dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this section, we discuss how the TEXES dataset we have presented may help to decipher by which mechanisms the 2010 Great White Storm disturbed Saturn's stratospheric thermal structure. The thermal structure we have measured adds some new information to the previous knowledge on the stratospheric state in mid July 2011. Our study definitely demonstrates that the thermal perturbation can be divided into two independent disturbances, lying at different pressure levels. The main disturbance was already identified by CIRS and VISIR/VLT observations  (Fletcher et al. , 2011 (Fletcher et al. , , 2012) )  as the B0 beacon. It is a warm vortex located in the mid-stratosphere. The second anomaly, is weaker, horizontally more extended than the beacon, and located in the upper stratosphere (p < 0.
Beacon The first characteristic of the B0 beacon that remains to be explained is its vertical structure. Our TEXES dataset confirms the CIRS and VISIR/VLT finding that the beacon thermal anomaly was centered at 2 hPa in mid-July 2011. This center pressure had changed during the evolution of the beacon, as the two early disturbances B1 and B2 were initially centered at 0. 5 hPa, before their merge resulted in a downward shift to the 2-hPa pressure level  (Fletcher et al. , 2012). In July 2011, the high spectral resolution power of TEXES allows us to conclude that, above this pressure level, the temperature was decreasing with altitude at least up to the 0. 2-hPa pressure level. Unfortunately, we cannot constrain firmly the temperature profile at pressures lower than 0. 2 hPa. We can only state that it was cooler than 160K.
A second distinctive feature of the beacon is its horizontal confinement. This can be readily seen by inspecting profiles of the potential temperature θ = T (p 0 /p) κ , where T is the temperature at the pressure p, p 0 the reference pressure (p 0 = 1000 hPa), and κ is defined from the specific heat at constant pressure c p and the universal gas constant R: κ = R/c p. In Fig. 14 , we compare potential temperature profiles measured from the TEXES dataset to that obtained from the CIRS limb dataset acquired before the storm outbreak, hence pertaining to the quiescent stratosphere  (Sylvestre et al. , 2015). In the 30°N-45°N latitudinal band, the potential temperature outside of the beacon remained largely unchanged compared to pre-storm conditions, while within the beacon, the potential temperature corresponded to that before the onset of the GWS at 1 hPa, i.
radiative transfer modelWe use a standard line-by-line radiative transfer model to compute synthetic spectra for given temperature and abundance vertical profiles as described in Guerlet et al. (2009). Our model consists of 360 layers equally spaced in log(pressure) between 10 4 and 10 -5 hPa. The column density is calculated assuming hydrostatic equilibrium, taking into account the latitudinal dependence of the gravitational field due to the rapid rotation and oblate geometry of Saturn. Our model includes the opacity due to CH 4 , CH 3 D, and H 2 quadrupolar lines and collision-induced continuum. For CH 4 and CH 3 D, we compute the opacity using the spectroscopic line parameters from the GEISA 2011 database (Jacquinet-Husson et al. , 2008). For the H 2 quadrupolar lines we use the Campargue et al.
5°W measured on July 15th and the beacon center measured by CIRS on July 8th, is indeed compatible with a drift rate of 1. 6°/day. In contrast, our center position of 52. 5°W measured on July 19th, is compatible with a drift rate 2. 5°/day between July 15th and July 19th, as well as between the TEXES observations performed on July 19th and the CIRS sequence taken on July 26th. Hence, our observations suggest that the transition between Phase II and Phase III could have been even sharper than revealed by the CIRS dataset, occurring in the July 15th-July 19th interval. However, a definitive conclusion cannot be reached on this issue because of our 5°-uncertainty on the exact beacon center longitude. The background temperature of 145±1K we measure outside of the beacon at the 2-hPa pressure level from the TEXES dataset is identical, within the error bars, to the temperature measured from CIRS limb measurements by Sylvestre et al.
(2015) on September 23rd, 2010, just before the onset of the GWS, or from CIRS nadir spectra on July 8th & 26th, 2011 by Fletcher et al. (2012). However, our inferred maximum temperature within the beacon is significantly lower than that measured by Fletcher et al. (2012) from the CIRS dataset. From observations taken on July 8th, they inferred temperatures in excess of 200K, while on July 26th, they found a maximum temperature still warmer than 190K. This disagreement between the CIRS and TEXES retrieved temperatures is specifically addressed in Sec. 4. 3 and 4. 4. The difference in maximum temperature measured from the TEXES dataset on July 15th and July 19th, respectively 180±0. 5K and 176±0. 5K, already suggests that atmospheric blurring of TEXES spectra is the major reason for this mismatch.
4b ): from initial guess set equal to the measured velocity, only 4 iterations are necessary to reach the thresholds for both the maximum and mean relative error. A Matlab script was implemented in order to automatize the search for the unsteady inlet condition. This algorithm has 4 main steps: (1) Set the measured peak velocity inside the vessel as initial guess for the inlet velocity in the CFD solver; (2) Launch a pair of STAR-CCM + simulations, with inlet velocity u(t) and u(t) + du respectively; (3) Extract the CFD peak velocity and calculate the error function e(t); (4) Check whether the thresholds are respected or not, in which case update the inlet velocity using Eq. 5 and restart from point (2). The whole process was run on a computer having an Intel ® Core ™ i7-2670QM processor with 16 GB of ram.
The main idea is that the input sequence, in this case a string of characters, is mapped to a vector which is fed into a recurrent neural network (RNN) to generate the output sequence conditioned on the encoding vector. We use LSTMs 8 (Graves, 2013) as the basic RNN unit since this has shown improved performance on various NLP tasks such as text generation. In our model, we stack two LSTM layers on top of each other: the first level is an encoder that reads the source character sequence and the other is a decoder that functions as a language model and generates the output. We also added a drop-out layer since this has been shown to improve performance  (Srivastava et al. , 2014). The model was implemented in Keras (Chollet, 2015), a python library for deep learning. Figure  1  shows the network hierarchy.
tions were not done independently. 6 Since the annotators did not have access to the original PDF files to check the original text, it was not possible to generate corrected text for some badly corrupted strings. 7 Since the annotators did not have access to the original PDF files to check the original text, it was not possible to generate corrected text for some badly corrupted strings. 8 An excellent low-level introduction to RNNs and LSTMs can be found at http://karpathy. github. io/2015/ 05/21/rnn-effectiveness/. In order to learn a robust language model, we fed the neural network with randomly corrupted input strings and provided the original (non-corrupted) strings as output labels. This way the NN learns both a character level language model that is domainspecific but it also learns to detect and eliminate random noise.
Conclusion In this paper we presented a method for the detection and correction of OCR errors in French patient files. Our method consists of a many-to-many sequence learner using LSTMs which is robustly trained on artificially corrupted good-quality training data in order to learn both the underlying character level language model, as well as to detect and eliminate noise in the input string. The relatively fast convergence of the models is likely due to the natural redundancy in the medical corpus. We experimented with two different methods of adding noise to the input and found that injecting information on likely character confusion pairs extracted from the training corpus had no positive impact on accuracy. Interestingly, the models are not good at detecting insertion errors, i. e. the detection of word boundaries.
33). A significant reduction in OS was shown in patients with vimentin-positive CTC compared to those without vimentin-positive CTC (median 305 days vs 453 days, p = 0. 0293). There was no significant difference in baseline PSA in patients with Ki67-positive CTC at baseline versus those without Ki67-positive CTC (p = 0. 228), but OS was significantly reduced in the Ki67-positive CTC group (median 512 days vs 751 days, p = 0. 0091). No changes in relative proportion of vimentin-or Ki67positive CTCs were observed in post-treatment samples compared to baseline. Conclusions: Analysis of vimentin and Ki67 expression can straightforwardly be assessed in CTCs from patients with mCRPC. Poorer survival outcomes were observed in vimentin-and Ki67-positive CTC patients. Translational study protocols: CEC-CTC (IDRCB2008-AOO585-50) and Petrus (NCT01786031).
Patients Peripheral blood (7. 5 ml) was collected from metastatic CRPC patients recruited to one of two Gustave-Roussy CTC translational study protocols, CEC-CTC (IDRCB2008-AOO585-50) and Petrus (NCT01786031). For eligibility, patients had to have a confirmed diagnosis of metastatic CRPC (either by contemporaneous or historical biopsy). All patients offered their written informed consent and were enrolled on institutional protocols approved by the local Gustave-Roussy ethics review committee. The following minimal clinical data were collected: CTC count at baseline and following their first cycle of treatment (at cycles 2-3), date of CTC sample, date of progression or death (if applicable), histology, metastatic sites, systemic treatment due at time of baseline CTC sampling, and previous treatment.
Statistics The association of CTC number with clinical characteristics and patient demographics were assessed using Fisher's exact test for dichotomous factors, and Mann-Whitney tests for continuous data. The Kaplan-Meier method was used to estimate overall survival, and the difference between CTC cohorts compared using the log-rank test. Chi-square tests were used to analyse categorical data. Data cutoff for survival estimates was March 1st, 2015: outcomes were censored if a patient had not died before this date. Overall survival is defined as the time from first CTC sample until death from any cause, cancer-related or otherwise. Statistics were analysed using GraphPad Prism 6. 03 for Windows. All p values were two sided and considered statistically significant at <0. 05.
This suggests that the presence of vimentin-positive or Ki67-positive CTC is more clinically important than their relative numbers or proportions, and that their presence in the early stages of advanced disease could be of greater prognostic significance than in later stages. Finally, there was no change in patient percentage or CTC proportion with Ki67-or vimentin-expressing CTCs before and after treatment, suggesting the potential of these techniques to offer predictive benefits of treatment remains unestablished. It is important to reflect on the deficiencies of this dataset and its analysis. The results here are hypothesisgenerating and should be interpreted within the context of a retrospective observational design. Survival data reported was not adjusted for patient treatment, and mCRPC patient numbers tested were relatively small, particularly for the Ki67 cohort.
Mesenchymal or EMT CTC, as represented by vimentin expression in this study, have previously been associated with tumour invasiveness, chemoresistance, CTC clustering, stem cell characteristics, and poorer clinical outcomes  [29, 30]. Although not validated by our study, the results  generated here are generally consistent with this proposed biological and clinical phenotype. In parallel with the poorer prognosis seen in this study for those patients with vimentin-positive CTCs at baseline, the lack of survival difference observed in those patients whose samples contained the highest proportion of vimentin-positive CTCs is consistent with (but does not confirm) the stem cell theory that just one vimentin-positive cell will suffice to contribute to poorer outcomes. Armstrong and colleagues previously used the CellSearch platform to offer a detailed biological examination of vimentin and other markers of the EMT program in samples from patients with breast and prostate cancer, revealing the existence and high frequency of CTCs that can co-express epithelial and mesenchymal proteins  [25].
Acknowledgements Thank you to the  Institut National du Cancer  and  Cancéropôle Ile de France  for their funding of the PAIR prostate program contributing to this research (Grant No  2010-174 ). CL was supported by  ESMO  [translational research fellowship] with the aid of a grant from  Hoffmann-La Roche. Any views, opinions, findings, conclusions, or recommendations expressed in this material are those solely of the author(s) and do not necessarily reflect those of  ESMO or Hoffmann-La Roche. CL is also a recipient for the grant  DUERTECC/EURONCO  ( Diplôme Universitaire Européen de Recherche Translationelle et Clinique en Cancérologie ).
We introduce a method -called ADAMONT (ADAptation of RCM outputs to MOuNTain regions) v1. 0 -to downscale and adjust daily climate projections from a regional climate model against a regional reanalysis of hourly meteorological conditions using quantile mapping. The method produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short-and 5 longwave radiation, which can in turn be used to force any energy balance land surface model. The ADAMONT method is evaluated through its application to the ALADIN-Climate v5 RCM forced by the ERA-Interim reanalysis, compared to the SAFRAN reanalysis, used as the pseudo-observation database covering the entire French Alps split into 23 massifs within which meteorological conditions are provided for several elevation bands separated by 300 m altitude.
Different evaluation 10 criteria are analysed for temperature, precipitation, but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data generated using this method. The impact of the learning period and of the method used to select neighbouring RCM grid points for each SAFRAN massif/altitude configuration is tested. The performance of the method is satisfying, with similar or even better evaluation metrics than previous literature findings. Results for tempera-15 ture are generally better than for precipitation. Snow depth yields good results, which can be viewed as indicating a reasonably good inter-variable consistency of the meteorological data produced by the method. The temporal transferability of the method is assessed through the comparison of results obtained using different learning periods, and shows that the method is sensitive to the period considered due to the empirical treatment of values beyond the 99.
Various downscaling and bias adjustment methods have been developed  (Maraun et al. , 2010; Teutschbein and Seibert, 2012, 2013). They are generally separated into two categories depending on the approach used, but they all require an observation dataset which (i) meets the data requirements of the impact model and (ii) is sufficiently long and reliable to be used to infer the relationships between the observations and the raw climate projections during the observation time period. Perfect prognosis approaches search for relationships between observed large-scale predictors (generally from reanalyses) and observed local-scale predictands. The analog  (Dayon et al. , 2015)  and the weather typing  (Vrac et al. , 2007a ) methods fall into this category. In contrast, model output statistics approaches calibrate model outputs against observations.
e. if the model has a chronological behaviour which differs from the observations (too chaotic or too persistent), this will not be 75 adjusted  (Déqué, 2007). Moreover, quantile mapping does not guarantee spatial and inter-variables consistency, which is the case for the analog method for example. The choice of the downscaling and adjustment method implemented depends on the application and on the availability of raw climate projections and appropriate observation datasets. Raw regional climate projection data are increasingly made available to a broad scientific community, 80 e. g. the World Climate Research Program (WCRP) Coordinated Regional Downscaling Experiment (CORDEX,  Giorgi et al. (2009) ), which aim is to improve and distribute regional climate modelling worldwide. Its European branch, EURO-CORDEX  (Jacob et al.
, 2014) , gathers regional climate simulations over Europe from 30 different modelling groups at 50 km (EUR-44) and 12. 5 km  resolution. On the observation side, the use of surface meteorological reanalysis is a powerful alter-85 native to station observation data to provide the necessary observational dataset  (Berg et al. , 2015). Indeed, the process by which such reanalyses are generated address the time and space variations of the meteorological conditions, and by design they consist of gap-free and complete time series. In mountainous regions, snow on the ground plays a pivotal role, but the snowpack response to meteorological conditions depends on intertwined processes, involving surface mass and energy balance 90 as well as internal processes (e. g. Martin et al. , 1994). Not only temperature and precipitation act on the snowpack, but a broader range of meteorological conditions and their diurnal variations.
As a consequence, considering only downscaled and adjusted daily temperature and precipitation would miss some of the non-linear response of the snowpack. The SAFRAN meteorological analysis has been developed specifically to address the needs of snowpack numerical simulations, and contains 95 hourly time series of temperature, precipitation, wind speed, humidity, and short-and longwave radiation for so-called massifs (ranging between 500 and 2000 km 2 in the French Alps) by elevation steps of 300 m  (Durand et al. , 2009a, b). Combining the strength and efficiency of the quantile mapping downscaling and adjustment methods using the SAFRAN reanalysis as a pseudo-observation dataset in order to drive energy balance snowpack and land surface models is a highly desirable 100 goal making full use of the current capabilities of climate impact assessment tools for mountainous regions.
SAFRAN reanalysis The SAFRAN system is a meteorological downscaling and surface analysis system  (Durand et al. , 1993) , which provides hourly data of temperature, precipitation amount and phase, specific humidity, wind speed, and shortwave and longwave radiation for each mountain region (called massif) in the French Alps (23 massifs, as illustrated in Fig. 1 ). Massifs  (Durand et al. , 1993 (Durand et al. , , 1999) )  correspond 135 to regions ranging approximately between 500 and 2000 km 2 for which meteorological conditions are assumed to be spatially homogeneous but vary with altitude. SAFRAN data are available for elevation bands with a resolution of 300 m. SAFRAN was used by  Durand et al. (2009b)  to create a meteorological reanalysis over the French Alps by combining the ERA-40 reanalysis  (Uppala et al.
g. Gottardi et al. , 2012; Kotlarski et al. , 2012). 2. Four different daily weather regimes were diagnosed from ERA-Interim, based on the geopotential height at 500 hPa, following  Michelangeli et al. (1995). 3. SAFRAN data are integrated from hourly to daily to match the data content of the available RCM output. The integration method depends on the variable considered (see Table  1 ). 4. Percentiles of the RCM distribution and the SAFRAN distribution are then calculated at each point, for each variable, each season (DJF, MAM, JJA, SON) and each weather regime. Each snow year is considered from the 1 st of August to the 31 st of July of the following year. 205 5. Quantile mapping is then applied to the entire RCM dataset for the period 1980-2010, taking into account the season and the weather regime.
6. For each day in the RCM dataset, an analogous date is chosen in the SAFRAN dataset, matching the following criteria: the month and the regime must be the same as in the RCM dataset, mean precipitation over the Alps must be consistent between datasets (i. e. if there is no pre-210 cipitation in the RCM, precipitation in the SAFRAN analogue must be zero), and whenever possible, consecutive time slices are chosen in the SAFRAN dataset in order to avoid artificial jumps in the final data linked to the choice of analogues. 7. The adjusted RCM dataset is then disaggregated from a daily integration period into an hourly time step, by using the hourly SAFRAN data from each analogous date chosen in the previous 215 step to reconstruct the daily cycle of the data. Different disaggregation criteria (mean daily value, maximum and minimum values, value of the last time step, smallest possible jump between consecutive days,.
Discussion This study introduces and evaluates a downscaling and adjustment method for daily RCM model 535 output using a reanalysis of hourly meteorological data tailored for mountain regions as a pseudoobservation database (SAFRAN). The ADAMONT method applies quantile mapping depending on the season and the weather type, and uses analogue dates from the observation database to disaggregate the daily adjusted/downscaled meteorological data into hourly time series. This section discusses the main limits of the method described and evaluated here, and the limits of the evaluation 540 method itself.
11. 11 and 11. 12 therein). Using the longer learning period 1980-2010 generally allows for a good compromise, except for precipitation during summer months as already noted above. However, one should keep in mind the limits of evaluating the temporal transferability of our method. The first is the temporal heterogeneities of reanalysis data  (Sterl, 2004; Vidal et al. , 2010)  565 used here as forcing for the RCM (ERA-Interim) or for downscaling/adjustment and evaluation purposes (SAFRAN renalysis). These heterogeneities are especially marked in summer in the SAFRAN reanalysis, when most observations from mountain stations are not available. The second is the fact that variations which will occur in the future climate will be much stronger than the variations which can be tested on our evaluation period.
S1-S207), which are located less than 15 km from high-altitude points. This shows that, 580 although it seems appealing to select RCM grid points at elevations matching the elevation of the observation dataset, rather than using RCM grid points with a potentially large elevation difference (hence leading to stronger downscaling/adjustment), in practice the results are far more homogeneous and quantitatively generally equivalent or better when concentrating only on the horizontal distance between the RCM grid points and the observation dataset.
Geosci. Model Dev. Discuss. , doi:10. 5194/gmd-2016-168, 2016   Manuscript under review for journal Geosci. Model Dev. Published: 29 September 2016 c Author(s) 2016. CC-BY 3. 0 License. Table  1. Variables considered in this study : Variable name, Abbreviation, Input or Output of Crocus, Units, Level and Method of integration (SAFRAN from hourly to daily) and disaggregation (ALADIN adjusted data from daily to hourly). Variables used for the evaluation of the ADAMONT method are highlighted in bold characters. SW = shortwave, LW = longwave. Variable.