dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, under these conditions, CnB1, NFAT1 or NFAT2 silencing inhibited motility of 4T1 cells as assessed in Boyden chamber assays and by time lapse video microscopy (Figure  2d ; Supplementary Figure  2  and 3 ) and impaired their ability to heal a wound (Supplementary Figure  3 ). Of note, the data of Supplementary Figure  3  used a distinct set of shRNAs for CnB1, NFAT1 or NFAT2, thus excluding nonspecific, off-target effects of the shRNAs used. Invasion was also analyzed using Boyden chambers with membranes precoated with a layer of Matrigel. CnB1, NFAT1 or NFAT2 knockdown also resulted in impaired invasion (Figure  2e ). These results show that Cn and its major effectors NFAT1 and NFAT2 are important for breast cancer cells migration and invasion. The Cn/NFAT pathway is essential for mammary tumorigenesis.
To next determine whether the Cn/NFAT pathway is essential to tumorigenesis, the control and CnB, NFAT1 or NFAT2-silenced 4T1 cells were injected into the mammary gland of syngenic Balb/c mice and tumor growth was monitored every 3 days over a period of 3-6 weeks, time after which mice were killed. Immunohistochemical analysis of NFAT1 in 4T1 tumors revealed the activation of the Cn/ NFAT pathway, as NFAT1 was detected in the nuclei (black stars) of control tumor cells, whereas it was exclusively found in the cytoplasm of shCnB1-silenced tumors and not detected in shNFAT1-silenced tumors (Figure  3a ). As shown in Figures  2b  and c , CnB1, NFAT1 or NFAT2 silencing resulted in a severe decrease in tumor size. Similar experiments realized with 168FARN cells, a less metastatic murine mammary tumor cell line, also show that NFAT1 or NFAT2 silencing resulted in decreased tumor size (data not shown).
To understand the basis of the decreased size of CnB1-, NFAT1-and NFAT2-silenced tumors, tumors were analyzed 5 or 15 days after orthotopic engraftment for proliferation and apoptosis by Ki67 and cleaved caspase 3 immunohistochemical staining, respectively. No significant difference was observed in the proliferation/apoptosis rates, soon after cells injection (day 5, data not shown). Similarly, 2 weeks after engraftment, no difference was found in the proliferation rate of the different tumors (Figure  3d , upper panel). In contrast, a twofold increase in apoptotic cells was seen in CnB1-, NFAT1-and NFAT2-silenced tumors as compared with tumors transduced with the control vector (Figures  3d  and e ). These observations indicate that Cn, NFAT1 and NFAT2 are essential components of a survival pathway activated in mammary tumors in vivo.
Immunohistochemistry was performed using the Leica Bond-III automated immunostainer (Leica Microsystems, Nanterre, France) and the anti-NFAT2 antibody (Santa Cruz, Heidelberg, Germany; sc-7294) as previously described. 9 nalyses were performed in accordance with the French bioethics law 2004-800 and the French National Institute of Cancer (INCa) Ethic Charter and after approval by the Institut Curie board and ethics committee, which waived the need for written informed consent from the participants. Women were informed of the research use of their tissues and did not declare any opposition for such search. Data were analyzed anonymously. Immunohistochemistry. Seven micrometer sections of paraffin-embedded organs were used for immuno-histological analysis using rabbit anti-NFAT1 (HPA008789, Sigma-Aldrich), rabbit anti-cleaved caspase 3 (mAb #9664; Cell Signaling Technologies, Leiden, the Netherlands), mouse anti Ki67 (KI67-MM1-CE-S Leica) and amplified using the Vectastain Elite ABC kit (Vector) and revealed with DAB Peroxydase Substrate Kit (Vector, Les Ulis, France).
Analysis/counting was performed independently by two investigators. Affymetrix microarrays. Microarray analyses were performed using total RNA from three independent LKO-control and three independent shNFAT-1-4T1 cultures using the Murine Genome 430. 2 array (Affymetrix, Santa Clara, CA, USA) according to manufacturer's instructions (http://www. microarrays. ustrasbg. fr). Raw feature data were normalized, and log 2 intensity expression summary values for each probe set were calculated using robust multiarray average (package affy V1. 4. 32). Unsupervised hierarchical clustering analysis was performed using Java Tree view and Cluster programs. Immunoblotting. Antibodies used in this study are: mouse monoclonal antibodies, anti-NFAT1 4G6-G5 (Santa Cruz, sc-7294), anti-NFAT2 7A6 (Santa Cruz, sc-7294), anti-CnB1 (Sigma-Aldrich, CO581), anti-STAT5 C17 (Santa Cruz).
Chromatin immunoprecipitation. Control and shNFAT1-4T1 cells were stimulated with PMA (25 ng/ml) and ionomycin (1 mg/ml) for 4 h, then fixed in 1% formaldehyde for 20 min at 37 °C and processed for chromatin immunoprecipitation as previously described (Lesault, 2002 #63) using 10 μg of anti-NFAT1 antibody (Sigma-Aldrich). The presence of ADAMTS1 promoter fragments in immunoprecipitated chromatin was detected by PCR using 5′-CGCTTTAGCCATGGT GCCCATGG-3′ and 5′-CGAAACAGCGCTGGGACCAGC-3′ as primers. PCR products were analyzed on agarose gel and quantified using ImageJ software. Unprecipitated DNA was used as input. assistance with mouse husbandry, Morgane Thion and Sandrine Humbert for discussions. DP was supported by predoctoral fellowships from the Région Ile-de-France and Fondation ARC pour la recherche sur le cancer.
Acknowledgements. We thank  A Mensah  for the analysis of the IHC,  H Alcalde  for IV injection of the 4T1 cells,  M Hirondelle  for help with the gelatin assays,  F Cordelières  for assistance in cell migration analyses,  Vonick Sibut  for assistance in transcriptomic analyses,  Elodie Belloir  and  Christophe Alberti  for.
Data set description The collection consists of pressure observations made at 49 locations in Europe and North America, plus four ships' logbooks from voyages in the southern Atlantic, the Indian Ocean, the China Seas and the Persian Gulf (Fig. 1 ). More than half of the series were recently digitised at the University of Bern and considerable resources were also invested in the recovery of metadata. The digitisation usually involved the years from 1815 to 1817 only. In addition to barometer readings and the temperature of the barometer (when available), outside air temperature was also digitised, with the exception of a few stations in North America. Other series, some covering much longer periods (up to 257 years in the case of Stockholm), were provided by co-authors. Many of them have already been described in the scientific literature; their references are listed in Table  S1  in the Supplement together with the sources of the new records.
Reduction to 0 • C About half of the observatories in our data set recorded the temperature of the barometer. It was in fact common to have a mercury thermometer fixed on the same support as the barometer. Since the mercury expands and shrinks depending on the temperature, observations made with a mercury barometer must be corrected accordingly: L 0 = (1 -γ T )L mm , (4) where γ is the thermal expansion coefficient of mercury at 0 • C (1. 82 × 10 -4 K -1 ), T is the temperature of the barometer in • C, L mm is the original observation in millimetres of mercury and L 0 is the observation reduced to 0 • C. Today, the "neutral" temperature of 0 • C is dictated by international standards; this was partially the case already in the early 19th century. Note that some observers used to reduce their observations to other temperatures (10 • R being the most common).
Therefore, the original corrections do not correspond exactly to those resulting from Eq. (  4 ). The series from Milan, Salem, Stockholm and Turin had already been reduced to 0 • C in previous works by data contributors (see the respective references for more details). In Exeter, the observer started to register the temperature at the barometer only in 1817; outside air temperature was used before that year (absolute differences between temperature at the barometer and outside temperature were on average smaller than 2. 5 K during 1817). The conversion of pressure readings from millimetres to hectopascal follows from the hydrostatic equation: P n = ρg ϕ,h L 0 × 10 -5 , (5) where P n is the absolute pressure in hectopascal reduced to normal gravity, ρ = 1. 35951 × 10 4 kg m -3 is the density of mercury at 0 • C, g ϕ,h is the local gravity (see below) and L 0 is the barometric reading in millimetres (corrected for temperature).
Reduction to mean sea level To use the pressure observations for synoptic analysis, we reduced P to sea level: P 0 = P • exp g ϕ,h R • h T S + a • h 2 , (8) where R = 287. 05 J kg -1 K -1 is the gas constant for dry air, a = 6. 5 × 10 -3 K m -1 is the standard lapse rate of the fictitious air column below the station and T S is the outside temperature at the station in K. We did not apply further corrections described in WMO (2008), since the uncertainty in our data set is much higher than that required for modern barometers (i. e. ±0. 1 hPa). Similarly to the reduction in pressure readings to 0 • C (Sect. 2. 3. 3), we used in situ air temperature observations where available and resorted to climatological temperatures from the Twentieth Century Reanalysis (1871-1900) otherwise. We did not use the temperature of the barometer to reduce pressure readings to sea level.
Quality control We inspected visually each sea level pressure (SLP) series (and differences with nearby stations) to flag erroneous outliers and clear inhomogeneities in the period 1815-1817. Nearly all outliers derive from mistakes in the digitisation or in the transcriptions by the observer. When possible (i. e. when the original sources were readily available) we corrected them; otherwise, we flagged them as erroneous and excluded them from the analysis. The total number of pressure observations flagged after the quality control is 4657, corresponding to 4. 1 % of the 1815-1817 data set. Most of the flagged observations correspond to long periods in a few series where we detected large inhomogeneities: Madrid (whole year 1815 flagged), New Haven (most of autumn and winter of 1815/16), Valencia (all summer observations), Växjö (whole 1817) and Ylitornio (11 months in 1817).
Storminess One of the advantages of daily pressure observations with respect to monthly data is the possibility to study variability on the timescales of the typical large-scale weather phenomena. In particular, the variance in bandpass-filtered daily pressure observations (hereafter "storminess") is related to the frequency of stormy weather caused by extratropical cyclones and is commonly used for storm track analysis (e. g. Blackmon et al. , 1977; Chang et al. , 2002). In this section, we apply a 2-6-day bandpass Lanczos filter  (Duchon, 1979)  with a 31-day convolution vector to analyse winter and summer storminess in 1815-1817 in Europe and north-eastern North America. We use only interpolated SLP observations at 12:00 UTC because this is the only time step available for every series.
Using the original SLP observations, we calculated seasonal means for each series in the period 1815-1817 and then applied a constant offset necessary to match the 1815-1817 seasonal means of the nearest grid point in the reconstruction. This was possible only if enough data were available: we calculated the offset using only the years with at least 90 % of the days in the target season having at least one observation available. When a series does not have enough data in any year for a certain season, we used the average of the offsets from the available seasons. The seasonal offsets were then applied directly to the interpolated SLP values described in Sect. 2. 3. 7. If the data are insufficient in every season, the series is not used in this section. This was the case for Derby, which has only 1 month of data.
Cold spells in winter 1815/16 As already mentioned, the winter of 1815/16 was not a typical post-volcanic winter in terms of temperatures, being colder than usual in most of Europe. From our temperature data we detected two severe cold spells that hit in quick succession between the end of January and the first half of February, which significantly contributed to the cold anomaly. We use these two cold spells as a case study to evaluate the quality of the corrected SLP data set. Figure  9  shows four SLP synoptic maps corresponding to the initial phase of the two cold spells. We plotted the 06:00 UTC time step because more temperature observations (also shown in the maps) are available near that time. A common SLP pattern is evident for the two cold spells, although the one in February, the most severe, is charac- terised by much lower pressure values.
In fact,  Moberg et al. (2002)  and  Bergström and Moberg (2002)  found a particularly high frequency of very cold winter days in Stockholm and Uppsala in the late 18th and early 19th century, although they warned that the data might still be affected by inhomogeneities. Daily temperatures lower than those of February 1816 were registered, for example, in January 1814 and again in December 1817. Similar results (not shown) are obtained by analysing the temperature series from St. Petersburg, in north-western Russia  (Jones and Lister, 2002).
[. ] For ascertaining the temperature of the air and of the water of the ocean, I used delicate pocket-thermometers, the bulbs of which projected about an inch from the ivory scale. In the experiments on the temperature of the ocean, the water was tried the instant it was drawn, before it was affected by the air. " Figure  14  shows a comparison of daily means measured by Davy in the tropical Atlantic (March 1816) and Indian (July-August 1816) oceans with the respective 1961-1990 monthly SST climatologies from the ERSSTv3b (Extended Reconstructed Sea Surface Temperature, version 3b) data set  (Smith et al. , 2008). For the Indian Ocean, we used July climatologies (differences between July and August climatologies are negligible near the Equator). The magnitude of the anomalies (up to -3 K) suggests a cold bias in Davy's observations with respect to modern data, which is to be expected from uninsulated bucket measurements (e.
The recovered data allow similar analyses for specific events in the period 1801-1820, while the quantity of digitised observations becomes increasingly smaller before and after that period. We also analysed a record of SST observations made in the tropical Atlantic and Indian oceans in 1816. Even though more data would be necessary to increase the robustness of the results, we showed indications of large cold anomalies near the Equator, which would be consistent with reconstructions from coral archives  (Tierney et al. , 2015). Direct applications of this data set are limited by its low spatial coverage. For instance, in Europe the lack of data in key regions such as the North Atlantic and the Mediterranean prevents a complete analysis of synoptic patterns, although we showed that documentary sources can give valuable assistance.
On the other hand, the data can be a useful resource for numerical weather model simulations in order to produce a complete four-dimensional reanalysis. With this in mind, the data set will be made available in its raw format (uncorrected original observations) in the International Surface Pressure Databank  (Yin et al. , 2008)  and will be assimilated into a reanalysis using the scheme of the Twentieth Century Reanalysis  (Compo et al. , 2011). This paper also gives a picture of the quantity of meteorological observations available for the early instrumental period (the majority of which have not been digitised yet) and of their potential for climate research. In particular, future initiatives aimed at the recovery of historical records in the North Atlantic and the Mediterranean would make an important contribution to our understanding of the climatic changes that occurred in Europe during the 18th and the 19th centuries.
Acknowledgements. This work was supported by the  Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative  (www. met-acre. org),the  Swiss National Science Foundation (SNF)   Sinergia  project  FUPSOL-II  (Grant  CRSII2-147659 ) and the  EU Horizon 2020   EUSTACE  project (Grant Agreement no. 640171 ). Renate Auchmann was supported by the  SNF  project  TWIST  ( 200021_146599/1 ),  Rob Allan  by the  EU FP7 ERA-CLIM2  ( European Reanalysis of Global Climate Observations 2 ) project and the  Met Office Hadley Centre Climate Program (HCCP) ,  Rudolf Brázdil  by the  Grant Agency of the Czech Republic  for the project no. P209/11/0956 ,  Ladislava Řezníčková  by the  Ministry of Education, Youth and Sports of the Czech Republic  within the  National Sustainability Program I (NPU I , grant no.
3-7) and schematized in Appendix A: (1) satellite data collection and post-processing, (2) data set content study, (3) information content study, (4) correlative data selection, (5) colocated data sets study, (6) vertical resampling of reference profiles, and (7) comparative analysis. Section 2 first introduces the two independent nadir ozone profile retrieval schemes under study in this work: OPERA version 1. 26 and RAL version 2. 1, developed at the Royal Netherlands Meteorological Institute (KNMI) and at the Rutherford Appleton Laboratory (RAL, United Kingdom), respectively. For this study, both processors retrieved Level-2 (L2) nadir ozone profile data from the same MetOp-A GOME-2 (i. e. the second generation Global Ozone Monitoring Experiment on the first Meteorological Operational Satellite) Level-1 (L1) version 4.
0 products of 2008, as delivered operationally by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The respective performance of these two data sets and their compliance with user requirements have been investigated through a round-robin exercise in the context of European Space Agency's Climate Change Initiative (ESA CCI), aiming at the monitoring of essential climate variables (ECVs) from space  (ESA, 2013). A major goal of the Ozone CCI subproject is to produce time series of tropospheric and stratospheric ozone distributions from current and historical Eu-ropean, American, and third-party missions that would meet the requirements for reducing the current uncertainty in estimates of global radiative forcing. It should be emphasized that the focus of this work is on the presentation and application of the RR evaluation methodology, and not on a ranking of the OPERA and RAL retrieval systems.
KNMI retrieval system The optimal estimation retrieval system developed by Royal Netherlands Meteorological Institute is called Ozone Profile Retrieval Algorithm (OPERA, version 1. 26 for this work)  (Mijling et al. , 2010; van Peet et al. , 2014). OPERA derives the vertical ozone distribution from nadir satellite data between 265 and 330 nm. The multiple-scattered part of a sun-normalized radiance spectrum within this wavelength range is simulated by applying the Linearized Discrete Ordinate Radiative Transfer model (LIDORTA) to an atmospheric state model in six streams  (van Oss and Spurr, 2002) , while the single-scattered part of the spectrum is simulated by a dedicated single-scatter model. The sphericity of the atmosphere is taken into account using a pseudo-spherical approximation for both the solar direct beam and the line of sight irradiance.
Harmonization of satellite data representations As both the OPERA and RAL retrieval schemes provide nadir ozone profiles on identical vertical pressure grids, no regridding is required to obtain a harmonized satellite data representation. Unit conversions and mutual flagging (see Sect. 4. 2) nevertheless remain compulsory. When producing harmonized data structures from the HDF5 L2 data sets of KNMI, the fractionalized forms of the CVM and AKM are computed from their sub-column counterparts. The prior and retrieved nadir ozone profiles are, as for the retrieved profile, provided in partial column units already. Slant column densities (SCDs), which are required for later analysis, could therefore also be directly determined for all profiles of both algorithms. They have been approximated by dividing the total retrieved ozone column by the solar zenith angle's cosine.
Data set content study The second part of the round-robin flowchart in Appendix A focusses on the data content study for the satellite retrieval algorithms involved. Inspection of the atmospheric state profile's horizontal resolution and distribution in space and time yields important information on where and when the comparative analysis is valid, and whether or not these geographical and temporal ranges are identical (or at least similar) for the data sets under study (see  Sect. 4. 3). Mutual profile screening should therefore be applied whenever the spatial and temporal sampling agreement between the retrieval outcomes can be improved, or whenever specific retrieval settings can be (further) aligned. This is illustrated for the OPERA and RAL data sets in Sect. 4. 2. A more straightforward Level-2 data file inspection is described first.
Data set inspection The content of the files provided by the retrieval teams has to be examined first. Although OPERA provides significantly more metadata for each nadir ozone profile than the RAL system, about the same (amount of) information has been used for the evaluation processing of all retrievals. Whereas OPERA data for example contain a vertical atmospheric temperature profile, this quantity is not explicitly included in the RAL output. Knowledge of the VMR and number density nadir ozone profiles for RAL nonetheless easily allows calculating the temperature at each retrieval level by use of Eqs. (B1) or (B2). Also thermal tropopause level and tropospheric and stratospheric ozone columns for each profile are provided within the OPERA files. However, applying OPERA's rather harsh tropopause definition, forced to coincide with one of the 17 predefined pressure levels, to RAL's retrievals of the same satellite measurements yields significant differences for the resultant vertically integrated tropospheric columns of up to 50 %, especially around the South Pole.
The centroid offset quantifies the displacement of the centroid from the nominal altitude and as such provides an estimate of the uncertainty on the retrieval height registration (also see dashed lines in Fig. 3 ). Note that the vertical resolution estimated by the data density reciprocal does not at all account for non-vanishing centroid offsets, nor for the actual averaging kernel shape, while the FWHM definitions automatically yield vertical resolutions from kernel values that are by rough approximation located symmetrically around the retrieval centroid. Altitude cross-sections of time-averaged OPERA and RAL centroid offsets are represented on the right in Fig. 6. Except for a 5 km difference below about 15 km in altitude (roughly at the tropopause), median relative AK centroid offsets are very similar for OPERA and RAL.
6. Despite the rudimentary definition, the data density reciprocal sometimes agrees rather well with the four other vertical resolution estimators that take account of kernel shape, mainly for OPERA. RAL d(i) -1 values often deviate from other vertical resolution estimators and are significantly smaller than the OPERA values, especially at low altitudes. This effect is due to RAL's larger averaging kernel elements near the earth's surface, as a result from its somewhat smaller retrieval barycentre offsets. OPERA's median direct FWHM resolution estimate is considerably different. A forthright assessment of the averaging kernel shape around the centroid clearly results in the lowest vertical resolution values. The FWHM estimate obtained from a Gaussian fit to the AK deviates from the direct FWHM.