dataesr/scientific-paragraphs-categorization huggingface dataset:
Reviews of Geophysics Another source of error is a seasonal variation of polar tides inferred by the seasonally varying ice coverage. In Arctic regions this effect causes changes in tidal amplitudes of O (1-10 cm). Since the modern tide models represent an annual mean state but most of the observation do not account for the observed seasonality, this implies some inconsistency in this modelobservation evaluation. Because the Arctic tide gauge data set has not undergone extensive quality control, we edited the data prior to the model evaluation. This was performed on the reduced 60 tide gauge set common to the five models HAM12, FES12, EOT11a, DTU10, and TPXO8; in cases where more than three models showed RSS differences for the four largest constituents greater than 15 cm with respect to the gauge data, the station was removed from the analysis.
Only Sun-synchronous satellites (ERS-1, ERS-2, and Envisat), for which the S 2 is phase locked and aliases into a constant, have been available in the Arctic. Similarly, K 1 and P 1 are aliased into the annual signal. Tide models were also tested against three bottom pressure time series in the Arctic close to the geographic North Pole: ABPR1 at 89. 2543  a The average amplitude (in cm) for each tidal constituents of the Arctic is listed. The values are given for the largest common set of tide gauges to five of the models. b TPXO8 model assimilates a subset of the selected in situ measurements. 178. 23 • E. For each bottom pressure gauge, more than 1 year of data was used to derive the tidal constituents. Since the tidal signal is relatively small at the North Pole (M 2 amplitude varies between 5.
Antarctic Seas A total of 102 in situ measurements are available from seas surrounding Antarctica. Data sources include bottom pressure recorders; coastal tide gauges; and GPS, gravimeter, and tiltmeter measurements on the floating ice shelves  [King and Padman, 2005; King et al. , 2011a King et al. , , 2011b]]. All derived tidal constituents, along with information about record length, measure type, and references are included in the Antarctic Tide Gauge Database (http://www. esr. org/antarctic_tg_index. html). The gravimeter and tiltmeter records are expected to be less reliable, generally being based on shorter and older records and a greater number of uncertain corrections used in the reduction. Unfortunately, the gravimeter records make up a large fraction of the Ross Ice Shelf records.
Because OSU12 is not available on the ice shelves, it is excluded from this comparison. The RMS differences for the full set of common gauges are 3-4 cm, 2-5 cm, 2-6 cm, and 1-5 cm for the M 2 , S 2 , K 1 , and O 1 constituents, respectively. Most models have large RSS on the Larsen C and Filchner-Ronne ice shelves in the Weddell Sea. TPXO8 exhibits the lowest RMS for most constituents and clearly the lowest overall RSS, consistent with its assimilation of many of these in situ data records. Table 7 also shows the average constituent amplitude over the tide gauges. In contrast to the Arctic Ocean, amplitudes of the K 1 and O 1 constituents around sections of Antarctica can be large; the four major constituents, M 2 , S 2 , K 1 , and O 1 display roughly similar amplitudes around Antarctica (although this assessment is influenced by the irregular distribution of the in situ data).
Satellite Laser Ranging Tests The impact of individual tide models on satellite orbit computations was tested against precise SLR tracking data from four geodetic satellites orbiting at a variety of altitudes and inclinations: Lageos, Starlette, Stella, and Larets. All four satellites are heavy, "cannonball, " geodetic satellites covered with cube corner reflectors. For all four satellites we undertook a consistent set of analyses based on 261 seven-day orbital arcs over the period 2004-2008. Many SLR analyses estimate empirical acceleration adjustments at resonant frequencies (e. g. , once per revolution) to accommodate force model errors; however, these kinds of adjustments potentially dilute the sensitivity to tidal perturbations in the orbit, so we here avoided such adjustments. The metric of this test is to identify the tide model that produces orbits with the smallest SLR tracking residuals.
Because SLR tracking is not continuous in time but rather relies on occasional satellite overpasses of the laser observatories, the SLR data are sensitive essentially to only long-period perturbations in satellite orbits. These perturbations depend on the orbital elements of a satellite, so each satellite is more sensitive to some tidal constituents than others. We therefore should not expect results to be completely consistent among the four satellite tests. For example, Starlette, specially designed for sensing tidal perturbations, is most sensitive to K 1 , and then (in order) S 2 , M 2 , and P 1 , with the K 1 perturbations having periods near 90 days  [Williamson and Marsh, 1985]. Lageos is also sensitive to K 1 , K 2 , and S 2 , but the K 1 periodicities are near 3 years and therefore potentially confounded by nontidal mass variations  [Lambeck, 1988; Tables 6.
8 ]. Lageos is also at far higher altitude than the others and is therefore sensitive to only the very lowest degree gravitational terms. Both Stella and Larets are in near-Sun-synchronous orbits. Table  8  presents the mean RMS residuals in SLR tracking data for seven tested tide models, all of them modern altimetric-based models except-to assess historical progress-the old NSWC  [Schwiderski, 1979]  model. Each entry in the table gives the mean RMS over the 261 arcs, with the quoted uncertainties being the standard errors in those means. For each satellite the NSWC model clearly, and reassuringly, yields the largest SLR residuals. As an overall assessment of the SLR tests, Table  8  suggests that models EOT11a, HAM12, and GOT4. 8 all perform well for all four satellites. TPXO8 is especially good for Starlette but appears slightly less accurate for the two Sun-synchronous satellites.
GRACE Tests Satellite-to-satellite ranging data in GRACE are continuous in time, so (unlike the SLR test) GRACE data are sensitive to both short-period and long-period perturbations in the orbits. For a given mass anomaly of sufficient size, GRACE perturbations can be localized over that mass, which of course is the source of its great value for monitoring mass changes  [Tapley et al. , 2004]. Our GRACE tests are based on the semiqualitative approach described by  Ray et al. [2009]. Using similar, but more complete, background models as described above for the SLR tests, with higher-degree tide expansions, we have computed GRACE range rate residuals for the 7 year period 2004-2010. The processing of the range rate data follows steps described by  Luthcke et al. [2006]. The entire processing was repeated for each tested tide model.
However, the approach does allow consistent testing of the major constituents with identical software. In addition to the tide corrections the altimetric data were processed with standard sets of media, instrument, and geophysical corrections. These include a dynamic atmospheric loading correction via the MoG2D (2-D Gravity Wave Model)  [Carrère and Lyard, 2003 ]. An equilibrium model was used to correct for long-period tides, and the model of  Matsumoto et al. [2000]  was used for all load tides. Further detailed comparison methodologies are described by  Fok [2012]. The satellite altimeter data used for these tests include Topex/Poseidon (10 day cycles 4-364), Topex Interleaved Mission (cycles 369-481), Jason-1 (cycles 1-259), Jason-1 Interleaved Mission (cycles 262-374), Jason-2 (cycles 1-184), and the Jason-1 Geodetic Mission (cycles 382-409); GFO (17 day cycles 37-223), Envisat (35 day cycles 10-94), and the Cryosat-2 (29 day cycles 1-40).
Reviews of Geophysics 10. 1002/2014RG000450 employed in the development of the models but not all. For example, all models are independent of the Jason-1 geodetic mission data and all are independent of Cryosat-2 data. All the altimeter data used in this section were extracted from the Radar Altimeter Database System  [Scharroo, 2008]. The overall statistics for the altimetry sea level anomaly tests as a function of ocean tide models are summarized in Tables  10  and 11  for shallow (< 1000 m water depth) and deep-ocean areas, respectively. One immediately notices that differences in variance among the different missions is generally greater than the differences among models within a mission. This likely arises because the various missions sampled the oceans at different times, and it also may reflect differences in data quality.
The table entries suggest that the forward models HIM and OTIS-ERB are the most accurate in deep water, with OTIS-GN not far behind; OTIS-GN is the most accurate in shallow water, with HIM, STM-1B, and OTIS-ERB somewhat less accurate and the others trailing. It is interesting to observe that some of the forward models are now starting to reach accuracies comparable to those of the pre-Topex era assimilative NSWC model of  Schwiderski [1979] , which from the late 1970s to early 1990s was generally considered the most accurate tidal model available. The RMS differences between NSWC and TPXO8 are 4. 41 cm in deep water and 17. 4 cm in shallow water, which is only slightly better than some of the entries in Table  12. For models that lack any data constraints other than bathymetry, to approach the NSWC accuracies does mark substantial progress in modeling.
New GPS records floating Antarctic ice shelves in the Weddell Sea have recently been obtained  [King al. , 2011a [King al. , , 2011b]] ; comparable records over the Ross Ice Shelf would be useful to resolve uncertainties around old gravimeter sites. We expect that new satellite data will be invaluable additions for polar regions. ICESat laser altimeter data, although limited by its short time series, were useful in the southernmost Antarctic ice shelves  [Ray, 2008; Padman et al. , 2008]. Existing and future CryoSat-2, HY-2A, Sentinel-3, and ICESat-2 data will lengthen the time series and should considerably strengthen tidal inversions. Planned wide-swath altimeter missions are expected to overfly some polar seas, which will be beneficial; in addition, the high spatial resolution of such altimetry will help resolve short-scale tides in all shallow seas.
Methods We collected data of adult patients with rectal/pararectal GISTs treated from November 1991 to March 2011 in 12 French Sarcoma Group institutions and the Centre Hospitalier Universitaire Vaudois (CHUV) Lausanne. A standard data file was created to retrieve information on patient characteristics (gender, age at diagnosis, Eastern Cooperative Oncology Group [ECOG] performance status, and initial clinical symptoms), clinicopathological tumor characteristics, treatment approaches including surgical management and medical treatment (neoadjuvant and/or adjuvant imatinib), and patterns of failure (local recurrence or distant metastasis). Follow-up information was obtained during outpatient visits. The diagnosis of GIST was confirmed by an experienced local pathologist using morphology and immunohistochemical staining for KIT (CD117) and CD34.
8 mm/yr reveals a clear picture of the interseismic deformation along the NAF. The deformation field shows a gradual change in velocity across the fault and is consistent with the right-lateral sense of motion between the Eurasian and Anatolian Plates as warm colors indicate movement away from the satellite (i. e. , westerly) and cool colors toward (i. e. , easterly) the satellites which are flying in descending (i. e. , heading southward) orbits. The InSAR velocity fields on all the tracks are in close agreement with GPS velocities obtained from the reprocessing of raw data from  Reilinger et al. [2006] , Yavaşoğlu et al. [2011], and  Tatar et al. [2012]  with the GAMIT/GLOBK GPS software  [Herring et al. , 2010]  (Figure  3 ). Profiles of faultparallel velocities plotted in Figure  4  together with surface velocities predicted by elastic screw dislocation models better illustrate the excellent agreement between the InSAR and GPS measurements.
05°E and 37. 85°E step over to the north with respect to the 1939 surface ruptures (Figure  6 ). From east to west, the modeled fault follows the 1939 rupture for about 70 km. However, west of Koyulhisar, the modeled fault deviates from the fault trace and joins with the 1942 rupture at Niksar after running fairly straight for about another 70 km (Figure  6b ). Therefore, the along-strike complexity of the NAF that apparently controlled the 1939 and 1942 ruptures disappears at ©2014. American Geophysical Union. All Rights Reserved. depth, an inference being supported by two independent InSAR data sets on two different overlapping tracks (i. e. , T350 and T78). The shear zone is highly localized and aligns to first order with the NAF, which suggests most of the deformation concentrates on the block boundary  [Cavalie and Jónsson, 2014].
Acknowledgments The NephroTest study group collaborators also include  P. Houillier ,  E. Letavernier ,  P. Urena ,  G. Maruani ,  M. Vallet ,  J. P. H. Rougier ,  E. Rondeau ,  P. Ronco ,  E. Plaisier ,  H. Fessi ,  C. Descamps ,  R. de La Faille ,  S. Dautheville ,  E. Daugas ,  C. d Auzac ,  M. A. Costa  and  J. Bouet.
General Study Design and Available Data Decrease in fertility was quantified for cows in reported case herds and cows in non-reported herds located in the 2007 outbreak area (herds with uncertain infectious status). These quantifications were performed using cows in herds unexposed to BTV-8 in 2007 as reference population. Information about exposure of herds to BTV-8 during 2007 was obtained from the official veterinary surveillance system. Herds were reported in 2 distinct situations: (i) in the event of clinical signs detected by the farmer or the veterinary practitioner, subsequently confirmed by a diagnostic test and (ii) in the event of a positive serological test performed either before animal transfer or sale, or in sentinel herds. Among herds reported during 2007, only herds with a confirmed detection reported after clinical suspicion (situation (i)) were included as the date of a positive serological test did not necessarily identify the possible date of BTV-8 exposure of the herd.
Information about BTV-8 exposure was available at the herd level only. Thus, a herd was considered exposed if at least 1 animal with clinical signs had tested positive for BTV-8. The proportion of infected animals in reported herds was unknown. Herds reported after clinical suspicion and  subsequently selected for this study will be referred to as 'case herds'. Case herds located in 23 departments were selected (n = 8,494), corresponding to 96% of all cattle herds reported due to clinical suspicion during the 2007 epizootic. Cattle herds that were not reported during 2007 and located in these departments were considered to have an uncertain infectious status, and were thus referred as 'herds with uncertain BTV-8 status' (n = 46,569). The performance data were obtained in dairy herds enrolled in the official Milk Recording Scheme and where artificial insemination was used.
For each cow, obtained data were dates of AIs, rank of service (number of AI within lactation), date of culling (if it had occurred during the study period) and data used to adjust for factors known to influence fertility: cow and bull breeds, lactation number, calving date, milk production data at each milk record (record date, milk yield, protein content, fat content). After selection of dairy herds that use AI for reproduction, populations of case herds and herds with uncertain BTV-8 status were composed of 4,392 and 13,804 herds respectively. A date of exposure to BTV-8 was either estimated from recorded data for cows in case herds, or interpolated for cows in herds with uncertain BTV-8 status. This interpolation was based on the spatio-temporal dynamics of detection of confirmed case herds that reported clinical signs in 2007.
Estimated Date of Exposure for Reported Case Herds For each case herd, available data included the date at which clinical signs of disease were first suspected and the date at which disease was confirmed via diagnostic tests. The estimated date of exposure for reported herds was defined as the recorded date of suspicion which corresponded to the first detection of clinical signs in the herd. The same date of exposure was assigned to all cows in a herd. For 6. 1% of the case herds, the date of clinical suspicion was missing but the date of confirmation by a diagnostic test was known. In order to assign a date of suspicion, an imputation method based on the distribution of the time interval between dates of suspicion and confirmation was applied (values selected at random around a median interval of 4 days).
Interpolation of a Date of Detection of Clinical Signs for Herds Not Reported and Located in Exposed Areas A date of exposure to BTV-8 for each cattle herd with uncertain BTV-8 status during 2007 was interpolated. Kriging, a geostatistical interpolation method, was used to estimate a date of detection of clinical signs for herds with uncertain BTV-8 status. Dates were expressed as the number of days since the first case herd reported in 2007. Kriging uses data sample (cattle case herds) to predict values at unsampled locations (herds with uncertain BTV-8 status). All the cattle herds (dairy and beef) were included because they could have all played a role in the epizootic wave diffusion. Both beef and dairy herds are exposed to Culicoı ¨des. Densities of beef and dairy herds widely vary among French areas.
Fertility Parameter and Data Selection Fertility was assessed by the occurrence of a repeat AI after the first AI. In case of a return-to-oestrus after the first AI in a lactation, most farmers are likely to decide to reinseminate the cow. In such a context, the occurrence of a repeat AI (return-toservice) after a first AI can be used as a proxy for infertility. The criteria used to quantify effect of BTV-8 exposure on fertility was the 90-d-return-to-service, defined as a return-to-service occurring between 18 and 90 d after AI (binary variable). It was assumed that BTV-8 exposure could have 3 possible effects on fertility:  conception failure, embryonic death and fetal death in early gestation, all resulting in the same outcome. Cows with nonplausible or extreme data (checked for consistency of calving to first AI interval, AI to calving interval, interval between AI, interval between 2 test-days, peak milk yield and minimum Protein:Fat ratio) and herds with unusual management (unusual demographic structure, delayed first service or very small herds, low rate of IA indicative of the use of a bull) were excluded.
The random effect term made it possible to adjust for clustering within the data using frailty model. The effects in percentage points of return rate were calculated from estimated hazard ratio (HR). All statistical analyzes were performed by using R software  [11] , Cox models were performed using the survival package (Therneau T, 2008. A package for survival analysis in S) and kriging map using gstat package (Pebesma, EJ, 2004. Multivariable geostatistics in S).
Supporting information Crystallographic data for the structure reported in this paper have been deposited at the Cambridge Crystallographic Data Centre (CCDC), No. CCDC 789750 for 1. Copies of the data may be obtained free of charge from the Director, CCDC, 12 Union Road, Cambridge CB2 1EZ, UK (Fax: +44 1223 336 033; e-mail: deposit@ccdc. cam. ac. uk or www: http://www. ccdc. cam. ac. uk).
Reciprocal Best Hits BLAST A variation of the BLAST  [21]  reciprocal-best-hit method was used to identify orthologous CDSs (coding sequences) between two genomes. With the standard BLAST reciprocal-best-hit method two CDSs, C1 and C2 (from genome G1 and G2 respectively) are considered to be orthologous if and only if C2 is the best BLAST hit when G1 is used as the query sequence and all CDSs in G2 are used as the database, and (b) C1 is the best BLAST hit when G2 is used as query sequence and all CDS of G1 are used as the database. We modified the method by extending best hit to best hits while establishing threshold 1 and threshold 2. Threshold 1 says one hit can be grouped as one of the best hits if and only if the bit-score of the hit is equal or smaller than e. g. 80% of the Table  1.
Strain Accession number Host Location E1 CAMD00000000 (WGS_scaffold) CAMD01000001-CAMD01000212 (contigs) Human Austria maximal bit-score. Threshold 2 is analogous to threshold 1, except that it refers to the length of the query CDS and is defined as the percentage of the query sequence. Threshold 1 and 2 were set to 0. 8 BLAST was used with the default parameters and an E-value cut-off of 10 25 and the filter-option was set to false. The BLAST release 2. 2. 22 was locally installed. The BLAST reciprocal best hits method was implemented in Java. The results of the reciprocal best hits BLAST were stored in a MySQL database. That method is named reciprocal best hits BLAST. In our dataset the genome sequences of six C. difficile genomes were not annotated (see Table  2 ). To use these strains we assigned functions to ORFs (open reading frames) by using the reciprocal best hits BLAST.
First, ORFs (open reading frames) were predicted in all six genomes with the software GLIMMER  [22] [23] [24]. In total, three reference genomes (C. difficile R20291, C. difficile CD196 and C. difficile 630) were used for each annotated genome (Table  2 ). Orthologous sequences were computed with reciprocal best hits BLAST for all predicted ORFs of each organism and all CDSs of each reference genome like described above. The reciprocal best hits BLAST was computed using translated nucleotide BLAST (tblastx) with default parameters, expect an E-value cut-off of 10 25 and the filter-option was set to false, threshold 1 and 2 were set to 0. 8. To determine the core genomes of any set of C. difficile strains, orthologous CDSs were identified between all CDSs in the set, like described before. If one CDS had an orthologous CDS in every strain of the set the CDS was assigned to the core genome.
Distance Matrices Fourteen gene sequences encoding highly conserved proteins () were used for distance matrices computations. The respective protein primary structures are commonly used besides the rRNA sequences as markers in comprehensive phylogenetic studies of the organisms  [25]. The nucleotide sequences of the 14 gene sequences from strain CD630 were download from the UniProt database  [26]. To detect the corresponding gene sequences in the remaining C. difficile strains, nucleotide sequences comparisons with BLAST were performed using as query the nucleotide sequences of the 14 CD630 genes and as database all whole genome sequence of all analysed C. difficile strains in the study. For each of the 14 genes, the group of detected nucleotide sequences was aligned using the ARB program  [27]  using processed seed databases as templates.
Distance matrices-based dendrograms were obtained applying the neighbor joining method implemented in ARB. The distance matrices contain always the normalized hamming distances. The normalized hamming distance between two sequences is the number of different characters divided by the number of comparisons. To estimate the root of the dendrograms, the corresponding conserved genes from close relatives of C. difficile were included. GG770744-GG770776 (WGS_scaffold) ADVM01000001-ADVM01000100 (contigs) 078 V NAP08 GG770710-GG770733 (WGS_scaffold) ADNX01000001-ADNX01000111 (contigs) 078 V CD002 CAMG00000000 (WGS_scaffold) CAMG01000001-CAMG01000071 (contigs) 002 0 Strains marked with an asterisk are not annotated. The ribotypes of these strains were calculated using their GenBank data. doi:10. 1371/journal.