dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION This ITQ, involving more than 13,000 patients from 42 countries, is one of the largest surveys of its kind in diabetes, and perhaps in medicine. Its scope covers injection practices from beginning to end: choice of device through disposal. It elucidates the common habits that patients practice as well as the complications and other obstacles they face. Only in this manner can practical and targeted recommendations be made. Use of these data should stimulate renewed interest in and commitment to optimizing injection practices in patients with diabetes. A separate article in this issue covers the remainder of the ITQ and is titled "Worldwide Injection Technique Questionnaire Study: Injecting Complications and the Role of the Professional. "  62 A new set of insulin delivery recommendations based on the ITQ survey has been developed and is also published in this issue.
The vertex early or vertex late curvature of these arcs indicates whether the emission source was located westward (Jovian dawn) or eastward (Jovian dusk) of the observer (in the direction of Earth). Hess et al. [2012 Hess et al. [ , 2014] ]  showed that forward shocks (where the magnetosphere may be compressed by increased solar wind pressure) are often followed by emission from only one side of the magnetosphere. They showed that reverse shocks (where the solar wind pressure decreases and the magnetosphere may expand) are often followed by emission from both sides of the magnetosphere (i. e. , both vertex early and vertex late emission would be observed). At DOY ∼276. 3 and 276. 7, STEREO A and B data showed two bursts of decametric emission with only vertex early morphology, which suggests the incidence of two solar wind forward shocks at these times.
WRF simulations largely overestimate the annual totals, especially at low spatial resolution, while reproducing correctly the diurnal cycle and locating the spots of heavy rainfall more realistically than either the ground-based KED or the Tropical Rainfall Measuring Mission (TRMM) products. The main weakness of kriged products is the production of annual rainfall maxima over the summit rather than on the slopes, mainly due to a lack of in situ data above 3800 m a. s. l. This study also confirms that one limitation of TRMM is its poor performance over ice-covered areas because ice on the ground behaves in a similar way as rain or ice drops in the atmosphere in terms of scattering the microwave energy. While all three products are able to correctly represent the spatial rainfall patterns at the annual scale, it not surprisingly turns out that none of them meets the challenge of representing both accumulated quantities of precipitation and frequency of occurrence at the short timescales (sub-daily and daily) required for glacio-hydrological studies in this region.
A proper evaluation of these two variables is consequently a key issue for properly predicting the future of the glaciers and of the associated water resources. Published by Copernicus Publications on behalf of the European Geosciences Union. L. Mourre et al. : Assessment of WRF, TRMM and in situ precipitation data over the Cordillera Blanca, Peru The tropics are thermally characterized by an annual variation less important than the diurnal cycle (e. g. ,  Kaser, 1999; Baraer et al. , 2012). This applies to the Cordillera Blanca, where homogeneous thermal conditions are observed throughout the year  (Juen et al. , 2007). For instance, at Querococha, located in the southern part of the Cordillera Blanca, mean monthly temperature variation is less than 1 • C  (Kaser et al. , 2003). By contrast, there is a strong seasonality of precipitation, controlled by the upper air circulation, with easterly wind transporting moisture from the Amazon plain  (Aceituno, 1987)  and westerly flow causing dry conditions due to the Humboldt Current  (Garreaud et al.
These stations are of a tipping bucket type; they have the double advantage of being located at higher altitudes and of providing data at sub-daily time steps. As compared to previous studies in this region, the key new information used comes from a database of 16 meteorological stations with hourly data located in the An-cash region of Peru. They were installed in the framework of a project (Centro de Información e Investigación Ambiental de Desarrollo Regional Sostenible -CIIADERS), operated by the Universidad Nacional Santiago Antúñez de Mayolo (UNASAM) of Huaraz. These stations provide essential information for understanding the spatial (increased sampling density) and temporal (hourly resolution) distribution of precipitation within our study area. The SENAMHI data are routinely quality controlled, using standard procedures in use in the Met services worldwide.
WRF simulation In this study we use the high-resolution simulations from the WRF model version 3. 4. 1  (Skamarock et al. , 2008)  that had only a few applications in the tropical Andes  (Murthi et al. , 2011; Ochoa et al. , 2014; Sanabria et al. , 2014). The WRF is a nonhydrostatic model and uses a terrain-following vertical coordinate (sigma). The limited domain simulations are forced by a boundary condition every 6 h by the National Center for Environmental Prediction (NCEP) Final Analyses (FNL) Global Forecast System (GFS) with 1 • of latitude and longitude horizontal resolution. The elevation data set is from the USGS GTOPO30. A large tropical Andes domain was first delimited for simulations at a 27 km resolution (WRF27). Two sub-domains were then used for carrying out simulations at a 9 km (WRF9) and 3 km (WRF3) resolution, respectively, both being centered in the Santa River basin (Tables  2, 3 ).
WRF9 (WRF3) simulations are forced by the WRF27 (WRF9) simulations using a one-way nesting technique. The simulations begin on April 2012, the first 4 months being used as a spin-up period for producing 1 year of data to be compared to the KED and TRMM products. Figure  2  shows the boxes corresponding to each simulation domain, and Table  2  lists the resolutions and coordinates of each configuration. Table  3  lists the parameterizations used in the simulations. We use the Thompson microphysical scheme  (Thompson et al. , 2008)  and the Grell-Devenyi ensemble scheme for the cumulus parameterization. We also use a topographic correction for surface winds, previously tested in a complex orographic terrain of the Iberian Peninsula  (Jimenez and Dudhia, 2012). The Noah-MP (Multi-Physics) land surface model is used for the representation of land-atmosphere interaction processes  (Niu et al.
Methods and criteria used for comparing the rainfall products A total of seven gridded rainfall products are compared here, as described in Table  4. These products differ from one another on two accounts: (i) the type of information used (ground data, satellite data, atmospheric model), (ii) the spatial resolution, ranging from 27 km corresponding to the size of the TRMM satellite product grid mesh, down to 3 km, the finest resolution at which the WRF model was run. These seven gridded products are available at the daily scale, which is the corner scale for the comparison carried out in this paper. While TRMM products and WRF simulations are inherently gridded, in situ data need to be interpolated in order to build grids at the three spatial resolutions: 27, 9 and 3 km.
In the following, the gridded daily precipitation product at 27, 9, and 3 km spatial resolutions will, respectively, be referred to as KED27, KED9 and KED3 (see Table  4  and Fig. 3 ). The daily RMSE value is large (3. 41 mm d -1  ) compared to the mean daily precipitation over all stations (1. 85 mm d -1 ), and errors are reduced with aggregation on a yearly basis (RMSE of 271 mm yr -1 for an average in situ amount of 572 mm y -1 for the 32 stations and a correlation coefficient of 0. 78). In yearly values, kriging products will then be the basis of our comparison to TRMM data and WRF outputs. Despite some bias in the estimation of annual and daily rainfall, it is assumed that the most important spatial pattern is captured by KED.
Assessing the quality of the WRF3 hourly precipitation grids To facilitate the comparison among all stations, the hourly precipitation amounts were normalized by dividing them by the mean of hourly values during the year. Few studies deal with hourly rainfall amounts from WRF modeling. In this study, we compared the timing of the precipitation peak from hourly rain gauge data and from WRF3 simulation outputs. Studying hourly data allowed us to see whether short time processes governing precipitation in the Rio Santa Valley are well represented in WRF3, considering in situ hourly measurement as the reference.
In this comparison, KED3 seems to underestimate daily precipitation amounts and overestimate n p>1 in light of in situ data, but this can be related to a resolution effect between the 3 km resolution grid and punctual measurement. Noting that WRF products are unrealistic in terms of daily precipitated quantities, we will now evaluate their perfor-mances in terms of occurrence, a notion that is essential in glacio-hydrological studies. This can be seen in the results of the contingency table and is studied by comparing KED3 and WRF3 with in situ data for different daily precipitation thresholds in Fig. 5. The results are shown for the Sierra region, but are similar for the Cordillera Negra and Marañon areas. WRF3 largely overestimates the amount of strong daily precipitation, which can be linked to the overestimation of the product (Fig.
4d ). The FAR, POFD and HSS show that there is an important improvement considering only precipitation above 1 mm d -1 in KED3 and that the amount of daily precipitation between 0 and 1 mm d -1 is largely overestimated by this product (Fig. 5b-d ). POFD can be seen as an inter-comparison indicator as it does not depend on the number of predicted events. Above 1 mm d -1 , KED3 is then a better estimator of precipitation occurrence compared to WRF3. However, we faced the same spatial-resolution problem as above when comparing the 3 km mesh grid and in situ data for low-precipitation amounts. HSS indicates that daily precipitation in KED3 is in better accordance with in situ data than WRF3, with a few rainy days well predicted in WRF3. Although we noted a spatial-resolution effect for daily precipitation quantities under 1 mm d -1 , KED3 appears to be a good estimate of precipitation in terms of daily average quantities and occurrences, and will be considered later as a basis for comparison between different gridded products.
5 % for the meshes of Corongo and Shancayan. Error in dry season for Shilla can be seen as a poor consideration of ice-covered surface in the TRMM algorithm, as ice on the ground scatters energy in a similar way as precipitation drops in the atmosphere  (Yin et al. , 2004). Temporal trends of KED27 and WRF27 are similar, with occasional shifts of a few days in heavy rainfall events (for example, between days 200 and 230 for Corongo station; Fig. 8a ). Concerning the finer spatial resolution (Fig. 8d-f ), KED3 and in situ data have strong similarities for the three stations, and that confirms the use of the 3 km spatial resolution to compare gridded data with in situ punctual data. Regarding WRF3, intensities of precipitation peaks are false in the heart of the rainy season, but the temporal distribution remains close to that of rain gauge precipitation.
In situ data at Corongo (station no. 2), Shilla (station no. 12) and Shancayan (station no. 16) display a clear precipitation peak in the late afternoon, between 16:00:00 and 19:00:00 LT (Fig. 9 ). This diurnal cycle is visible in the WRF3 simulations, even though somewhat less pronounced (more rainfall around noon), and with a slight lag at Shilla and Shancayan. Looking at the diurnal cycle of precipitation at a regional scale (Fig. 10 ), it is noteworthy that the peak hour of precipitation occurs later in the bottom of the Rio Santa Valley (dark green for altitudes below 4000 m a. s. l. , and around 19:00:00 LT) than in the surrounding mountains (light green color, around 17:00:00 LT). A lack of hourly information at high altitudes prevents one from validating these hourly scale characteristics with observations, but they correspond to well-documented orographic processes (valley and mountain breezes)  (Biasutti et al.
A comparison with measured on-site data was carried out when relevant (diurnal and seasonal cycle, statistics of rainfall occurrence), while the ability of each product to reproduce some well-known spatial features of precipitation fields at various timescales (from annual down to daily) was analyzed when no obvious quantitative reference could be used. In line with the results of other studies, WRF27 simulations are found to be totally unrealistic in terms of annual quantities. WRF9 and WRF3 simulations are better in this respect but still largely overestimate the annual total, with WRF9 being additionally unable to capture properly the details of the spatial pattern that are well restituted by WRF3. This shortcoming of WRF9 can be explained by its resolution that is still too coarse to reproduce correctly the orographic influence, because a number of key features are smoothed out (for instance, grid meshes reaching altitudes above 5000 m a.
Acknowledgements. This study was performed thanks to the  IRD (French Research Institute for Development)  and the  LMI-GREATICE IRD program. Field work was carried out in cooperation with the UGRH and the  UNASAM. We are grateful to all those who took part in those field campaigns. Gratitude is expressed to the  Senamhi  ( Servicio Nacional de Meteorología e Hidrología del Perú ), to  Waldo Lavado , to the UGRH and to the  UNASAM-CIIADERS  project for making station data available. We also warmly thank our Peruvian colleagues  Ken Takahashi  and  Jhan Carlo Espinoza  from the  IGP (Instituto Geofísico del Perú)  who participated in the development and improvement of this work. We are grateful to  Dr Maussion  and an anonymous reviewer who made constructive and detailed comments, which helped improve the manuscript.
This interaction takes place at methylated promoter regions of several TSGs including p16 INK4A , p14 ARF (known as p19 ARF in mouse), both UHRF1 is protected from this process by its interaction with herpes virus-associated ubiquitin-specific protease (HAUSP). Through its SRA domain, UHRF1 recognizes hemi-methylated DNA during DNA replication and interacts with DNA methyltransferase 1 (DNMT1) and histone deacetylase 1 (HDAC1). UHRF1 can also interact with DNMT1 via its PHD domain. Both PHD and TTD are involved in the readout of histone methylation that are catalysed by histone methyltransferases G9a and Suv39H1. The UBL domain could be involved in the proteasome pathway encoded by the CDKN2A gene  [34] , and RARalpha  [7]. However, to our knowledge no data are so far available in the literature about the consequence on p14 ARF and RAR protein levels  [7].
Taken together, these data show that HAUSP has a dual regulatory role of UHRF1, by protecting it from autoubiquitination (Fig. 1 ) and by facilitating its association to the chromatin through the readout of the histone code. Therefore, HAUSP tandem may control TSGs expression via UHRF1 mediated by a ubiquitination/ deubiquitination balance and might be further considered as a key mechanism involved in controlling cell proliferation and apoptosis  [51]. A regulatory influence on TSGs by UHRF1 might also be mediated independently of DNA methylation, for instance through an enzymatic partner. Indeed, it has been demonstrated that UHRF1 inhibits the interplay between Tip60 and p53  [52]. Tip60, an essential partner of UHRF1  [53] , acetylates p53 at K120 to induce apoptosis  [54]. It was suggested that increased expression of UHRF1 found in cancer might be responsible for decreased activity of p53 and apoptosis failure in tumors  [52].
The reference loop for the measurement of strain components and stress values was that recorded at 80% of the fatigue life. The scanning electron microscopy (SEM) observations were conducted at an accelerating voltage of 20 kV on a FEI Quanta 400 tungsten-SEM fitted with an electron backscatter diffraction (EBSD) detector from Oxford Instrument. TEM observations were performed on a Philips CM30 at an operating voltage of 300 kV, while automated crystal orientation mapping (ACOM-TEM) were conducted at an accelerating voltage of 200 kV on a FEI Tecnai G2-20 equipped with the ASTAR TM acquisition system developed by NanoMEGAS  [33, 34]. TEM thin foils were beforehand prepared by jet-polishing using Struers D2 commercial solution at 15 V and 15°C. The orientation data was post-treated using the TSL OIM TM Analysis 7 commercial software provided by EDAX.
10. For the other specimens tested at strain ranges higher than Δε t = 0. 6%, in addition to the former observations, the other grains were decorated with a grid pattern of white bands as shown in Fig. 11. The more the specimen is strained, the higher is the amount of grains presenting this pattern. These bands crossed the entire grain from grain boundary to opposite one and propagated from neighbouring grains or twins. Close observations showed that they were free of precipitates. The nature of the white precipitate-free bands has been determined using the ACOM-TEM technique. A zone 4 µm 2 in area including precipitate-free bands was scanned at a 5 nm step with the ASTAR TM software. Fig. 12a  is virtual bright field image and Fig. 12b  is the inverse pole figure for copper crystals. No misorientation is pointed out between the band and the matrix, or is below the angular resolution of the system, evaluated at 1° with the selected experimental conditions  [34].
Acknowledgments This work has been financially supported by  Bpifrance  and the  Conseil Regional du Nord-Pas de Calais. The authors thank  A. Addad ,  D. Creton  and  J. Golek  for their technical assistance. The  SEM  and  TEM national facility in Lille (France)  is supported by the  Conseil Regional du Nord-Pas de Calais , the  European Regional Development Fund (ERDF).
A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer-Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures (T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1. 5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship.
The sensitivity of AMSR-E T b 's to soil temperature (T soil ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b 's to retrieve T soil is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T soil information was chosen to obtain SM in the 2003-2011 period. The new dataset shows a low bias (<0. 02 m 3 /m 3 ) and low standard deviation of the difference (<0. 04 m 3 /m 3 ) with respect to SMOS L3 SM over most of the globe's surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003-2011 period.
This is also the goal of numerical re-analyses, such as ERA-Interim  [2] , which use data assimilation techniques to merge observations and models in an optimal way during long periods of time. Soil moisture (SM) has been endorsed by the GCOS as an ECV  [3] , as it affects the water and energy fluxes at the land surface/atmosphere interface  [4, 5]. Therefore, one of the ESA CCI projects, launched in 2010, focuses on SM. As already mentioned, in order to use SM information for climate modeling, SM datasets spanning long time periods are needed. The ESA SM CCI has developed a strategy  [6] [7] [8] [9]  that consists of merging a posteriori different SM datasets obtained with different algorithms applied to data from different microwave active and passive sensors. The scaling of the different datasets is done using as a reference a land surface model (such as GLDAS Noah)  [6, 7].
The Soil Moisture and Ocean Salinity (SMOS) satellite  [10]  was launched in November 2009, and it is the first mission specifically designed to retrieve SM from space. Many studies have evaluated the SMOS SM dataset in comparison to other remote sensing datasets, models and in situ measurements  [11] [12] [13] [14] [15] [16] [17] [18] [19]. SMOS shows very good global performance although other remote sensing and model products can show better performances at some sites. In any case, datasets from the only two instruments specifically conceived to measure SM, SMOS and Soil Moisture Active Passive (SMAP), compare very well with each other  [20, 21]. Since the ESA SM CCI started shortly after the SMOS launch, SMOS is not included in the CCI. However, it is important to investigate ways of including SMOS data in the long-term SM datasets and of evaluating the new datasets.
One of the advantages of SMOS is that it can observe at a lower frequency  (1. 4  GHz) than previous instruments, and this frequency is less affected by the vegetation cover  [22]. A pertinent first step towards that goal is to study how to obtain a coherent dataset from SMOS and the NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E), which operated from 2003-2011. Two approaches have been discussed in the literature: (i) adapting the Land Parameter Retrieval Model (LPRM) algorithm to SMOS data  [23] ; (ii) using SMOS SM as a reference to compute local linear regression equations linking AMSR-E T b 's to SMOS SM and recomputing a SM dataset from AMSR-E observations  [24]. The present study is devoted to a third approach: using SMOS SM as reference to find a global non-linear regression relationship linking AMSR-E T b 's to SMOS SM and recomputing an SM dataset from the AMSR-E observations.
Preliminary results were presented in  [25] [26] [27]. Neural networks are a very efficient tool for computing global non-linear regressions. Recently, the ESA has released a new SMOS SM near-real-time product computed with neural networks  [28]. NNs have been used successfully to retrieve SM from AMSR-E observations over Mongolia, Australia and Italy  [29, 30]. NNs have also been used to test the a priori merging of active and passive microwaves  [31, 32] , including Advanced Scatterometer (ASCAT) and AMSR-E  [33]  or ASCAT and SMOS  [34]. In contrast, to our knowledge, this is the first time that NNs have been used to extend an SM record in time using a global remote sensing dataset as a reference to train an algorithm to retrieve SM from previous observations by another instrument. The NN training phase can be considered as a consistency check of AMSR-E T b 's and SMOS SM.
It has been proposed that training an NN with a land surface model could be an efficient approach to assimilate satellite data into that same model  [31, 32]. However, if the goal is creating a generic long-term data record suitable for general data assimilation applications, not using a land surface model to train the NN is a potential advantage as one does not want to assimilate information from one model into another. The rest of this paper is organized as follows. Section 2 describes the remote sensing and model datasets used. Section 3 presents the in situ measurements used for the evaluation and the comparison protocol. Section 4 discusses the training of the neural networks and the soil moisture retrieval. The new SM dataset is evaluated in Section 5 in comparison to SMOS L3 SM (Section 5.