dataesr/scientific-paragraphs-categorization huggingface dataset:
e. the mean and RMS of the density ratios are computed as the average per calendar year). The density ratios are also binned as a function of altitude, latitude, local time, month, and 81-day mean F10. 7/F30. Analysis of the mean and RMS of the ratios may give clues about specific modeling errors, or about data inconsistencies. Specific results are presented in Section 3. 2. The density datasets may be inconsistent due to errors and approximations in the satellite macro models, in the drag coefficients, and surface-to-mass ratios used in their derivation. This will cause the model to be biased with respect to those datasets, but the least-squares adjustment provides the leastbiased solution. This kind of inconsistency will disappear when a standard calculation for the drag force is adopted, which is not the topic of this paper, and all datasets reprocessed applying the standard.
Overall relative model precision The mean and RMS of the density ratios are computed per year for GOCE, CHAMP, GRACE, Stella, and Starlette, and as the average of the EDR density ratios in the 200-400 km altitude range (13 satellites) and in the 400-500 km range (five satellites). Figures  2  and 3  show the mean and RMS of the density ratios per year for DTM2009, JB2008, and DTM2013, respectively. For JB2008, the comparisons start in 1999 due to the unavailability of its indices earlier. It is evident from these figures that DTM2013 is the least biased model and that variations are best reproduced over the solar cycle and in the altitude range 250-800 km covered by these datasets. It is a clear improvement over DTM2009, especially at the lowest (GOCE) and highest altitudes (Stella and Starlette), which was the objective of ATMOP.
DTM2009 underestimates GOCE densities by about 10% (16% in 2013), whereas DTM2013 has negligible bias and an RMS of the density ratios that is at the 10% level for 2010-2013. Note that GOCE data in the interval June 2012 to October 2013 were not assimilated in DTM2013. For the only overlapping year of GOCE and EDR densities, 2010, the density ratios are nearly identical. The Stella and Starlette density ratios of DTM2009 are gradually decreasing from 1992 to 2009, and this is no longer the case of DTM2013. This hints at calibration issues most probably in F10. 7, or in F30, or in both. The Starlette density ratio with DTM2013 in 1993 appears to be affected by errors in F30. JB2008 is most biased at all altitudes, and its performance for 2009-2013 is significantly worse than for 1999-2008 (i.
e. the data assimilation period). The EDR data, not used in DTM but partially in JB2008, are reproduced with smaller bias (in particular during the solar minimum 2008-2009) and homogeneous RMS with the DTM models. A notable feature common to the three models is the dip in 2008-2009 during solar minimum, which can be explained by the very low solar UV emissions. The actual UV flux is overestimated with the F10. 7 proxy during the last solar minimum, and consequently models overestimate density  (Solomon et al. 2010 (Solomon et al. , 2011)). Since this effect is also seen DTM2013, F30 apparently also overestimated the UV flux during the last minimum, but less. The models also have large biases in 2010-2011 for CHAMP and GRACE (densities are underestimated). The EDR densities for both altitude ranges have a similar jump in the density ratio from 2009 to 2010, even if their biases are not as big in 2010.
The effect is largest for JB2008 and smallest for DTM2013, which suggests that the solar proxies are the cause, but this time they seem to underestimate UV flux. F10. 7 and F30 are rather different for 2010-2011 (Fig. 1 ) and consistent with the observed density ratios, i. e. F30 is larger than F10. 7. The correlation coefficients for the complete datasets are listed in Table  2. They are equal to or larger than 0. 93 for all datasets with DTM2013. The low correlations of JB2008 with the Stella and Starlette data are due to its model   algorithm, which does not calculate density as a sum of constituents. Helium mainly sits in the winter hemisphere, the so-called winter Helium bulge  (Keating & Prior 1968 ), but JB2008 predicts density maxima always in the summer hemisphere. The erroneous latitude structure causes the correlations to be small.
Latitude bins Figure  4  shows the CHAMP and GRACE density ratios in 20°l atitude bins, per year, for DTM2009 and DTM2013. Both models have small biases for most years (most profiles lie within the ±5% interval indicated by the dashed lines), without apparent latitudinal structure. The biases in 2010-2011 are large, but significantly smaller with DTM2013. The solar minimum year 2008 with CHAMP is also less biased with DTM2013, whereas the first year of GRACE (  2003 ) is more biased. Overall, improvement is made. DTM2009 already was less biased than JB2008, as can be seen in Figure  6  of  Bruinsma et al. (2012)  Figure  5  presents the density ratios in 20°latitude bins, but now for the complete datasets (i. e. CHAMP from 2001 to 2010, etc. ) of DTM2009, DTM2013, and JB2008. The scale is the same for the DTM models, but larger for JB2008.
Day-of-year bins The data are binned in day-of-year (1-365/366) per 10 days in Figure  7 , for DTM2009 and DTM2013. A seasonal error is not visible in the DTM2009 density ratios, or in DTM2013; however, all datasets seem to follow a similar variation in the second half of the year for the latter, in particular for days 270-370. The cause of such a 90-day variation is unknown. The DTM2013 density ratios remain well within the ±10% bias interval when all years are averaged. Figure  8  displays the CHAMP density ratios in day-of-year bins, but this time per year, in order to show an example of the increased errors when analyzing 10-day intervals directly. The fat black line is the all-year average, i. e. the same as in Figure  7.
81-day mean solar flux bins Figure  9  presents the density ratios in 81-day mean solar radio flux bins. The last bin of DTM2013 is 230-240 sfu due to the use of F30 (scaled to F10. 7) instead of F10. 7, while for DTM2009 and JB2008 it is 220-230 sfu. DTM2013 is overestimating density at low altitude (GOCE and CHAMP) for minimum solar cycle conditions; for high solar activity (larger than 190 sfu) the model is less accurate too. JB2008 is not handling the low activity data (less than about 100 sfu) well at any altitude; note that the density ratio scale is much larger. Binning in mean solar flux bins does not give unambiguous results because it is a mix of solar activity and seasonal effects. Especially for the highest and lowest solar activity the density ratios are often taken in a single year.
2 ). DTM2013 is the most precise for the datasets that were assimilated. For CHAMP, GRACE, and GOCE it performs significantly better than DTM2009 and JB2008. For the higher altitude Starlette and Stella data, DTM2013 performs significantly better too than DTM2009, notably before 2000. The external EDR data in both the 200-400 km and 400-500 km altitude intervals is equally more accurately reproduced with DTM2013 (the bias is 1% larger, but the RMS of the density ratios is smaller). JB2008 models the EDR densities more accurately than DTM2013 for the solar maximum years 1999-2002, but after that its accuracy decreases progressively over time. Taking all of the above into account, DTM2013 is the most accurate model overall and a significant improvement over DTM2009 under all conditions. It is more accurate than JB2008 at all altitudes.
Model improvements are still necessary, in particular below 300 km and above about 800 km, for which the complete density database is still rather sparse. New high-resolution data are however not foreseen in the immediate future, and certainly not above 800 km. Secondly, it is essential that the density datasets available at present are reprocessed according to a to-be-developed standard in order to make them consistent. That will already improve the model accuracy, because the adjustment of the model coefficients will then become more precise.
Acknowledgements. This study received funding from the  European Community 's  Seventh Framework Programme  ( FP7-SPACE-2010-1 ) under the Grant Agreement No. 261948  ( ATMOP  project, http://www. atmop. eu). Part of this work was performed in the framework of  ESA 's Support to  Science Element  ( STSE) study  '' GOCE+ Air density and wind retrieval using GOCE data ''. SLB is equally supported by  CNES/TOSCA. The editor thanks two anonymous reviewers for their assistance in evaluating this paper.
Background Since 2004, the French Institute for Public Health surveillance (InVS) has been in charge of monitoring vaccination coverage. This requires reactive tools to assess recent changes. This is particularly important in two types of situation: after a modification in the immunization schedule such as the introduction of a new vaccination or to assess the impact of an event that can impact coverage positively (e. g. , an immunization campaign) or negatively (e. g. , a vaccine scare). Although immunization registries are regarded as one of the best ways to assess vaccination coverage  [1] , this type of tool is not available in France, and vaccinations given in private clinics are not recorded in a central database. Vaccination coverage of infants is routinely estimated by analyzing the information on the immunization status recorded in the Child Health Certificate (CHC)  [2].
About 85% of childhood vaccinations are performed by the private sector. The vaccine is purchased at the pharmacy under a prescription and is administered by the physician. Most of the time, the vaccine is delivered free by the pharmacist who is directly reimbursed by the client's health insurance. The reimbursement, made to either the pharmacist or the client, is recorded, as is any other care act provided or product delivered, in the database of his/her health insurance. For children vaccinated in the public sector (about 15%), according to the place or the type of vaccine, the vaccine is either delivered free of charge by the Maternal and Child Health (MCH) program or prescribed to be bought in a private pharmacy. In the latter case, the same procedure as for vaccines prescribed by private practitioners applies for vaccine reimbursement and registration in the client's health insurance database.
The databases of the different plans are progressively merged into a single anonymous database, under the National Health Insurance Inter-scheme Information System (SNIIR-AM). It was created in 1999 in order to improve the knowledge on use of care and the costs of health care consumptions. It contains anonymous and comprehensive data on health spending reimbursements from 2004 and for the entire population covered by the health insurance system. The database includes a large amount of information, among which health care reimbursement data (including vaccine reimbursements), data on patient characteristics (gender, date of birth, place of residence, date of death, etc. ), information about health care workers, medical information (such as chronic disease categories), and hospital admissions data.
These data are stored in a data warehouse for a period covering the current year plus the 3 most recent years. Insured individuals who have had no health consumptions during this period of time are not included in the database. Up to 2012, access to this huge database was restricted to the CNAMTS. The need to monitor health care consumptions over more than 3-4 years and to give access to a manageable database to several public health institutions, including InVS, in 2005 led the CNAMTS to establish a sample of the SNIIR-AM, the Permanent Sample of Beneficiaries (EGB). At that time, the EGB was a representative sample of 1/97th of the health insurance beneficiaries covered by the GHI. It includes about 500,000 individuals. In the first step, all GHI beneficiaries with a strictly secret value of their National Identity Register (NIR) control key number (going from 1 to 97) are selected and included in the EGB whether they have consumed care or not.
In a second step, the health care consumptions of these selected individuals are searched in the SNIIR-AM database and individually linked to each beneficiary. Hence, the EGB database contains fully anonymized individual data of a random selection of GHI health beneficiaries, which will be followed for a period of at least 20 years. Health care consumptions have been integrated every month since 2004 for the GHI. The time between actual delivery of the vaccine by the pharmacist and availability of the information in the EGB is 1 month  [7, 8]. We have explored the added value of the EGB to complement classical sources of estimation of vaccine coverage. We present in this article the initial validation step conducted by comparing measles, mumps, and rubella (MMR) vaccine coverage between the EGB and the CHC.
Data analysis 2. 2. 1. Validation step with MMR vaccination data The main challenge posed by the use of reimbursement data to measure vaccine coverage in children was to account for vaccines delivered free of charge in MCH clinics for which no reimbursement information is available in the EGB. We therefore had to find a way to exclude children receiving vaccine from the MCH clinic stockpile from the denominators of our analysis. We hypothesized that children in the EGB with no pentavalent or hexavalent vaccine reimbursed in the 1st year of life were children followed in MCH clinics. These children were excluded from the denominators. To validate this hypothesis, we compared MMR vaccine coverage estimates produced by the analysis of the EGB under this hypothesis with estimates from CHC data.
Analysis for meningococcal, hepatitis B, and HPV vaccinations We analyzed data until 31 December 2013. We estimated vaccination coverage for children born between 2004 and 2011, which allowed vaccination coverage estimations for children up to the age of at least 24 months, even for the latest birth cohort. According to the French childhood immunization schedule, children are considered as correctly vaccinated for hepatitis B if they received three doses at 24 months of age. For the Men C vaccine, introduced in 2010, vaccination coverage was only computed for children born in 2009 or after, i. e. , aged at least 12 months at the time the vaccination was introduced. Current guidelines recommend one dose at 12 months of age. HPV vaccination has been recommended since 2007 for girls aged 14 years, with a catch-up for girls aged 15-23 years old who have had no sexual relationships or in the year following the initiation of their sexual activity at the latest  [11].
We estimated HPV vaccination coverage for young girls born between 1994 and 1998 at their 15th, 16th, and 17th birthday. We were unable to estimate vaccination coverage after the age of 17 years of age because at this age, many girls become students and are no longer registered in the GHI. HPV coverage was estimated for young girls present in the EGB between 11 July 2007 (date of reimbursement of HPV vaccine) and 31 December 2013. Three doses given at least a 6 months between the first and last dose are required for complete vaccination. For each selected individual and vaccine, we extracted his or her vaccine history (number of doses reimbursed and age at delivery). The vaccination coverage, at a given age, was calculated by dividing, for each vaccine and each dose (in case a series of doses is needed for full vaccination), the number of vaccines reimbursed by that age, by the number of eligible subjects of that age in the EGB database.
The 56. 4% vaccination coverage for Men C for children born in 2011 at 24 months of age is close to the coverage found in an internet-based study, which showed a 32. 3% coverage between 12 and 23 months of age and a 57. 3% coverage between 24 and 35 months of age  [17]. Both estimations show insufficient coverage in this age group. Regarding HPV vaccination, our analysis showed a very disappointing coverage with only 20. 1% of the targeted girls being vaccinated at 16 years of age in 2013. Our estimates are consistent with a survey in which 23. 6% of girls aged 15 years in 2012 were fully immunized against HPV  [18]. However, the use of EGB for the assessment of vaccination coverage presents several limitations. First, some vaccinations dispensed very early in life are not recorded in the database.
Age in mon ths Fig. 1. Cumulative vaccination coverage for one dose of hepatitis B vaccine according to age and year of birth. France. ……………20 04 ……………20 05 ……………20 06 ……………20 07 ……………20 08 ……………20 09 ……………20 10 ……………20 11 ……………20 12 ……………20 13 estimation was lower by the former (61. 2%) than by the latter (78. 1%). Our data showed that, on the one hand, the average age at the first dose was 2. 2 months in children who received at least three hepatitis B vaccine reimbursements and 4. 5 months (close to the age of the second dose) in children who received only two reimbursements. Furthermore, 39. 9% of second doses in children who received only two reimbursements were delivered between 15 and 19 months of age, which is the age of the third dose. This strongly suggests that, in some children, the first dose recorded in the database is the actual second dose and the second dose is the third one.
This is very likely the consequence of the late registration of infants in the SNIIR-AM, which failed to capture the reimbursement of the first dose scheduled at 2 months of age. Contacts with the CNAMTS, which manages the EGB database, have confirmed our finding of uncompleted recording of care given in the very first weeks of life. Another potential contributing explanation would be that some of these infants received the first dose in an MCH clinic, free of charge, and the subsequent ones in the private sector. Second, this study focused on the beneficiaries of the GHI, which covers only 77% of the population, and no data exist comparing the prevention attitudes of the population according to their insurance coverage. This limitation will soon be overcome in 2011, when the EGB database is loaded with the beneficiaries of the two other important health insurance plans, MSA and RSI.
The data pertaining to the special plans covering students and civil servants (SLM) are currently being added, which will expand the EGB coverage well above 95% of the population in the very near future. More importantly, in 2012, InVS was granted access to the comprehensive SNIIR-AM database of reimbursement for all medical care of all of France's 64 million residents. Besides covering the whole population, this comprehensive database will allow the analysis of data at the subnational level (region, district, and even subdistrict). This type of analysis, which is impossible through the EGB due to an insufficient sample size, will allow identifying pockets of undervaccinated populations at a small geographical scale and planning complementary vaccination activities targeted to those specific subpopulations.
The relevance and feasibility of using this huge database for vaccine coverage monitoring is currently being explored by InVS, as part of its mandate of vaccine coverage monitoring in France. A third limitation of the EGB is that it only covers persons for whom the GHI pays for the vaccines. The inclusion in the denominator of children vaccinated in the public sector with free delivery of the vaccine would tend to underestimate the coverage. We therefore restricted the analysis to children for whom at least one dose of pentavalent or hexavalent vaccine had been reimbursed during the 1st year of life. We therefore excluded from the calculation fully unvaccinated children. We believe the resulting bias to be very low given that these combined vaccines include mandatory diphtheria, tetanus, and polio vaccinations for which coverage is estimated to be above 98%  [12].
7%, higher than the health certificate estimation (90. 6%), whereas exclusion from the denominator of children with no pentavalent or hexavalent vaccine reimbursement yielded a vaccine coverage of 91. 4%, much closer to the estimation based on health certificates. Last, the use of a reimbursement database is unsuitable for the analysis of vaccination coverage in adults, because many vaccinations are provided outside the private sector (by occupational health services, public clinics, or travel medicine clinics) without registration in the reimbursement databases. In summary, the analysis made from the EGB cannot yet fully estimate coverage of all children because it does not include all health insurance plans or the small proportion of children for whom vaccines are offered free of charge in MCH clinics.
There are no data to support these hypotheses or to estimate the frequency of such situations, but we believe that they are very rare. Finally, there may be a delay of several weeks before the purchase and the actual administration of the vaccine, but this should not affect the results as long as this delay does not vary over time. In conclusion, the EGB, and in the near future the whole SNIIR-AM database, are novel and useful tools for routine monitoring of vaccination coverage for vaccination in children and adolescents, particularly because of its high reactivity after changes in the vaccination schedule. It provides a rapid and reliable estimate of vaccine coverage for new vaccines or after a change in vaccine recommendation or implementation. Most of the limitations underlined above should be overcome or greatly reduced in the coming years.
Serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs) enables high-resolution protein structure determination using micrometre-sized crystals at room temperature with minimal effects from radiation damage. SFX requires a steady supply of microcrystals intersecting the XFEL beam at random orientations. An LCP-SFX method has recently been introduced in which microcrystals of membrane proteins are grown and delivered for SFX data collection inside a gel-like membrane-mimetic matrix, known as lipidic cubic phase (LCP), using a special LCP microextrusion injector. Here, it is demonstrated that LCP can also be used as a suitable carrier medium for microcrystals of soluble proteins, enabling a dramatic reduction in the amount of crystallized protein required for data collection compared with crystals delivered by liquid injectors.
, 2014) , corresponding to an average hit rate of 40%. In the case of PC, a total of 287 520 images were collected within 40 min, of which 18 794 were identified as crystal hits (average hit rate of 6. 5%). The peak-detection parameters and the experimental geometry were optimized to ensure the best quality of peak finding and indexing. Autoindexing and structure-factor integration of the crystal hits was performed using CrystFEL  (White et al. , 2012) , which involved the application of fast Fourier transform (FFT)-based autoindexing algorithms, MOSFLM  (Leslie, 2006) , DirAx (Duisenberg, 1992) and XDS  (Kabsch, 2010)  followed by averaging and integration of Bragg peaks using a Monte Carlo algorithm. The final statistics of the quality of the data sets are summarized in Tables  1  and 2.
The maximal radiation dose per crystal was estimated using RADDOSE  (Paithankar et al. , 2009). (I) values were estimated as the standard deviations of the means of the intensity measurements  (White et al. , 2012). The appropriate resolution cutoff was based on the behavior of the Pearson correlation coefficient CC 1/2 versus resolution, and on the improvements in the R work / R free values after including higher resolution shells in refinement  (Karplus & Diederichs, 2012). After integration with CrystFEL, the initial phases were obtained by molecular replacement using known structures of the protein from the PDB (PDB entries 4et8 for lysozyme and 3l0f for PC; Boutet et al. , 2012; R. Fromme, D. Brune & P. Fromme, unpublished work) and the structures were refined using phenix. refine (Afonine et al.