dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimating the basic reproduction number R 0i We assumed that dengue is at endemic equilibrium and that the force of infection λ is constant in time in all cases except model C. Unless otherwise stated, we assumed that all four serotypes of dengue were in circulation. Since IgG data contain no information on the infecting serotype, we assumed that the four dengue serotypes are equally transmissible and estimated a single reproduction number applicable to each serotype. For the PRNT data, since we were able to estimate serotype-specific forces of infection, we computed strain-specific reproduction numbers as described by Ferguson et al. [40]. We computed the reproduction numbers under two different assumptions on the number of infections required to obtain full protection against infection by any dengue serotype.
Although recent serosurveys used commercial diagnostics, many studies used in-house assays. Tables  1  and 2  summarises the study and demographics of the datasets retained for analysis from the corresponding or closest year. All studies summarised in Table  1  were fitted using model A and B, and model C was also fitted to the two Nicaraguan datasets (Table  1 ). Models D1-D4 were fitted to studies summarised in Table  2. Only an overall force of infection could be estimated from non-serotype specific IgG data. As expected, estimates of the force of infection varied widely between countries and, to a lesser extent, within countries (Fig  2A ). Southeast Asian countries known to be hyper-endemic for dengue, such as Vietnam and Thailand, had a higher force of infection compared with most sites in the Americas  [75].
We estimated that exposure increased in individuals over 3. 9 years old (95% CI: 2. 7-5. 4 years), with the estimated force of infection during the study period (2001-2007) being 0. 323 (95% CI: 0. 261-0. 377) above 3. 9 years and 0. 174 (95% CI: 0. 118-0. 280) below 3. 9 years. These estimates represent the average total force of infection for all four serotypes in circulation. The force of infection was estimated to vary with a period of 8. 8 years (95% CI: 1. 3-12. 5 years). Resulting estimates of R 0i         PRNT data are serotype-specific, allowing us to estimate the force of infection (λ i ) and basic reproduction number (R 0i ) for each serotype individually  (Fig 4). Estimates varied widely between different surveys, again highlighting the heterogeneity of dengue transmission. Within the same survey, serotype-specific differences in transmission intensity were apparent, demonstrating how a certain serotype may be more dominant at any one time point.
For example, for model D2, force of infection estimates for Haiti were 0. 046 (95% CI: 0. 010-0. 179) for DENV-1 but 0. 219 (95% CI: 0. 088-0. 445) for DENV-4. Comparison of cross-protection or enhancement parameters under different assumptions allowed us to estimate the probable serotype causing primary and secondary infections. However, due to the wide credible interval of the estimated parameter, it is difficult to definitively determine the sequence of infections (Tables S5-S8 in S1 Text). For all datasets, the model fit improved when we assumed some level of inter-serotype interaction, demonstrating that interserotype interactions play an important role in dengue dynamics. Interestingly, the serotype-specific estimates of the reproduction number did not scale linearly with the estimated values of the force of infection, although the relative order is maintained i.
e. if λ 3 < λ 4 then R 03 < R 04. If one serotype dominates, as was the case in Haiti, changes in the force of infection of the other non-dominant serotypes marginally affect the estimates of the reproduction number of the non-dominating serotypes. In order to compare the estimates of dengue force of infection derived from IgG and PRNT assays, we also analysed the PRNT data ignoring strain-specificity (i. e. treating PRNT data as if it were IgG data), by categorising individuals as 'seronegative' if their PRNT titers were negative for all serotypes, or seropositive if they tested positive for at least one serotype. We used the same thresholds for seronegativity used by each source study. The resulting force of infection estimates generated using model A were consistent with the sum of the individual serotype-specific λ estimates obtained from the full PRNT datasets.
Discussion From a literature review, we selected 39 studies reporting age-structured seroprevalence data obtained with IgG/IE (31 out of 39) or PRNT (8 out of 39) assays in 22 different locations from 1980 to 2010. From each dataset, we estimated dengue transmission intensity, quantified by the force of infection (λ) and the basic reproduction numbers (R 0i ). Overall, our estimates highlight the highly heterogeneous nature of dengue transmission in both space and time, and by serotype. Our analysis also highlights how the relationship between the force of infection and R 0i is affected by underlying assumptions about serotype interactions and immunity. The majority of our estimates of R 0i from 22 countries ranged from 1-4 (28 out of 28 and 24 out of 28 from model A fitted to IgG datasets under assumption 1 and 2 respectively, and 6 out of 7 from model D2 fitted to the PRNT surveys).
However, multiple cross-sectional surveys or cohort studies would be needed to estimate how forces of infection by age have changed over time. The low R 0i estimated in the Indian subcontinent is probably due to the lack of datasets from this region and the spatial heterogeneity of transmission within that large region. The one serosurvey from India used in our study was conducted in Andaman, an island with a low population density where we estimated a very low force of infection. It is likely that the epidemiology of dengue on Andaman is not representative of dengue epidemiology on the mainland. Seroprevalence surveys have the benefit of not being affected by surveillance system sensitivity or case reporting rates, but still have several limitations (Box 1)  [77, 78]. A particular issue is the wide variation in the assays used between studies (Table  1 ).
Interview Face-to-face interviews were proposed to patients after the end of the adherence measurement period (6 cycles at most). Patients were randomly recruited for the interviews, sampling continuing until data saturation was achieved. Interviews were conducted face-to-face with a sociologist, lasted at least 1 h and investigated those topics : (i) the declared adherence behavior and perception (ii) the patient understanding of his treatment and its management (iii) the place of relatives in treatment management (iv) patient's adverse event declaration and management. See the Additional file 1 for more information on "Methods".
Statistical analysis Patients' answers were first analysed descriptively by frequencies (percentages) and medians (interquartiles). Then they were analysed with a multiple classification analysis (MCA) followed by an ascending hierarchical classification (AHC), which is an agglomerative hierarchical clustering procedure, to statistically and graphically identify patient clusters. Adherence categories identified with MEMS caps were projected onto a MCA cluster map to graphically identify adherence clusters. Patients without adherence measured with MEMS caps were considered as having missing data. Then categories of adherence were compared with the whole clusters tendency using univariate analyses. All analyses were performed using SAS (version 9. 1) with a significance threshold of p < 0. 05.
Results 38 patients were included in the OCTOquali study. 20 of them were followed by MEMS during all the treatment intake, until the first event occurred: either treatment discontinuation or the end of the 6 cycles. 16 patients were included after the 6 cycles period (with or without treatment), for the interviews, one by one, until data saturation was reached. 4 patients refused to conduct the interview (Figure  1 ). The main reason given for not completing the interview is not being available.
For the first time, we carry out an airborne gravity survey and we collect new land gravity data over the islands of Tahiti and Moorea in French Polynesia located in the South Pacific Ocean. The new land gravity data are registered with GPS-derived coordinates, network-adjusted and outlier-edited, resulting in a mean standard error of 17 μGal. A crossover analysis of the airborne gravity data indicates a mean gravity accuracy of 1. 7 mGal. New marine gravity around the two islands is derived from Geosat/GM, ERS-1/GM, Jason-1/GM, and Cryosat-2 altimeter data. A new 1-s digital topography model is constructed and is used to compute the topographic gravitational effects. To use EGM08 over Tahiti and Moorea, the optimal degree of spherical harmonic expansion is 1500. The fusion of the gravity datasets is made by the band-limited least-squares collocation, which best integrates datasets of different accuracies and spatial resolutions.
1a ), making a limited insight on the three-dimensional structure of the Tahiti volcanism. The insight can be improved by the new gravity datasets collected in this study. In the oceans around Tahiti, gravity-based geophysical investigations have been largely based on marine gravity fields from old-generation altimeter missions and shipborne measurements  (Calmant and Cazenave 1987; Sichoix and Bonneville 1996). These studies may be significantly improved by using the high-resolution and high-precision gravity from the new-generation altimeters Jason-1/GM and Cryosat-2  (Sandwell et al. 2014; Hwang and Chang 2014). In addition, there was no specific campaign to collect gravity data for modeling the geoid of Tahiti, which in the past is exclusively derived from global gravity models (GGMs) such as EGM08  (Pavlis et al.
In addition, the new land gravity survey occupied sites not only along the major routes across the islands but also the coastal routes around the islands (Fig. 1b ). The combination of gravity measurements from these two surveys leads to a completely new gravity dataset in Tahiti. In this paper, we will accomplish the following two objectives: (1) detailing the data acquisition and processing for the 2013 airborne and land gravity surveys, and (2) constructing the detailed gravity anomaly and geoid models of Tahiti by combination of all gravity data available around Tahiti. For the first objective, we show how a small aircraft and an air/sea gravimeter can be used to map the gravity field on islands such as Tahiti and how the resulting gravity field is compared with the existing one. For the second objective, we show how the method of band-limited least-squares collocation  (BL-LSC; Moritz 1978; Novák and Heck 2002)  is used to combine gravity datasets of different accuracies and spatial resolutions.
Land gravity data The gravity values at the two absolute gravity sites (Fig. 1a ) were determined in 2003 using a FG5 absolute gravimeter and the related information is shown in Table  1. With a Scintrex CG5 relative gravimeter, we collected relative gravity values at 461 sites, of which 387 are on the island of Tahiti and 74 on the island of Moorea (Fig. 1b ). The land gravity data were corrected for the effects of polar motion, air pressure (data from the Geodesy Observatory of the Tahiti Weather Station), and solid and ocean tides (the latter uses the GOTIC2 software with the NAO99 tide model,  Matsumoto et al. 2000; 2001). Afterwards, we carried out a network adjustment of the relative gravity values by holding fixed the gravity values at the two absolute gravity sites  (Hwang et al. 2002).
Marine gravity from satellite altimeters To achieve a best marine gravity field around Tahiti from satellite altimeters, we first improved the altimeter range measurements from the geodetic missions of Geosat, ERS-1, and Jason-1 by re-determining the gates of radar returns using the subwaveform retracker  (Yang et al. 2011). The altimeter data from Cryosat-2 were used without retracking. In general, the accuracy gain in coastal regions due to retracking is larger than the gain in the open oceans. The sea surface heights (SSHs) were then converted to along-track geoid gradients, which were used to form geoid gradients in the north and east directions on a 1′ × 1′ grid. Finally, we used the inverse Vening Meinesz formula  (Hwang 1998)  to compute marine gravity anomalies from the two gradient components.
The resulting marine gravity anomaly field (the area defined in Fig. 2 ) was assessed by the shipborne gravity measurements from the National Geophysical Data Center (NGDC; http://www. ngdc. noaa. gov/), resulting in a standard deviation of 6. 3 mGal in the differences. This level of gravity accuracy is the same as that of the latest gravity field by  Sandwell et al. (2014). In Fig. 2 , the land gravity anomalies were computed on a 1′ × 1′ grid from EGM08 to degree 2160 without contributions from the new airborne and land gravity datasets.
This assessment shows that the DTM is at the 1-m level accuracy. The new DTM shows that Tahiti is about 6500 m high,  counting from the mean seafloor depth of 4300 m  (Clouard et al. 2000)  to its highest peak, Mt. Orohena, whose elevation is 2241 m. To compute the RTM-derived gravity and geoid effects, the 1″ × 1″ grid was re-sampled onto a 9″ × 9″ grid. It turns out a 9″ × 9″ grid is sufficiently good for our geoid modeling and is regarded as the "true terrain" in all computations below. A smoothed terrain was obtained by applying the Gaussian filter to the 1″ × 1″ grid using an 18-km window size. A RTM was obtained by subtracting the smoothed topography from the true terrain. The technique used for computing the RTMderived gravity and geoid effects is detailed in  Hwang et al. (2003). In order to assess the accuracy of the geoid models in this paper, we collected GPS and leveling data at 48 benchmarks on the island of Tahiti and at 6 benchmarks on Moorea.
The ellipsoidal heights of the 48 benchmarks were determined by the Leica Geo Office software using GPS static observations on the benchmarks. The session length for GPS measurements is 24 h, resulting in a 1-cm accuracy in the ellipsoidal heights. The geodetic reference system (GRS) of Tahiti is the Réseau Géodésique de Polynésie Française (RGPF; Geodetic Network French Polynesia; Service Hydrographique et Océanographique de la Marine 1999). The RGPF is a local solution of the ITRF 92 geodetic system and then is also based onto the GRS80 ellipsoid. In conclusion, apart from a datuminduced bias in the orthometric heights, the geometric geoidal heights have a 1-cm accuracy.
Downward continuation of airborne gravity by bandlimited least-squares collocation For combination with different gravity datasets and for geoid modeling, the Tahiti airborne gravity data was downward continued to sea level. Downward continuation is an unstable process and will amplify short-wavelength noises in the data, requiring a low-pass filter to smooth the r; ψ; λ ð Þ¼ GM R 2 X lþb n¼2 n-1 ð Þ R E r nþ2 T n ψ; λ ð Þ ð2Þ where (ψ, λ) are geocentric latitude and longitude, GM denotes the product of the Newtonian constant and the earth's mass, T n is the surface spherical harmonic of degree n, l þ b is the resolvable degree corresponding to the resolvable wavelength of the airborne gravity, l is the degree of expansion of the reference GGM (the optimal case, l ¼ 1500, in Table  4 ), R E is the mean radius of the earth and r = R E + H is the radius of a sphere at a flight altitude H.
The band-limited gravity anomaly contains gravity signal components at wavelengths from degree 2 to l þ b. The relation between l þ b and the resolvable wavelength λ H of the airborne gravity data is  (Seeber 2003) : l þ b ¼ 2π R E þ H ð Þ λ H ð3Þ Because of the limited frequency contents of the Tahiti airborne gravity, we used BL-LSC  (Moritz 1978)  to carry out the downward continuation and the combination with the altimeter-derived gravity. Hwang et al. (2014)  has used BL-LSC to combine gravity data of different spatial resolutions and accuracies in Taiwan, which, like Tahiti, is an island surrounded by seas. In  Hwang et al. (2014) , the downward continuation (for the airborne gravity dataset) and merging of all gravity datasets were carried out in a one-step procedure. In order to assess the gravity contribution from each of the datasets in this paper, we used a sequential combination procedure described below, rather than the one-step procedure.
The short-wavelength components were computed from the residual land gravity anomaly and the RTM-derived gravity anomaly as Δg ∞ short ¼ C ∞ ΔgΔg C ∞ ΔgΔg þ D Δg land 0 0 C ∞ ΔgΔg þ D Δg RTM ! -1 δΔg land Δg RTM ! ð17Þ N ∞ short ¼ C ∞ NΔg C ∞ ΔgΔg þ D Δg land 0 0 C ∞ ΔgΔg þ D Δg RTM ! -1 δΔg land Δg RTM ! ð18Þ where vectors δΔg land and Δg RTM are the residual land and RTM-derived gravity anomalies, the former being Fig. 6  Free-air gravity anomaly models of Tahiti. The two models (see Table  7 ) are from: a EGM08 to degree 1500, airborne and altimeter gravity (1500+a+o). b All datasets (1500+a+o+t+l). The topographic contours are shown at a 500-m interval obtained by subtracting the medium wavelength gravity component (Eq. (  15 )) and long-wavelength gravity components (due to EGM08 to degree 1500) from the land gravity anomalies.
Results and discussion In this paper, we constructed four new gravity-related models for Tahiti: free-air gravity anomaly, Bouguer anomaly, gravimetric geoid, and hybrid geoid. They are all given on a 9″ × 9″ grid. Figure  6a  shows the free-air gravity anomaly model from the EGM model (to degree 1500), airborne and altimeter gravity, and Fig. 6b  shows the model from all datasets (airborne, altimeter, terrain, and land data). The difference between Fig. 6a  and 6b  highlights the contributions of RTM-derived and land gravity data, which contribute the high-frequency components of the gravity field. Figure  7  shows the hybrid geoid model. The gravimetric geoid map is close to the hybrid geoid map, so it is not shown here. Figure  8  shows the BA model. The statistics of the modeled values of free-air gravity anomaly, Bouguer anomaly, and hybrid geoid are summarized in Table  6.
After quality assessments, we will present a preliminary discussion on the scientific implications of these models. First, we assess the quality of the free-air gravity anomaly model. Using ground gravity measurements, we already assessed EGM08 to decide the optimal degree (i. e. , 1500) for use in Tahiti (Table  4 ). Here, we use the same gravity measurements to assess the free-air gravity anomaly models from different combinations of datasets, in comparison to the degree-1500 EGM08 gravity model. Table  7  shows the statistics of the differences between gravity values from ground measurements and those from different combinations of gravity datasets. The RMS difference drops when an additional dataset is incorporated into the previous model that does not use this dataset. For example, the RMS difference is reduced from 27.
However, along the coastal lines (at low elevations), there are also some large differences (see Fig. 10a  below), indicating that the RTM cannot adequately pick up the high-frequency gravity signals in certain parts of the coasts. The large gravity differences along the coasts can be reduced by adding the new land gravity dataset, which contains gravity measurements in the coastal areas. This reduction is evident by comparing the differences in Fig. 10a  (differences between gravity measurements and modeled values from the case of 1500+a+o+t) and Fig. 10b  (the case from 1500+a+o+t+l). Figure  10a  shows large gravity differences exceeding 10 mGal (absolute values) along a part of the south coastline of Tahiti-Nui and the northern flank of the isthmus linking Tahiti-Nui and Tahiti-Iti. The incorporation of land gravity values into the gravity model significantly reduces the gravity differences in these two areas (Fig.
10b ). In fact, adding the land gravity data in the model results in the least RMS difference of 0. 91 mGal (see the case for 1500+a+o +t+l in Table  7 ). These assessments suggest that future high-degree geopotential models will benefit from the new airborne and land gravity data collected in this paper for the Tahiti region. Next, we assess the quality of the gravimetric geoid model (the case for 1500+a+o+t+l in Table  8 ) and the hybrid geoid model (the case for 1500+a+o+t+l+n in Table  8 ) using 54 geometric geoidal heights. A similar    evolution of the differences is found for the geoid models that use different gravity datasets. Adding the airborne gravity data and the altimeter-derived gravity data reduces the RMS difference from 14. 0 (the degree-1500 EGM08 geoid model) to 10. 7 cm.
Conclusions In this study, we have updated the gravity field of Tahiti by adding datasets from a new airborne mission, a land survey campaign and a new result of altimeter data processing. The new land gravity data collected in 2013 have a mean accuracy of 17 μGal and are on average 17. 6 mGal larger than the values acquired in 1994. This 17. 6-mGal difference should be investigated to identify potential misinterpretations of the earlier gravity field in Tahiti. The new land and airborne gravity give the gravity highly associated with the volcanism of Moorea, which is not seen in the EGM08 gravity model (compare Figs. 2  and 7 ). As such, we recommend a new interpretation of the Moorea volcanism using the new gravity model constructed in this paper. Because the BL-LSC method can best integrate datasets of different accuracies and spatial resolutions, it was used for downward continuing the airborne gravity and for constructing the free-air gravity anomaly and hybrid geoid models of Tahiti.
Acknowledgements This study is supported by the  Service de l'Aménagement et de l'Urbanisme of French Polynesia  "Convention 2684/MAA/AU of June 1st, 2012",  MOST/Taiwan , under grant No. 103-2221-E-009-114-MY3  and  Ministry of the Interior/Taiwan , under project " Gravity Datum Service  ( 2014 ). "The comments from two anonymous reviewers greatly improved the quality of this paper.
2 and 3, relevant aspects of the SCIAMACHY and TANSO-FTS instruments are discussed. Section 4 gives a short overview of the SCIAMACHY BESD retrieval algorithm whereas in Sect. 5 the recently developed GOSAT BESD XCO 2 retrieval algorithm is introduced. This includes the GOSAT Level 1C generation (fully calibrated total intensity, measurement error and a priori information), the GOSAT Level 2 XCO 2 generation as well as the cloud filtering and post-processing. In Sects. 6 and 7 the comparison of the satellite XCO 2 data with TCCON and CarbonTracker are described and discussed. Finally, conclusions are given in Sect. 8.
8 mrad (∼ 10. 5 km diameter at nadir when projected to the ground), TANSO-FTS can measure ± 35 • across track and ± 20 • along track. The typically used scan time of one interferogram is 4 s. Between 4 April 2009 and 31 July 2010, the five-point across track mode was used, which yields footprints separated by ∼ 158 km across track and ∼ 152 km along track at the equator (e. g. Crisp et al. , 2012). In order to improve the pointing stability during the scans, on 1 August 2010 the observation mode was changed to a three-point across track mode with footprints separated by ∼ 263 km across track and ∼ 283 km along track at the equator. The TANSO-CAI instrument is a high spatial resolution imager detecting clouds and optically thick aerosol layers within the TANSO-FTS field of view. The TANSO-CAI data products are not used for the BESD algorithm.