dataesr/scientific-paragraphs-categorization huggingface dataset:
5) and 100 mM KSO 4. For data collection, crystals were cryoprotected by soaking in a mother liquor supplemented with 20% glycerol and was flash-frozen in liquid nitrogen. Diffraction data were collected at the PXII beam line of the Swiss Light Source (SLS; Villigen, Switzerland) and processed using XDS  (Kabsch, 1993). The structure was solved by molecular replacement with the programme PHASER  (McCoy et al, 2007) , using the NMR structure of the HOT1 DBD as a search model (PDB entry 2CUF). Model building and refinement was carried out with Phenix  (Afonine et al, 2005)  and COOT  (Emsley and Cowtan, 2004) , and models were validated with the programme Molprobity  (Davis et al, 2007). The alignment of the HOT1, TRF1 and TRF2 homeobox domain sequences was done in two steps. First, a secondary structure-based alignment was generated using the programme Chimera (www.
Telomeric quantitative FISH For metaphase preparation, cells were incubated for 4 h with 200 nM nocodazole in order to induce mitotic arrest. A hypotonic shock was achieved in 0. 03 M sodium citrate at 371C for 40 min. Cells were fixed in an ethanol/acetic acid solution (3:1) and washed three times in this fixing reagent. Metaphase spreads were obtained by dropping suspensions of fixed cells onto clean glass slides. The QFISH procedure was carried out as described, using an Alexa488 -O-O-(CCCTAA) 3 or Cy3-O-O-(CCCTAA) 3 PNA probe (Panagene) (London ˜o-Vallejo et al, 2001). Telomeric signals were quantified using the iVision software (Chromaphor). Telomere signals were segmented manually and average pixel intensities from every segment were quantified. For each metaphase, the average background intensity was determined and subtracted from individual telomere signals.
Acknowledgements We thank members from the  Buchholz, Mann and London ˜o laboratories  for helpful comments on the manuscript,  Charlott-Amelie Teutsch , an internship student selected by the  Fo ¨rderverein der Biologieolympiade e. V. , for performing DNA pull-down experiments,  Sebastian Rose  for technical assistance and  Karla Neugebauer  and  Karl Lenhard Rudolph  for critical reading of an earlier version of the manuscript. We are indebted to  Sabine Suppmann  and  Claudia Franke  of the  MPI-B core facility  for help with recombinant protein expression, the  MPI-CBG antibody facility  for raising anti-HOT1 antibodies, the  MPI-CBG light microscopy facility  for assistance with imaging and the  MPI-B crystallization facility  for help with crystallization screening. We thank  Anthony Hyman  for providing reagents, and  Ina Poser ,  Martina Augsburg ,  Marit Leuschner  and  Andrea Ssykor  for help with generating LAP cell lines.
Since the 1980s several spaceborne sensors have been used to retrieve the aerosol optical depth (AOD) over the Mediterranean region. In parallel, AOD climatologies coming from different numerical model simulations are now also available, permitting to distinguish the contribution of several aerosol types to the total AOD. In this work, we perform a comparative analysis of this unique multiyear database in terms of total AOD and of its apportionment by the five main aerosol types (soil dust, seasalt, sulfate, black and organic carbon). We use 9 different satellite-derived monthly AOD products: NOAA/AVHRR, SeaWiFS (2 products), TERRA/MISR, TERRA/MODIS, AQUA/MODIS, ENVISAT/MERIS, PARASOL/POLDER and MSG/SEVIRI, as well as 3 more historical datasets: NIMBUS7/CZCS, TOMS (onboard NIMBUS7 and Earth-.
Probe) and METEOSAT/MVIRI. Monthly model datasets include the aerosol climatology from  Tegen et al. (1997) , the climate-chemistry models LMDz-OR-INCA and RegCM-4, the multi-model mean coming from the ACCMIP exercise, and the reanalyses GEMS and MACC. Ground-based Level-2 AERONET AOD observations from 47 stations around the basin are used here to evaluate the model and satellite data. The sensor MODIS (on AQUA and TERRA) has the best average AOD scores over this region, showing a relevant spatiotemporal variability and highlighting high dust loads over Northern Africa and the sea (spring and summer), and sulfate aerosols over continental Europe  (summer). The comparison also shows limitations of certain datasets (especially MERIS and SeaWiFS standard products). Models reproduce the main patterns of the AOD variability over the basin.
, 2009) , highly variable in space and time, driving their interactions with shortwave (SW) and longwave (LW) radiation, with ensuing impacts on climate. In order to identify and quantify these impacts with regional climate models, a robust 4-D aerosol content database over the region is required to be used as a reference for radiative calculations. The atmospheric aerosol content can be represented by the Aerosol Optical Depth (AOD), which is the vertical integral over an atmospheric column of the fraction of incident light scattered and absorbed by aerosols. This optical parameter is often used as input for calculations of aerosol direct and semi-direct radiative forcing in Regional Climate Models (RCMs). Several AOD datasets exist over the Mediterranean region, both satellite-derived (e.
This study has shown that the MODIS AOD product has the most reliable estimate of the total AOD over ocean and land. Carboni et al. (2012)  have provided an intercomparison of Saharan dust aerosols retrieved by various satellite instruments (e. g. , MODIS, MISR and MERIS) during a strong event in March 2006, showing significant differences between them. Over ocean,  Myhre et al. (2005)  have also underlined significant differences in AOD retrievals from 9 datasets between 1997 and 2000. The AeroCom exercise (http://nansen. ipsl. jussieu. fr/AEROCOM/) has enabled a comparison of aerosols from different global models:  Kinne et al. (2006)  have found a relative agreement in global mean AOD, evaluated between 0. 11 and 0. 14 in the different AeroCom models, against 0. 135 for AERONET and 0. 15 for satellite composites.
These interactive modules are useful for studies at the daily scale and for future climate projections (e. g. ,  Szopa et al. , 2013). However, they might lead to biases when simulating present aerosol conditions, as the climatic biases impact the AOD field through emission, advection and wet deposition. Resorting to monthly climatologies can be justified for this reason, especially for hindcast simulations, targeting a realistic representation of the past decade. Given the strong aerosol impact on climate, the choice of the aerosol dataset is essential  (Hohenegger and Vidale, 2005). The objective of this work is to compare and evaluate the different existing multi-year datasets of aerosols in terms of monthly AOD at 550 nm over the Mediterranean basin, in order to set up a reference aerosol climatology.
Finally, those remote-sensing datasets have been associated with model-simulated products, which provide AOD climatologies, as well as a vertical dimension for some of them. In addition, they offer the possibility of separating aerosols between different types. Five classes are generally distinguished in climate modelling: soil dust (SD), sea-salt (SS), black carbon (BC), organic carbon (OR) and sulfate (SU) aerosols. The following numerical model-based products will be assessed: the climatology from  Tegen et al. (1997)  (hereafter noted TEG97), the climate-chemistry models  RegCM-4 (2000 RegCM-4 ( -2009) )  and LMDz-OR-INCA (1850-2100), the reanalyses GEMS  (2003) (2004) (2005) (2006) (2007) (2008)  and  MACC (2003 MACC ( -2010)) , and the multi-model mean coming from a subset of models taking part in the ACCMIP exercise.
As a result of this comparison, a possible reconstruction of the aerosol field over the Mediterranean basin will be provided for use in RCMs, as well as an estimation of the different uncertainties. It should be noted that the present work focuses on tropospheric aerosols, and that consequently the consideration of volcanic aerosols will only be raised in the discussion part. After a description of each aerosol dataset in Sect. 2, the comparison and evaluation of the different satellite-derived and model-simulated products is given in Sect. 3, considering both the spatial and temporal variability, and also the vertical distribution. The period before the 2000s when few satellite retrievals and models are available is studied separately. Section 4 presents our blended 4-D reconstruction of the aerosol field, as well as a discussion about optical properties notably before the concluding remarks in Sect.
Satellite data Aerosol information retrieved from different sets of satellite measurements is compared over the Mediterranean basin in the present work, in terms of aerosol optical depth at 550 nm. Table  1  summarises their main characteristics (instrument, platform, period of study, spatial resolution and coverage). The following list includes the main aerosol sensors over the basin, and all of them will be evaluated against AERONET ground-based observations (when available) and compared with each other in this study. We have separated the ones retrieving data only before the 2000s, which cannot be evaluated against AERONET observations. Among these 11 datasets, only the ones covering the whole Mediterranean basin and surrounding land areas could be considered for a reconstruction purpose.
, 2004)  and the SeaWiFS Ocean Aerosol Retrieval (SOAR) algorithm over ocean  (Sayer et al. , 2012). This new product has been included in the present intercomparison (level-3 monthly AOD), and will be called SeaWiFS-2 in this paper. The Medium Resolution Imaging Spectrometer (MERIS) is a multi-spectral sensor launched onboard the ENVISAT satellite in 2002. MERIS retrieves aerosol properties in the 390 to 1040 nm spectral range over land and ocean  (Antoine and Morel, 1999; Santer et al. , 1999) , but does not include bright surfaces like Sahara. In this study, we use the Level-2 monthly aerosol product (MER T550M) obtained with the Giovanni online data system. The Advanced Very High Resolution Radiometer (AVHRR) included in the Pathfinder ATMOSphere (PAT-MOS) project provides a multiyear  (1982-2007) multisatellite (NOAA-7, -9, -11 and -14)  dataset  (Stowe et al.
, 1997). The aim of PATMOS is to generate multiple products (cloud, aerosol, surface and radiometric) on the same grid using a common processing path. We use for this study the level-3 aerosol product (patmosx aft mor), downloaded from the PATMOS website (http://cimss. ssec. wisc. edu/patmosx/). AVHRR only covers ocean surfaces, but provides AOD data over the longest period (26 yr). All eight satellite-derived aerosol datasets listed above are extending up to 2010 (except for AVHRR to 2007). We also consider 3 more historical datasets, which cover the early 1980s, as listed below. The Total Ozone Mapping Spectrometer (TOMS) onboard the Nimbus-7 satellite retrieves aerosol properties over land and water surfaces  (Torres et al. , 2002). These measurements rely on the near-UV region, and the aerosol index from TOMS is often used  (Herman et al.
Methodology The different satellite and model monthly AOD products are evaluated for each station when both AERONET and satellite-derived (and model-simulated) AOD are available. Every dataset is averaged over a 1 × 1 • box around the AERONET site. This method enables to include in the comparison products which cover only ocean surfaces thanks to coastal AERONET stations. Results based on temporal and spatial correlations will be both presented in Taylor diagrams  (Taylor, 2001) , which indicate how closely a pattern matches observation representing three statistics, namely the correlation coefficient (indicated by the azimuthal angle), the standard deviation normalised by the observed standard deviation (the radial distance from the origin) and the root-meansquare (RMS) difference (the distance to the point on the xaxis identified as "observed").
In our case, given the high number of stations and datasets for evaluating temporal correlation, the ensemble of points representing all stations is included in a polygon for the corresponding dataset. The cross (resp. the diamond) indicates the average for the corresponding satellite (resp. model). In the same way, for evaluating spatial correlation, polygons include the ensemble of points representing the different months for the correspond-have only used AOD monthly average quality-assured data (Level 2. 0) downloaded from the AERONET website (http://aeronet. gsfc. nasa. gov). Angstrom exponent was also downloaded in order to calculated AOD at 550 nm from measurements at 440 or 500 nm (see equation 1). As the main interest of the paper is the Mediterranean region, all the stations located in the area from 28 to 47 • N and from 8 • W to 37 • E have been selected, provided the measurements cover a period of at least one year.
Methodology The different satellite and model monthly AOD products are evaluated for each station when both AERONET and satellite-derived (and model-simulated) AOD are available. Every dataset is averaged over a 1 • x 1 • box around the AERONET site. This method enables to include in the comparison products which cover only ocean surfaces thanks to coastal AERONET stations. Results based on temporal and spatial correlations will be both presented in Taylor diagrams  (Taylor, 2001) , which indicate how closely a pattern matches range (outliers) will be indicated with small circles. 4 ). 4 ). ing dataset. Box and whisker plots are also used to characterise the biases of AOD products against AERONET measurements. The median will be indicated as a thick black line while the central box will be limited by the first and third quartile.
Satellites The common period for all the recent (in the 2000s) datasets covers only two years  (2006) (2007) , which turns out to be very short. For this reason we have decided to use the whole period of time for every dataset, especially as very few differences are noticed using 2006-2007 instead of the whole time period (figure not shown). Table  3  presents the corresponding period of time for each dataset, as well as averages on the European continent (region 8 + 9 + 10 in Fig. 1 ), the Mediterranean Sea (5 + 6) and Northern Africa (1 + 2) over the mentioned period. Figures  2  and 3  present the averaged AOD map at 550 nm over the Mediterranean basin, respectively, for the different satellite and model datasets. Averages over the common period 2006-2007 are also indicated between brackets in Table  3.
The two means over the period of study for each dataset and the common period are found to be very close in most cases. Large differences can be noticed between these sensors in Fig. 2 , but a relative agreement can be found between MODIS (on AQUA and TERRA) and MISR. These three products which cover the whole domain show the same geographical structure and their averages in Table  3  are very close. The maximal AOD, related to dust aerosols, is Table  3. Averaged AOD at 550 nm over the European continent (region 8 + 9 + 10), the Mediterranean Sea (region 5 + 6) and Northern Africa (region 1 + 2) for each satellite-derived and model-simulated product. Averages are calculated over the period of study (second column) for each dataset. Averages over the  2006-2007 common period   observed over Northern Africa (averaged AOD between 0.
50, 0. 84 and 0. 81 × 10 -2 / • ). However, the AOD in LMDz-OR-INCA is shown to be higher than in other datasets for the three studied regions of Table  3  (Europe, Mediterranean Sea and Northern Africa), especially over Northern Africa (0. 403). RegCM-4 AOD over the Mediterranean Sea (0. 178) and Northern Africa (0. 257) is closer to MODIS and MISR AOD averages, but slightly lower. In Europe, RegCM-4 AOD (0. 127) is lower than in satellite datasets, particularly in western Europe (Benelux, Germany, Po Valley), suggesting a possible underestimation of AOD due to a lack of anthropogenic aerosols (sulfates, see Sect. 3. 3). This would also explain the slight difference over the Mediterranean Sea and Africa where sulfate aerosols are less important. Over the Atlantic Ocean, low AOD simulated in RegCM-4 compared to satellite datasets may be due to the proximity of this region to the limit of the domain, thus, missing in RegCM-4 simulation aerosols produced over the Atlantic Ocean outside the domain.
GEMS and MACC have also a similar spatial distribution of AOD. The AOD assimilation enables them not to be far from MODIS products. However, GEMS AOD is larger over Europe (0. 245 on average), linked to an overes-timation of sulfate aerosols over Central Europe, related to a bug in the conversion from SO 2 to SO 4 corrected in the MACC version  (Morcrette et al. , 2011). Over the Sea (0. 243) and Northern Africa (0. 335), its AOD is also higher than in most other datasets. MACC is the second version of the ECMWF reanalysis and has reduced the difference over Europe (0. 194), but still have a high AOD over the Sea (0. 238). Over Northern Africa, MACC (0. 257) has a lower AOD than GEMS (0. 335), and also smaller than other datasets in this region of dust production. Over the bright surfaces of Sahara where MODIS AOD is not assimilated, GEMS and MACC have to rely on the model dust emission parameterisation, which was revised downward for MACC when GEMS was assessed to produce too high an AOD for dust aerosols  (Mangold et al.
Comparison of seasonal cycle The aerosol atmospheric content is submitted to a strong seasonal cycle over the Mediterranean region  (Moulin et al. , 1998; Barnaba and Gobbi, 2004)  and this seasonal variability    is now evaluated for the different datasets. Figure  4  shows the AOD monthly averages at 550 nm over the same studied regions for satellite-derived (top) and model-simulated (bottom) products. All of them present a seasonal cycle with a maximum in spring or summer and a minimum in winter, but with various amplitudes. Mediterranean AOD is indeed controlled by the dust emissions over Northern Africa  (Moulin et al. , 1998) , with a maximum in spring and summer, and anthropogenic aerosols in summer over Central Europe  (Barnaba and Gobbi, 2004). Deposition also favours this summer maximum, as winds and rainfall increasing aerosol Amongst satellite datasets, the median bias is close to 0 for AQUA/MODIS (-0.
011 respectively, but the spread is lower for RegCM-4. LMDz-OR-INCA, GEMS, MACC and ACCMIP biases are slightly higher. We can also mention that RegCM-4, LMDz-OR-INCA and AC-CMIP show several outliers, which come from several points near the Alps as well as from Cairo. Spatial and temporal correlations with AERONET observations are now evaluated, and the results are presented in Taylor diagrams, as detailed in Section 3. 1. Figure  6  evaluates temporal correlation at every AERONET station (av-eraging all months available at the given station) for the different datasets. MODIS (on AQUA and TERRA) have 790 the smallest average root-mean square error (RMSE) among satellite datasets, as well as the highest correlation (respectively 0. 76 and 0. 77). Moreover, their spread is similar and not so large compared to the other datasets.
Over the sea, PARASOL also shows a good correlation (0. 74) but its stan-795 dard deviation is much higher (59%) than that of AERONET. AVHRR has the best estimated standard deviation and a very small spread, but a lower correlation (0. 66) than MODIS. The other datasets show lower scores in the Taylor diagram, even for MISR. Large polygons for MERIS, MISR, SEVIRI 800 and PARASOL indicate strong errors at some stations contrary to MODIS whose data are more homogeneous. Sea-WiFS is the only one to show much too little variability, but a significative improvement is noticed with SeaWiFS-2. With regards to model datasets, MACC and GEMS reanal-805 yses have the best RMSE and correlations, even if it should be noted that the evaluation against MODIS data is not completely independent because of data assimilation in these reanalyses.
TEG97 only captures the summer maximum, but not the spring one, probably because of the lack of dust aerosols. LMDz-OR-INCA cycle is disturbed by its strong dust emissions over Africa, which lead to two peaks in May and September which might be overestimated compared to other datasets. RegCM-4 is similar to satellite products before summer, when sulfate aerosols begin to have a significant role. Over Europe, differences between MODIS (on AQUA and TERRA), and MISR do not exceed 0. 02 for every month revealing a very good agreement between these sensors, SeaWiFS-2 shows similar AOD variations and values except a minimum in June, while MERIS AOD is higher on average. TEG97, ACCMIP and LMDz-OR-INCA are quite similar to these three satellite sensors. GEMS presents the same variations, but with a positive difference compared to MODIS and MISR, which has been reduced in the MACC version.
RegCM-4 clearly misses aerosols in summer compared to all the other datasets. Over Northern Africa, the seasonal cycle is still similar between both MODIS platforms, MISR and SeaWiFS-2. AOD peaks at 0. 44 in summer in AQUA/MODIS and TERRA/MODIS, 0. 40 in MISR and 0. 36 in SeaWiFS-2. The models show different variations and values with a maximum situated between April and July, except LMDz-OR-INCA with these two peaks mentioned before which reach 0. 66 over Africa.
Comparison to AERONET After this comparison, we resort to independent groundbased measurements in order to evaluate these different  as the other models, which might be due to their coarser spatial resolution. To summarize, this evaluation with ground-based measurements indicates that MODIS has the best AOD estimation for several criteria, in agreement with the evaluation at 835 the global scale by  Bréon et al. (2011). It is more difficult to choose between AQUA and TERRA as they show very similar results. AOD products. The AERONET network provides standardised high quality aerosol measurements and is used here as reference data. Comparison with satellite and model data is achieved for total AOD at 550 nm over the 47 stations available throughout the Mediterranean basin (Sect. 2. 3, Fig. 1 ).
Spatial and temporal correlations with AERONET observations are now evaluated, and the results are presented in Taylor diagrams, as detailed in Sect. 3. 1. Figure  6   tions for the given month as the other models, which might be due to their coarser spatial resolution. To summarize, this evaluation with ground-based measurements indicates that MODIS has the best AOD estimation for several criteria, in agreement with the evaluation at 835 the global scale by  Bréon et al. (2011). It is more difficult to choose between AQUA and TERRA as they show very similar results. Figure  5  has shown a more reduced spread for the AQUA/MODIS dataset than for the TERRA platform. Besides, Figure  13 (c  all months available at the given station) for the different datasets. MODIS (on AQUA and TERRA) have the smallest average root-mean-square error (RMSE) among satellite datasets, as well as the highest correlation (respectively 0.
76 and 0. 77). Moreover, their spread is similar and not so large compared to the other datasets. Over the sea, PARASOL also shows a good correlation (0. 74), but its standard deviation is much higher (59 %) than that of AERONET. AVHRR has the best estimated standard deviation and a very small spread, but a lower correlation (0. 66) than MODIS. The other datasets show lower scores in the Taylor diagram, even for MISR. Large polygons for MERIS, MISR, SEVIRI and PARASOL indicate strong errors at some stations contrary to MODIS whose data are more homogeneous. SeaWiFS is the only one to show much too little variability, but a significative improvement is noticed with SeaWiFS-2. With regards to model datasets, MACC and GEMS reanalyses have the best RMSE and correlations, even if it should be noted that the evaluation against MODIS data is not completely independent because of data assimilation in these reanalyses.