLIMat ) programme. EB acknowledges support from the  French Space Agency (CNES)  through the TOSCA and ISIS programs and from the  Programme National de Télédétection Spatiale  (PNTS, http://www. insu. cnrs. fr/pnts),grant  PNTS-2016-01  and the  Agence Nationale de la Recherche (ANR)  Grant  ANR-12-BS06-0018  (SUMER). This work could not have been performed without the  GLIMS  project that allowed populating a vast archive of ASTER stereo images over glaciers.
(C) Secondary-structure-guided sequence alignment between the anti-cholera toxin VHH structural template (PDB code: 4IDL) and the chromatibody chromatibody. The three complementary determining regions (CDRs) are delineated under the alignment (turquoise, green and red, respectively) and are similarly color-coded in the optimized view of the 3D models. (D) Prediction of the complex between chromatibody and the H2A-H2B dimer. Three acidic residues from the H2A second helix (displayed in CPK mode), E1056 (brown), E1061 ( purple) and E1064 (red), form hydrogen-bonds with chromatibody R29 (green), S108 ( pink) and R111 (red), respectively. Hydrogen bonds between chromatibody and H2B (marked by black arrowheads, Fig. 1C ) strengthen the interaction. (E) Modeled structure resulting from docking the chromatibody CDR3 hairpin into the H2A-H2B acidic cavity at the surface of the nucleosome core.
, 1998) , was expressed. We observed that both interphase chromatin and mitotic chromosomes labeling were similar (Fig. 3A ). We then performed time-lapse fluorescence microscopy to evaluate the ability of chromatibody-GFP to allow non-invasive imaging of the chromatin in living cells. This revealed that the chromatibody-GFP probe remained specifically associated with chromatin throughout cell division, as we never observed any cytoplasmic labeling (Fig. 3B ; Movie 1). The chromatibody-GFP expression did not perturb progression through mitosis, and the mitotic timing was comparable to the H2B-GFP fusion (Fig. 3B ). The growth curves established with HT1080 cells stably expressing chromatibody-GFP are similar to those of the parental HT1080 cells (Fig. S3A ). Cells stably expressing the fluorescent chromatibody could be passaged for up to 2 months without substantial cell death or loss of transgenic expression (data not shown).
Finally, stable expression of chromatibody-GFP does not alter the cell cycle kinetics of synchronized cells (Fig. S3B ). Taken together, these data strongly suggest that the expression of the fluorescent chromatibody does not affect either cell cycle progression or cell viability. To explore the potential for using the non-invasive chromatibody during development of a multicellular organism, we generated transgenic Drosophila that expressed GFP-tagged chromatibody. Time-lapse imaging of developing blastoderm embryos expressing chromatibody-GFP was performed. Chromatibody associated with chromatin and allowed its visualization throughout development, from embryo to larval and adult stages (Fig. 3C ). Embryos expressing chromatibody-GFP developed normally, giving rise to fluorescent larva, pupae and adults in which nuclei, even those of internal structures (and especially those of the salivary glands where endoreplication takes place), are clearly labeled (Fig.
Molecular modeling Modeling was performed using the Accelrys Discovery Studio v. 3. 1 (DS 3. 1). The X-ray structure of the llama anti-cholera toxin VHH domain (PDB code: 4IDL) was identified as the best structural template to homologymodel the chromatibody structure. Secondary-structure-guided sequence alignment between the structural template and chromatibody was carried out using the Align 123 program and served as an input for the automated homology modeling program Modeler v. 9. 8. We built 50 models and selected the one with the lowest probability density function (PDF) total energy and the best set of DOPE and Profiles-3D scores, reflecting optimal loop refinement and folding consistency, respectively. The X-ray structure of a Kaposi's sarcoma herpesvirus LANA peptide bound to the nucleosome core (PDB code: 1ZLA) provided the H2A-H2B dimer coordinates and a framework to model the interaction with chromatibody.
The mounting medium was from Dako. For immunofluorescence analyses with the anti-53BP1, anti-BRCA1 and anti-γH2AX antibodies, cells were scored positive when more than 10 foci per nuclei were detected. Immunostaining with the anti-ubiquitylated H2A (1:100, E6C5, Millipore) and anti-FK2 (1:100, PW8810, Enzo Life Science) antibodies were performed as previously described  (Luijsterburg et al. , 2012). Immunostained human cells were imaged through a 63× oil objective (NA 1. 4) using a Leica DM5000 microscope controlled with MetaMorph acquisition software (Molecular Devices) and equipped with a CoolSNAP EZ camera (Photometrics). Drosophila melanogaster embryos were dechorionated and fixed  (Harlow and Lane, 2006). Rehydrated embryos were blocked (PBS with 0. 3% Triton X-100 and 1% BSA) and incubated overnight at 4°C in the primary antibody mixture diluted in the same buffer.
Time-lapse fluorescence microscopy HT1080 or HCT116 cells stably expressing the chromatibody-GFP fusion were grown in calf-serum-complemented DMEM medium in chambered coverglass (Lab-Tek Nunc). Time-lapse fluorescence microscopy was performed using an inverted wild-field microscope (Zeiss Axio Observer Z1) controlled with the MetaMorph software (Molecular Devices), associated with a CoolSNAP ES2 camera (Photometrics), and equipped with an incubation chamber (Pecon) with controlled temperature (37°C), humidity, and CO 2 (5%) parameters. The acquisitions were performed using a 20× NA 0. 5 or a 40× NA 0. 95 objective at 5-min intervals. Drosophila embryos were collected on 3% agar plates supplemented with grape juice, manually dechorionated, and placed in a drop of halocarbon oil deposited in a 35-mm cell culture dish with a glass bottom (Greiner Bio-one) prior to live imaging.
Time-lapse fluorescence imaging of embryos was performed using a 25× objective (NA 0. 8) of a laser-scanning microscope (LSM 510 Zeiss) in inverted configuration, equipped with a controlled temperature (25°C) and moisture incubation chamber (Pecon). The image acquisition frequency was 2 min. Live larvae and adults expressing the fluorescent chromatibody were imaged by epi-fluorescence illumination with a MacroFluo (Leica) equipped with a CoolSNAP ES2 camera (Photometrics). All the imaging data were processed using Fiji (ImageJ NIH) software.
FRAP analysis Fluorescence recovery after photobleaching (FRAP) experiments were performed using a confocal laser-scanning microscope (Zeiss LSM510) in inverted configuration, equipped with a 40× (NA 1. 2) immersion objective and an incubation chamber, controlled by Zen software (Zeiss). Photobleaching and GFP excitation were performed with the 488 nm line of an argon laser. A small (1. 2 µm) circular region of interest (ROI) was arbitrarily positioned in the nucleus. The GFP bleaching acquisition sequence was a pre-bleaching acquisition, bleaching of the ROI (single scan 163. 8 µs pixel dwell at 100% laser power), followed by an acquisition every second. The FRAP module of the Zen software (Zeiss) was used to determine the mean ROI intensity value, the relative mean ROI for each time point, and to fit the FRAP recovery curves to the mono-exponential function I(t)=I0-I1.
Statistical analyses For ELISA assays, the results were analyzed using the GraphPad Prism 6 software for Windows. The statistical differences were analyzed by two-way ANOVA (multiple comparisons) and are indicated by dollar symbols in the figures. When ANOVA showed a statistical difference (P<0. 05), comparison among data was performed using Tukey's multiple comparisons test or Bonferroni's multiple comparisons test (between chromatibody and control VHH). For DNA damage response (DDR) alteration, statistical differences were evaluated using a Student's t-test in Microsoft Excel and are indicated by asterisks in the figures. P<0. 05, P<0. 01, P<0. 001 or P<0. 0001, indicating a statistical significance, are noted with dollars or asterisks ($ or *, $$ or **, $$$ or *** and $$$$ or ****, respectively).
Acknowledgements We thank  J. Rouquette  ( ITAV imaging facility, Toulouse ) for his help in cell imaging,  M. Danel  ( ITAV chemistry facility, Toulouse ) for the reverse phase-HPLC histone purification,  F. Gallardo  ( ITAV, Toulouse ) for yeast immunostaining,  J. Favier  ( CBD, Toulouse ) for the Drosophila transgenesis,  O. Mondesert  ( ITAV, Toulouse ) for lentiviral transductions and  L. Huc  ( Toxalim, Toulouse ) for statistical analysis. The authors also thank  F. Larminat  and  P. Calsou  ( IPBS, Toulouse ) for the anti-BRCA1 antibody and access to the γ-irradiation facility,  L. Van den Berghe  (vectorology platform, INSERM  U1037 Cancer Research Center of Toulouse, Toulouse, France ) for the technical assistance in the BSL3 laboratory, and  G. Favre  ( INSERM U1037 Cancer Research Center of Toulouse, Toulouse, France ) for access to his laboratory for phage-display and ELISA experiments.
Funding This work was supported by an  Agence Nationale de la Recherche  ( ANR) program  [grant number  ANR-10-CESA-011 ]; and  Universitéde Toulouse 3 Paul Sabatier  ( Actions Transverses du Conseil Scientifique , 2011 to G. M. and PhD fellowship to Y. F. ). Deposited in PMC for immediate release.
The release of T. reesei QM6a genome revealed a relatively small number and poor diversity of carbohydrate-active enzymes (CAZymes)  (Martinez et al. 2008). Other ascomycete genomes have been sequenced such as Neurospora crassa  (Galagan et al. 2003) , Neocosmospora haematococca (as Nectria haematococca )  (Coleman et al. 2009 ) and many Aspergilli, among which Aspergillus nidulans  (Galagan et al. 2005) , Aspergillus niger  (Pel et al. 2007) , Aspergillus oryzae  (Machida et al. , 2005)  and others (see CAZy database;  Lombard et al. 2014). Computational analyses revealed large number of genes coding for potential CAZymes, of which numerous have been biochemically characterized and applied in biorefinery processes  (van den Brink et al. 2011 , Vasu et al. 2012 , Yamada et al. 2015 , Johansen et al.
Protein identification The raw mass data were first converted to the mzXML format with the ReAdW software (http://tools. proteomecenter. org/software. php). Protein identification was performed by querying the MS/MS data against the corresponding protein databases along with an in-house contaminant database, using the X!Tandem software (X!Tandem Tornado 2008. 02. 01. 3, http://www. thegpm. org) as described in  (Couturier et al. 2012). All the peptides that matched with an E-value lower than 0. 05 were parsed with an in-house programme (http:/PAPPSO. inra. fr/bioinformatique. html). Proteins identified by at least two unique peptides and a log (E-value) lower than 1. E-8 were considered to be validated.
Functional annotation of CAZymes Annotation of the set of CAZymes for each secretome was performed by comparing the predicted proteins to the CAZy database. CAZymes were listed based on the number of representatives of each of the glycoside hydrolases (GH), carbohydrate esterases (CE), polysaccharide lyases (PL), auxiliary activities (AA) and carbohydrate binding modules (CBM) families. For GH5 family, the annotation was performed at the subfamily level  (Asperborg et al. 2012).
Qualitative analysis We recorded the continuous AE signal during the compaction of a bed of brittle UO 2 granules. In this experiment no detection threshold of the acoustic events was applied, and the upper punch moved at the speed of 1 mm/s. This high speed, used only for this experiment, was motivated by the requirement of recording a huge amount of data during the whole compaction process. The press chamber filled up with the granules was 12 mm in height (filling density equal to 3. 3 g/cm 3 ). As expected, the number of hits globally increases during the compaction process. Nevertheless, by examining carefully the continuous signals, we can define three zones of acoustic activity strongly related to the applied stress (Fig. 11 )  [28]. The first zone, labelled 1 in Fig. 11 , ranges from 0 to 1.
Supervised and unsupervised classification algorithms are widely used for finding a relationship between acoustic signatures and damage mechanisms of composite materials  [29, 30] , such as delamination, fiber/matrix debonding, fiber breakage and matrix cracking. In our study we applied an unsupervised classification algorithm (namely, the kmeans algorithm  [31] ) by means of ten burst parameters including  rise time, amplitude, and energy. The optimal number of clusters was determined by using two statistical criteria (calculated in Noesis Ò software), namely the standard deviation of the AE parameters in one cluster and the standard deviation of the signal between two clusters. The principal component analysis (included in Noesis Ò software) led to four classes but it appeared impossible to link each class to a specific compaction mechanism unambiguously.
Spectral analysis of the signals, as well as unsupervised classification algorithm together with principal component analysis, have been inefficient. A deeper study has to be performed to better constrain the problem. More specifically, processing of acoustic signals must be flexible enough to adapt to the strong variations in the signal characteristics and to allow for a more quantitative data exploitation. Therefore, the waveform is the most reliable descriptor of the fragmentation process. Three zones of acoustic activity, strongly related to the applied stress, have been clearly defined from analysis of the continuous signals recorded during the compaction process of a bed of UO 2 particles. They correspond mainly to particle rearrangement and/or fragmentation. The end of the noteworthy fragmentation process has been clearly defined as the end of the significant process that increases the compactness of the material.