dataesr/scientific-paragraphs-categorization huggingface dataset:
Observational datasets The assimilated observations consist in Sea Surface Temperature (SST) maps, along track Sea Level Anomaly (SLA) data and in situ temperature and salinity profiles. For each cycle, we assimilate the associated centered SST map coming from the daily NOAA Reynolds 0. 25 • AVHRR-AMSR product  (Reynolds et al. , 2007). In MEDRYS, we assimilate SST only each 1 • to avoid correlation problem between observations. Moreover, we noted a negative average bias of 0. 2 • C between AVHRR-AMSR product and the ERA-Interim reanalysis SST that has been used for fluxes computation. For the sake of consistency between fluxes and assimilated SST in MEDRYS, we decided to add 0. 2 • C to the AVHRR-AMSR maps as a constant offset. The along-track SLA is provided by AVISO (SSALTO/DUACS handbook, 2009) and comes from different satellite datasets.
Names and acronyms used in this paper as well as the measurement period of each satellite are summarized in Table  3. Small scales signals are removed by filtering the data and a sub-sampling is applied. The filtering and sub-sampling is adapted to each region and product as a function of the characteristics of the area and of the assimilation needs. In the Mediterranean Sea, only one point over two is retained to avoid redundant information  (Dufau et al. , 2013). The assimilation of SLA observations requires the knowledge of a Mean Dynamic Topography (MDT). The mean surface reference used is a hybrid product between the CNES-CLS09 MDT  (Rio et al. , 2011)  adjusted with GOCE and reanalysis data  (Lellouche et al. , 2013). In MEDRYS, the effects of the Glacial Isostatic Adjustment (GIA) is taken into account with a correction is about -0.
Conclusions Figures Back Close Full In situ temperature and salinity profiles come from the CORA4  (Cabanes et al. , 2013 ) in situ database provided by CORIOLIS data center from the start of the reanalysis up to December 2012. For the last 6 months we used the real-time database. A check through objective quality control and a data thinning have been done on the dataset in CORA4. Indeed, for each instrument, only one profile per day and within a 0. 1 • distance is selected. The best profile is identified thanks to a set of objective criteria on measurement resolution and number of validated measurements along the profile. In addition to the quality check done by CORIOLIS, SAM2 carries out a supplementary quality control on in situ observations. In order to minimize the risk of erroneous data being assimilated, the system automatically removes, through different criteria, the data too far from a seasonal climatology  (Lellouche et al.
Assimilation statistics We present here assimilation diagnostics to highlight that the reanalysis system is stable and well constrained by the assimilated observations. In this section, the evolution of the mean and the RMS innovation for all SLA, SST and in situ profiles are shown. The mean and the RMS of SLA innovation are presented in Fig. 3. The mean SLA innovation has a slight linear decrease of 0. 65 mm yr -1. This suggests that the volume correction (effect of the GIA and ice melting, see Sect. 2. 4) we applied is not accurate enough. On average over the whole period, the mean SLA innovation shows then a slight negative anomaly of -8 mm. We also note a seasonal cycle. This is probably due to inconsistency between ORAS4 interannual SSH fields in the Atlantic part and the assimilated data but a part of this problem could also come from runoff forcing.
If the seasonal variations represented in the runoff climatological values are not realistic enough, the error in the intake of water mass through the Mediterranean basin is directly transferred to the SLA innovation. The RMS of the innovation is steady all along the reanalysis and close to 6. 5 cm. This result is quite good, knowing that the RMS of observations is 8 cm (not shown here). The main constraint on the SST consists in the assimilation of in situ surface data and gridded maps derived from satellite measurements. Thus, for each cycle, we assimilate at least 243 values uniformly distributed every spatial degree and a variable amount of in situ data (Fig. 4 ). Before 2004, we note that the main part of assimilated data comes from the satellite data. The mean satellite SST innovation is close to 0 • C during the whole period of the reanalysis.
The assimilation of data restores good average values and realistic variability. It is interesting to notice that despite poor data coverage in the early 90s, the assimilation system is able to restore a realistic averaged salinity. As we noted in the previous section, we note a spurious positive anomaly in the MEDRYS salinity in the early 2000s. Those too salty and too dense waters have been formed in the surface layers and have been advected toward the bottom layers. We also detect it in the bottom layers until 2005. Considering the small number of assimilated data below 600 m depth, the model is only slightly constrained beyond this depth, especially before 2005. Thus, the reanalysis is quite close to the free simulation in terms of tendency and mean value for both temperature and salinity.
Temperature and salinity vertical profiles The model equivalent at the time and spatial location of the observations has been computed from daily averaged outputs. Mean and RMS differences over the whole Mediterranean basin were computed for 3 layers (0-150, 150-600, 600-4000 m) for temperature and salinity profiles (CLASS4 validation;  Lellouche et al. , 2013). We applied the same process with the profiles from MEDATLAS-1998. The MEDATLAS-1998 temperature and salinity fields are the initial states of short simulations used for process studies such as in  Beuvier at al. (2012a). Those fields have been obtained pondering by a low pass filtering with a time-window of three years, the MEDATLAS data covering the 1997-1999 period. The choice of centering the climatology on the late 90s corresponds to a compromise between a recent year (before 2002, the last field in MEDATLAS) and a sufficient data coverage in both temperature and salinity, knowing that the uncertainty associated with the MEDATLAS fields increases after 2000.
Only a daily dataset, checked through objective quality control, have been assimilated in MEDRYS. Large differences may appear locally in the CLASS4 scores with spurious observations. CLASS4 results complements here the statistics made against one week forecasts in Sect. 3. 1. We first assess the mean and RMS temperature differences between the analysis and the observation in Fig. 11. Concerning the layer-averaged mean differences, results are not fully consistent with comparisons made with integrated content in Sect. 3. 2. 2. Indeed, those statistics show that, on average, MEDRYS is very close to the observations (at the location of the observations). We only note a significant negative bias of 0. 03 • C in the layer 150/600 m on average over the period 1993-2012. The mean temperature difference in the two first layers of the reanalysis reproduces the interannual variability present in the observations.
This can be explained by the lack of salinity measurements and the poor data coverage in Mediterranean Sea under 1000 m depth, especially before 2005. In average, MEDRYS presents a lower RMS of the difference of temperature and salinity than MEDATLAS98. It is not surprising considering that MEDATLAS98 is composed of climatological monthly fields and does not represent the variability of the Mediterranean Sea along the whole period of 21 years. In the first 150 m, the RMS of the difference in MEDRYS increases with the summertime stratification.
High frequency variability: validation at LION buoy We assess here the ability of NM12-FREE and MEDRYS to reproduce the high frequency variability at the surface in the Mediterranean basin. In Fig. 13  CTD measurements) during HyMeX SOP2 (Special Observation Period 2 from 27 January to 15 March 2013) to the hourly outputs of the two numerical experiments at the same location. As we noted in paragraph 2. 4, the real-time database have been assimilated in 2013. Data from LION buoy were not yet available in real-time and were not assimilated. For both SST and SSS comparisons, MEDRYS is slightly closer to the independent observations than the free simulation, in terms of mean values and variability. Indeed, the mean surface water of MEDRYS shows a positive bias of 0. 07 • C and 0. 03 psu while NM12-FREE shows negative biases which are larger in magnitude (0.
Although the characteristics of the ocean are the same in the buffer zone in the two experiments, the amplitude of both inflow and outflow has been improved thanks to data assimilation in MEDRYS (Fig. 14 ). Despite the realistic value of the net flow through the Strait of Gibraltar, outflow and inflow are underestimated in NM12-FREE in comparison with results published in the last twenty years  (Bryden and Kinder, 1991; Bryden et al. , 1994; Tsimplis and Bryden, 2000; Candela, 2001; Baschek et al. , 2001; Lafuente et al. , 2002; Soto-Navarro, 2010). The reason of having a more accurate exchange at Gibraltar in MEDRYS is that the density difference between the inflowing and outflowing waters is larger (-2. 34 kg m -3 in MEDRYS and -2. 30 kg m -3 in NM12-FREE). In terms of net heat transport, the reanalysis and the free simulation (respectively 6.
Discussion and conclusion This study describes the configuration and the quality of the high resolution reanalysis MEDRYS and its companion free simulation NM12-FREE, for the Mediterranean Sea over the period 1992-2013. The common element to both simulations is the ocean model, NEMOMED12, a high-resolution regional configuration of the ocean general circulation model NEMO. The model is relaxed to ORAS4 interannual fields in the Atlantic buffer zone and forced at the surface with the homogeneous and high-resolution ALDERA atmospheric fluxes. The 21 years of the reanalysis have been produced using all available in situ profiles in the CORA4 database, SST maps from the daily NOAA AVHRR-AMSR product and along-track SLA from SSALTO/DUACS associated to SAM2 the assimilation scheme from Mercator Océan.
The 12 km and 3 h spatiotemporal resolution of ALDERA fields allows MEDRYS to explicitly reproduce diurnal cycle, and thus SST, and to simulate the impact of local winds on coastal oceanic areas. The consistency of ALDERA dataset along the whole 1979-2013, allows us to trust in the consistency of the interannual variability of processes known to be driven by air-sea interactions (mixed layer variability, surface circulation variability, etc. ).
In response thereto, the system tends to compensate by densifying surface waters. As the assimilation system is more constrained on temperature (due to better data coverage) it has a strong effect on salinity. We also point out that it had inconsistencies between ORAS4 interannual fields in the buffer zone and the assimilated data. To correct for those inconsistencies, it will be necessary to apply a correction to the ORAS4 SSH fields in order to better represent the seasonal variations of sea level in the Mediterranean. In further version of MEDRYS, we simply propose to correct the seasonal cycle and the trends of sea level anomalies in ORAS4 in order to match with altimetry observations in the buffer zone. Introduction.
Back Close Full According to additional works (not shown in this study), we realized that SLA innovations were strongly correlated with the mean wind patterns (Mistral-Tramontane, Aegean winds), suggesting that the hydraulic constraint component is not negligible in the Mediterranean Sea. Knowing that, the configuration of SAM2 has been adjusted in order to take into account the wind component in SSH. Moreover, as the effect of the wind at high frequency has been filtered from the SSALTO/DUACS database (SSALTO/DUACS User Handbook, 2014), it will be also necessary to filter it in the model. In order to better assimilate the SLA in Mediterranean Sea, we computed, from MEDRYS outputs, a new empirical observation operator for SLA data. This new configuration takes into account a larger proportion of dynamic height anomaly, determined using a statistical regression over the whole basin.
Acknowledgements. This work is a contribution to the  HyMeX program  (HYdrological cycle in The Mediterranean EXperiment) through  INSU-MISTRALS support and the Med-CORDEX program  ( COordinated Regional climate Downscaling EXperiment -Mediterranean region ). This research has received funding from the  French National Research Agency (ANR)  project  REMEMBER  (contract  ANR-12-SENV-001 ).
Clinical trials with MDM2 inhibitors in hematological malignancies A large number of MDM2 inhibitors have already progressed to advanced preclinical development or early phase clinical trials (Table  2 ). Structure-based optimization of nutlin 3 has been done to generate RG7112 (RO5045337; Hoffmann-La Roche Ltd), which is currently under testing in clinical trials for various types of cancer (ClinicalTrials. gov identifiers NCT01164033, NCT01143740, NCT00623870, and NCT00559533). RG7112 is resistant to oxidation, which had been a problem with earlier drugs, has a reduced molecular weight, decreased metabolic liability, and binds to MDM2 with an affinity of 11 nM compared to 90 nM for nutlin 3. 49  Therefore, RG7112 is three times more potent than nutlin 3 in inhibition of cell growth. The first published results of these trials come from a proof-of-mechanism study performed in patients with amplified MDM2 liposarcoma.
Activation of the p53 pathway in patients treated with RG7112 was demonstrated by an increase in serum levels of MIC-1 and the induction of p53 target genes, including MDM2, P21, and PUMA. This study shows that RG7112 has single-agent clinical activity, since it significantly lowers blast counts and even leads to complete remission in some AML patients (16%). 62, 63 The efficacy of RO5503781, another MDM2 inhibitor developed by Hoffmann-La Roche Ltd, is currently being tested in a Phase I/IB trial as a single agent or in combination with cytarabine in patients with AML (NCT01773408). This molecule is also under evaluation in another multicenter study designed.
Methods The BIO-RAIDs study is at the core of the European project called RAIDs "Rational molecular Assessment Innovative Drug selection" coordinated by Institut Curie. RAIDs is based on an international multidisciplinary cooperation between academic hospitals, SMEs and platforms for translational research www. raids-fp7. eu/ (Fig. 1 ). Institut Curie (France) is responsible for the overall coordination and management (study documents and data quality, statistical analyses). In countries other than France, the registration, management, and monitoring of clinical centers is delegated to a national coordinator.
Patient recruitment, data collection and biobanking Eligible patients with stage IB2-IV disease, scheduled for primary surgery, chemo-radiation or primary chemotherapy are invited to participate in this study. Table  2  details the inclusion and exclusion criteria. Documented informed consent is obtained for all patients. Patient data is anonymized and recorded in an electronic Case Report Form (eCRF) (Quanticsoft). The eCRF generates patient specific unique barcode numbers for each sample. At study entry, baseline demographic characteristics, medical history and findings of staging such as complete physical and gynecological examination, abdominal and pelvic CT, pelvic MRI (+/-optional PET-CT) are recorded. A central review of MRI imaging is planned to be performed in Serbia.
Bioinformatics and statistical analysis We try to assess which are the dominant mutations and which signaling pathways are activated in cervical cancers based on potentially deleterious genomic or proteomic alterations (COSMIC database). Furthermore we want to ascertain which abnormalities will be predictive of response to standard treatment and outcome. Analysis of a large number of patients is necessary because if a specific driver mutation is present in only 7 % of patients, 700 patients need to be evaluated to detect this mutation in 50 patients. No statistical hypothesis applies, but appropriate statistical methods will be used to analyze the results.
Results Evidence from in situ observation. We first compared daytime versus nighttime temperature accumulation for predicting in situ observations of LUD in Europe and the United States over the past 30 years (1982-2011). Twenty-four plant species from 2,400 phenology sites in Europe and lilac (Syringa L. ) shrubs from 35 phenology sites in the United States were selected from the European Pan European Phenological Database (hereafter EU) and the USA National Phenology Network (hereafter US) (Supplementary Fig. 1 ; see Methods), respectively. Temperature data included monthly averaged daily maximum (T max ) and minimum temperature (T min ) with a spatial resolution of 0. 5°o btained from the Tyndall Centre Climate Research Unit (CRU TS 3. 20; see Methods). Both precipitation and cloudiness were included in the partial-correlation analyses, while other variables such as soil moisture and soil temperature were not included, since temperature co-varies with these variables.
Methods In situ-observation data set. We used in situ observations of LUD from two independent phenology data sets. One is the Pan European Phenological Database (PEP725; http://www. pep725. eu/ ), which is an open-access database with longterm plant phenological observations from 19,608 sites and 78 species across 25 European countries. This data set has been widely used for studying the relationships between spring phenology and climatic changes, especially global warming  8, 27. To exclude potential biases caused by outliers and inadequate degrees of freedom, we removed species-site compositions with the dates of LUD later than June (180 DOY: day of the year) and focused on the sites with 415 years records over the period 1982-2011. In total, 2,400 phenological sites and 24 plant species from PEP725 were used in this study.
The GIMMS NDVI3g data set (1982-2011) with a spatial resolution of 1/12°and a 15-day interval has been used to monitor the phenological cycle of ecosystems  2, 30. Areas with sparse vegetation, that is, multi-year NDVIs o0. 1, were excluded from the analyses. Using NDVI3g data sets, we applied four methods (Spline Midpoint, HANTS maximum, Polyfit maximum and Timesat SG) to estimate the VGD. Detailed information about the four VGD-deriving algorithms and the uncertainty in VGD estimation from the 15-day interval NDVI data set have been documented by refs 15,30. Follow the previous study  15  , we applied a Bayesian constraint in each method to rule out the influence of snow cover and limit the VGD within the thermal growing season (5day average temperature 40 °C). The average VGDs of the four algorithms were used in this study (Supplementary Fig.
20 ), unless otherwise noted. Climatic data. Monthly data for T max , T min , T mean , precipitation and cloudiness were obtained from CRU TS 3. 20 and are available for a regular 0. 5°l atitude/longitude grid for 1982-2011 (ref. 31). Due to the lack of solar-radiation data in the CRU data set during the study period, we used cloudiness data. To independently validate the results based on the CRU data set, we also used 0. 5 Â 0. 5°latitude/longitude gridded 3-h climatic data applying WATCH Forcing Data Methodology to ERA-Interim data (WFDEI, 1982-2011)  32  , a 3-h global meteorological forcing data set (1982-2008)  33  and the station-level global-surface summary of day product (GSOD) by the National Climatic Data Center. Daily weather can be a very random event, and the satellite data are of biweekly resolution, so the climatic data from WFDEI, Sheffield and GSOD were then rescaled to weekly, biweekly and monthly resolutions.
For the GSOD data, we only considered the 2,510 stations with 415 years of available data for 1982-2011 and with NDVIs larger than 0. 1 for the 0. 5°latitude/longitude grids containing the stations. The data for short-wave radiation was obtained from WFDEI. Analyses. We used partial-correlation analyses to explore the effects of T max and T min on observed LUDs. With this approach, we could exclude the confounding effects of other climatic variables (precipitation and solar radiation) and of covariate effects between T max and T min (ref. 14). Temperature during the preseason dormancy period is arguably the most dominant factor for spring phenology  12  , and current phenology models in most DGVMs are solely based on temperature. It is therefore the aim of this study to identify the most appropriate temperature variables for use in phenology models.
The maximum k corresponded to the length of the period from the month of mean LUD (1982-2011) to the onset of preceding dormancy, defined as the month when the multi-year averaged mean temperature dropped to 0 °C, or November as a default value. The preceding months with the highest absolute partial-correlation coefficients were then considered as the T max -derived 'preseason', in which T max had the largest influence on the timing of green-up. Similarly, by replacing T max with T min , we also obtained the T min -derived preseason. To assess the robustness of our results, we also used grided climatic data sets at different temporal resolutions instead of the CRU monthly climatic data sets. In addition, to determine if winter chilling affected the responses of LUD to T max and T min , we performed the same partial-correlation analysis with T max , T min , precipitation, cloudiness and winter temperature as independent variables.
Accordingly, the monthly averaged values of each independent variable during the T mean -derived preseason were used in this analysis. The same partial-correlation and sensitivity analyses were applied to satellitederived observations, with preseason defined separately. To spatially match satellite data (1/12°spatial resolution) with climatic data (0. 5°spatial resolution), we used averaged VGDs within each grid of the climatic data set. Besides the same robustness tests as those in the species-site level analysis (see above), we also performed additional robustness tests by using VGDs derived from individual algorithms instead of the multi-method averaged VGD, using station-level climate data set at different time resolution instead of CRU monthly climatic data sets, as well as using VGDs derived from MODIS NDVI instead of AVHRR NDVI estimated VGDs.
Acknowledgements This study was supported by a  Strategic Priority Research Program (B)  of the  Chinese Academy of Sciences  (grant no. XDB03030404 ), the  National Basic Research Program of China  (grant no. 2013CB956303 ), the  National Natural Science Foundation of China  ( 41125004 ) and the 111 Project ( B14001 ). P. C. , I. J. and J. P. were supported by the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. S. V. is a postdoctoral research associate of the Fund for Scientific Research-Flanders.
" However, the real extent of genetic erosion is very difficult to measure using current data. Therefore, this challenging target demands better coverage, understanding and utilization of genomic and environmental data, the development of optimized ways to integrate these data with social and other sciences and policy analysis to enable more flexible, evidence-based models to underpin FAnGR conservation. At the conference, we attempted to identify the most important problems for effective livestock genomic resource conservation during the next decade. Twenty priority questions were identified that could be broadly categorized into challenges related to methodology, analytical approaches, data management and conservation. It should be acknowledged here that while the focus of our meeting was predominantly around genetics, genomics and animal science, many of the practical challenges facing conservation of genomic resources are societal in origin and are predicated on the value (e.