dataesr/scientific-paragraphs-categorization huggingface dataset:
We can easily imagine a multiple modality measuring system that could capture all at the same time. In this paper, we present two new methods (and a conceivable fusion scheme) to compare corneas, using both anterior and posterior surfaces to improve separability. The first approach is based on a spherical harmonics decomposition and the second one is based on a registration technique aiming at determining the minimal residual volume between corneas to be compared, these methods give equal error rate (EER) values of 2. 7 and 1. 2%, respectively. The first one provides a fast comparison to perform a biometric identification by storing only few discriminant coefficients (i. e. comparing one cornea to a large database). The second one is geometry based, and need to store and to compute the whole geometry to consider thin details of the corneal shape more accurately.
Results using inter-surface residual volume The results of our second method are presented in Figs. 12a-c , using the same dataset. As for Zernike and spherical harmonics, the error decreases from an EER of 2% (F1-Score = 0. 98 and MCC = 0. 96) to 1. 2% (F1-Score = 0. 99 and MCC = 0. 98) when adding the second surface (Fig. 12c  solid ROC curve).
2% followed closely by spherical harmonics (dot-dash curve) with an EER value of 2. 7%. Notice that the residual volume method is better for all thresholds of the ROC curves. In addition, in an identification context the inter-surface residual volume method gives a rank-one value of 97. 2%. Although the spherical harmonics method is the second best method (based on EER), it is the best method based on coefficient comparisons. This fact is important because a method based on coefficients allows the storage of a small number of values (only 36 values here) for each surface, and therefore searching for a cornea is much faster. For example, biometric identification (i. e. comparing one cornea to a large database) is possible with this method and would take only a few seconds for a dataset composed of thousands corneas.
e. spherical harmonics) to prune the database. If a computed distance is lower than a predefined lower threshold, the subject is immediately identified (true match), without further processing. This threshold can be chosen to get the false match rate = 0 to guarantee that no false positive occurs. Conversely, if the distance is more than another predefined upper threshold, the current enrolled individual of the database is immediately rejected (true non-match). This threshold can be set to get the false non-match  rate = 0 to guarantee that no false negative occurs. Computed distances between these two thresholds are uncertain and require using a second matcher (residual volume) to identify the subject. This kind of fusion strategy will greatly reduce the computation time required by using the residual volume method alone by eliminating 'easy' cases with the faster spherical harmonics method.
In our study, with spherical harmonics of both surfaces, only 9. 3% (overlapping region in Fig. 11b ) of the database would need further processing with the more accurate but slower residual volume method. In comparison with some other modalities, corneal biometry is certainly promising. For instance, the 3D ear shape can achieve an EER value of 1. 2%  [17] , which is similar to our best results. Other biometric traits such as gait are typically inferior with the EER value around 5% or higher  [18]. Other modalities such as fingerprints are better (e. g. EER ranging from 0. 2 to 0. 4%  [19] ) but one should take into account that our study is only a first step in this new domain and that corneal measures could be combined with others (iris, retina) within the same apparatus in the future.
g. with LDA: linear discriminant analysis). As for the residual volume comparison it could also be improved by adding other features such as the corneal thickness as a part of the discriminating features to represent in a better way the whole corneal volume (instead of its two surfaces only). In addition, it would be interesting to conduct a long-term repeatability study since the cornea might slowly evolve with age, to determinate how often a corneal database would need to be updated.
A similar study has been conducted in the United States in 2010 using patient database and focusing on the off-label use of some expensive intravenous agents (including the monoclonal antibodies cetuximab, rituximab, trastuzumab, bevacizumab)  [12]. The frequence of off-label use was 30% of that half was clinically supported by the National Comprehensive Care Network (NCCN). Among agents, the rate of off-label utilization also varied considerably between trastuzumab (1%) and rituximab (67%)  [12]. Another American study based on insurance administrative database found a rate of off-label use of rituximab of 25. 3% during the period 2001-2007  [13]. Around 50% of off-label use was evidence-based. Among targeted therapies, rituximab is the agent that probably carries the greatest potential for off-label indications mostly beyond oncology.
Indeed, a Spanish prospective investigation reported that rituximab was the most frequently used agent off-label (21. 1%) among 232 drugs (considering all therapeutic classes) in 5 tertiary hospitals during one year (2011-2012)  [14]. In addition, a prospective Australian national study found that offlabel use of rituximab covers 63 different diagnosis with 89% of off-label use outside oncology in the year 2012  [15]. This is not surprising because rituximab is a non specific lymphocytical agent having potential numerous applications in the treatment of corticosteroid-refractory autoimmune diseases. An Italian investigation described the off-label utilization of bevacizumab during the period 2006-2007 using patient database in the region of Lombardy  [16]. The anti-angiogenic monoclonal antibody was mostly used (81.
Acknowledgments Thanks, with all usual disclaimers, to  Giorgio Fagiolo , as well to participants to the workshop "Schumpeter and the Schumpeterians on economic policy issues" organized in Amiens, May 2014-in particular,  Agnès Festré. The authors gratefully acknowledge the financial support of the  Institute for New Economic Thinking (INET)  grants # 220 , "The  Evolutionary Paths Toward the Financial Abyss and the Endogenous Spread of Financial Shocks into the Real Economy " and  INO12-00039 , " INET Task force in Macroeconomic Efficiency and Stability ".
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:  Maaike H. Oosterveer  holds a  Rosalind Franklin Fellowship  from the  University of Groningen. The research is financially supported by the '' Ligue Nationale Contre le Cancer,'' ''Ligue Régionale contre le cancer,'' and ''Stichting Metakids. ''.
Its replication is dependent of an inserted E2F promoter that induces replication in transformed cells with an inactive RB pathway. Furthermore, the virus is deleted of the E3 protein that normally inhibits antigen presentation by HLA class I molecules in the infected cells. In phase I clinical trial, impressive clinical results were obtained for the treatment of nonmuscle invasive bladder cancer in patient by intravesicular injection of the CG0070. 48. 6% of complete response rate was observed with CG0070 in patients that had failed the first line therapy consisting in injection of BCG and chemotherapy  [9]. This clinical trial has now reached the phase III (NCT01438112). Reolysin is an oncolytic reovirus that infects preferentially transformed cells with an activated Ras pathway  [10]. It spares healthy cells that do not have activated RAS pathway.
Several reports of phase I and II clinical trials have been published for the treatment of different solid tumors such as melanoma or lung carcinoma  [11]. This OV was also evaluated in a completed phase III clinical trial for the treatment of platinum-refractory head and neck cancers, but results are not yet published (NCT01166542). The last OV to reach phase III clinical trial is the JX594, also known as pexastimogene devacirepvec (Pexa-Vec). JX594 is a recombinant Wyeth strain of vaccinia poxvirus that has been modified by addition of the GM-CSF gene to stimulate the anti-tumor immune response and deletion of the thymidine kinase gene. Thymidine kinase activity is required for replication of this virus and can be found in tumor cells where this activity is high. After encouraging clinical results of the phase II clinical trial to treat advanced hepatocellular carcinoma patients by intratumor injection of JX594  [12] , a phase III clinical trial is planned by SillaJen Biotherapeutics and Transgene SA  [13].
Acknowledgements Jean-Francois Fonteneau  research is financially supported by  "la Ligue Régionale Grand Ouest contre le Cancer  (CSIRGO:  CD16 ,  CD22 ,  CD44 ,  CD49 ,  CD72 ,  CD79  and  CD85 )", " La Ligue Nationale contre le Cancer ", "ARSMESO44 association", "la  Fondation du Souffle et le Fonds de Dotation Recherche en Santé Respiratoire", "la Fondation pour la Recherche Médicale (FRM) ", and "la Fondation ARC pour la recherche sur le cancer".
During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties.
In this paper we present the Sodankylä CAL-VAL reference site soil moisture observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer soil moisture observations between the years 2012 and 2014 against ESA CCI soil moisture data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL-VAL network sites. Comparisons are made with daily averaged and running weekly averaged soil moisture data as well as through application of an exponential soil moisture filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.
Introduction Soil moisture fluctuation plays an important part in water, energy, and carbon cycles. Both latent and sensible heat fluxes, governing the air temperature and humidity boundary layer over land, are affected by variations in soil moisture. For these reasons it plays an important role in the climate system  (Legates et al. , 2011). Global-scale and high temporal resolution soil moisture observations are urgently needed to monitor the ongoing changes in the climate. Soil moisture is estimated by either in situ measurements, remote sensing techniques  (Kerr et al. , 2010) , or by Earth system models, e. g. ECHAM6 combined with Jena Scheme of Biosphere-Atmosphere Coupling in Hamburg (JSBACH)  (Roeckner et al. , 2003; Reick et al. , 2013). Though several soil moisture networks have been developed all over the world, with most of them included in the International Soil Moisture Network's (ISMN) database  (Dorigo et al.
(2011). Top soil moisture for each in situ site are approximated from 5 cm depths apart from sites on Umbric Gleysol soil (UG forest 1 and UG forest 2 sites), where the organic layer is typically much thicker than in, e. g. , Podzol soils. With these in situ sites, top soil moisture is approximated from depths of 10 cm. Soil moisture comparisons are only conducted during snow-free and thawed periods, as the presence of snow and soil frost interfere with EO data-based soil moisture retrievals  (Chung et al. , 2014a) , such as is the case with the ESA CCI data product. Snow-free and thawed conditions are determined by a data quality filter provided as a part of the ESA CCI product itself  (Scipal et al. , 2005)  combined with direct Sodankylä CAL-VAL site snow observations. Figures  7  and 8  show daily and weekly ESA CCI and in situ soil moisture time series as well as corresponding scatter plots for the years 2012, 2013, and 2014, separately.
Corresponding statistical metrics can be found in Table  3. The overall correlation between the daily ESA CCI data product and daily spatially weighted average in situ observations is rather low at 0. 479. The best values are achieved in 2012 and 2013 at 0. 551 and 0. 621, respectively. In 2014 the correlation is very low at only 0. 186. The overall unbiased root mean square error (RMSE) is generally relatively low at 0. 039, with the highest RMSE value (0. 049) observed for 2014. All years exhibit a negative bias ranging from 0. 0346 to 0. 046. K n = K n-1 K n-1 + e - Application of the exponential filter to daily ESA CCI soil moisture estimates significantly improves correlation results against in situ observations. Application of the exponential filter does not have any effect on bias and the effect on RMSE is insignificant.
The degree of improvement in correlation is heavily dependent on the assigned T parameter value (tem-  poral variation of soil moisture within the root-zone profile, in days). In deriving our comparison results we have assumed a T parameter value of 3. 5 as suggested by  Brocca et al. (2011). We have also included comparison results using a T parameter value of 1 in order to demonstrate the sensitivity of this parameter to the filter. The average overall daily correlation between the ESA CCI data product and spatially weighted average in situ observations improves from 0. 479 to 0. 629 when assigning a T parameter value of 3. 5 and to 0. 559 when a T parameter value of 1 is assigned. The improvement in correlation between in situ data and ESA CCI estimates in 2012 ranges from 0. 551 to 0. 833 depending on which T parameter value is used.
Similarly, the improvement in correlation between in situ data and ESA CCI estimates in 2013 ranges from 0. 562 to 0. 782, again depending on which T parameter value is used. In general correlations  are better with a T parameter value of 3. 5. Interestingly the application of the exponential filter has very little effect on correlation results for the year 2014, and, in fact, the correlation is lower when a T parameter value of 3. 5 is assigned compared to when a value of 1 is assigned, albeit the effect is rather insignificant. Averaging ESA CCI soil moisture data from daily to weekly estimates significantly improves both correlation and RMSE, but has little effect on bias. The average correlation between the ESA CCI data product and spatially weighted average in situ observations improves from 0.
479 to 0. 637 and the average RMSE decreases from 0. 039 to 0. 026. The improvements in correlation are most pronounced in 2012 and 2013, with an improvement from 0. 551 to 0. 840 and from 0. 621 to 0. 813, respectively. Similarly RMSE decreases from 0. 034 to 0. 015 in 2012 and from 0. 033 to 0. 020 in 2013. The temporal correlation statistics between spatially weighted daily in situ data and ESA CCI soil moisture anomalies are very low. Anomaly correlations have been estimated by calculating the difference to a moving average and scaling it to the standard deviation, as in  Brocca et al. (2011). Three different anomalies were considered: (1) deviation from 7-day average, (2) deviation from 15-day average, and (3) deviation from 31-day average. The corresponding statistical metrics can be found in Table  4.
The low anomaly correlations indicate that the ESA CCI soil moisture product is unable to capture rapid changes in top-layer soil moisture. Interestingly the anomaly correlations are in all cases higher for 2014, although the performance of the ESA CCI soil moisture product is clearly lower according to our comparisons against spatially weighted in situ data in 2014. The higher anomaly correlations for 2014 are, however, not significantly higher, and therefore as such do not provide an explanation to the poor comparison statistics for 2014. In fact, the higher anomaly correlations for 2014 are unexpected since the comparison statistics for 2014 against spatially weighted in situ data are significantly poorer compared to 2012 and 2013. The general in situ observation soil moisture trend during the summer of 2012 is descending, while switching to an ascending trend towards the autumn period.
In 2013 the overall in situ soil moisture observation trend is primarily descending, with the exception of early summer and late autumn. These same general trends are also apparent in the ESA CCI data product, and therefore the correlations for these years are higher, as compared to 2014. It appears that in 2014 in situ observations exhibit larger wet-dry variation compared to the years 2012 and 2013, with no clear overall trend. In situ observation variation and lack of a general trend is, however, not observable in the ESA CCI data product and the general trend, as in all other years is descending, resulting in considerably poorer correlation with both the daily and weekly spatially weighted average in situ observations. In conclusion, it is clear that there is better correlation between ESA CCI soil moisture estimates and daily spatially weighted average in situ observations during years exhibiting a long-term trend in the data and less short-term variation.
In our study, soil moisture measurements network data was compared in a land cover sensitive scheme to the blended soil moisture product of the ESA soil moisture CCI initiative over several summer periods. While the overall achieved correlation for the daily ESA CCI was low (0. 479), this was largely the result of a single year of observations (2014) with poor correlation metrics. The ESA CCI data product is derived from AMSR2 and/or ASCAT observations. In the case of the Sodankylä test site and surroundings, only ASCAT observations have been applied. We assume that what is referred to as top-layer soil moisture, can in fact be considered to refer to soil moisture at a depth of only 1 cm or less. However, this has not been explicitly defined in the description of the ESA CCI data product, or definitively proved to be the case in other studies.
Nicolai-Shaw et al. (2015)  and  Dorigo et al. (2015) , however, supported our initial assumption that the ESA CCI data product represents soil moisture data at a depth of 0-5 cm. Furthermore, in both of these examples ESA CCI soil moisture is compared to in situ soil moisture observations made at depths of 5 or 10 cm. Despite our initial assumption on ESA CCI data representativeness in terms of depth, applying an exponential filter to relate ESA CCI data to depths that correspond with the used in situ observation depths can in our view supply valuable additional information. In general, applying an exponential filter to theoretically improve the relation of ESA CCI soil moisture estimates to in situ observation depths significantly improves correlation metrics. There exists, however, a possible drawback in its application related to the need of an additional parameter, T (temporal variation of soil moisture within the root-zone profile, in days).
As with the application of the exponential soil moisture filter, it can, however, be argued that the smoothing of noise through averaging data over a particular time period can in some cases simply just be used as a way to artificially improve statistical metrics. However, in various operational activities such as terrain trafficability analysis, inflow forecasting for hydropower production and flood forecasting, identifying a longer-term trend in soil moisture data can be useful. Therefore, evaluating the accuracy of EObased soil moisture data with this perspective is warranted, and in some cases even desirable. It is apparent from the comparison results that the ESA CCI data product has issues with noise between observation steps. Reduction of ESA CCI data noise by smoothing results to a running weekly average improves correlation against spatially weighted average in situ observations considerably.
This could possibly point to issues stemming from ESA CCI data acquisition times. In Sodankylä, and in boreal and sub-arctic zones in general, moisture often condenses over night into morning dew on vegetation. This could interfere with active microwave soil moisture retrievals acquired during the early morning hours. In order to investigate this, we created a subset of comparisons where ESA CCI data collected during morning hours were removed. Comparisons were also conducted against hourly in situ observations corresponding to ESA CCI data overpass times. However, neither one of these comparisons resulted in significant changes in correlation, RMSE or bias. Therefore, it is not clear to us if these shortcomings are related to issues with the retrieval algorithm, scaling issues induced by the GLDAS-Noah model or possible issues associated with the LPRM model.
The latter, however, seems less likely according to our findings as diurnal effects of vegetation moisture do not appear to be the cause. The effect of conducting comparisons of spatially weighted daily average in situ moisture against ESA CCI soil moisture data, which is intervallic by nature, was also investigated. However, as with the removal of observations conducted during morning hours, this also had little effect on any statistical metrics. The apparent soil-moisture-scale difference (dry bias) between spatially weighted average in situ observations and the ESA CCI data product is significant but not very meaningful. This is true even when it is likely that our weighted in situ average soil moisture level itself somewhat underestimates overall soil moisture content due to the use of surrogate in situ sites, as detailed in previous sections.
Further studies in to the apparent anomalies in the ESA CCI data product and assumption made in producing the data product should be conducted. For example, the validity of using a single, inter-annually constant, correction variable to counter the effect of vegetation moisture interference in soil moisture retrieval should be assessed. Although our findings do not directly point to an issue related to this, it could still explain at least part of the noise and variability of soil moisture estimates between the daily time steps. Further comparisons and analysis of ESA CCI soil moisture data over larger (boreal) areas and longer time spans against both in situ observations and distributed soil moisture model estimates will be performed in future studies.
Acknowledgements. This research was supported by the framework of the  ESA-funded Climate Change Initiative  programme (CCI phase 1:  European Space Research Institute (ESRIN)  contract no. 4000104814/11/I-NB ; CCI phase 2:  European Space Agency (ESA)  contract no. 4000112226/14/I-NB ) and by the  Finnish Academy Centre of Excellence in Atmospheric Science (FCoE -ATM). The  ESA CCI  soil moisture data set v02. 2 has been made available by the  Vienna University of Technology  (TUW). The authors would also like to thank the  Geological Survey of Finland (GTK)  for making Quaternary deposits map data available through their data delivery portal as well as  Agrifood Research Finland (MTT)  for producing, and  Natural Resources Institute Finland  for providing a  Web Feature Service (WFS)  to the Finnish Soil Database 1 : 250 000 data.