dataesr/scientific-paragraphs-categorization huggingface dataset:
). Cluster 6 includes genes HSF2 and PKIB for all PSs, and the analysis of PS1 and PS3 revealed an additional significant SNP located at SERINC1. The last cluster (no. 7) includes two SNPs, one located in a FKBP6 intron and the other located 0. 12 kb apart from NSUN5 and 3. 55 kb from TRIM50. The abovementioned 14 genes that include or are close (<50 kb) to SNPs that present signals of positive selection in the sliding-windows approach for all four PSs are described in Table  2. Five of the seven clusters showing signs of positive selection with the HGDP data sets also show signals of positive selection in the supplementary analysis. The exceptions are cluster 3 on chromosome 4, which was not covered by any SNP in the smaller data set, and cluster 7 on chromosome 7, for which the signal of positive selection disappears after replacing Pima by Zapotec.
In the supplementary analysis, no additional clusters with signal of positive selection were found in all SPSs. The type of natural selection-convergent evolution or selection in a single continent-inferred from the supplementary data set is generally concordant with the analysis done on HGDP data, except for cluster 6 on chromosome 6, where selection in Africa is more important in the supplementary analysis. After performing the sliding-window approach, cluster 1 appears for SPS1 and SPS3, clusters 4 and 5 for all SPSs, and cluster 6 for SPS2 and SPS4. Fifteen genomic regions had been found to be associated with the pygmy phenotype by means of covariation between allele frequencies and body height in Africa in another study  [35]. We found signals of positive selection in the African continent in four of these regions.
Discussion Tropical forests are believed to be very harsh habitats for human beings  [3]. In addition to being almost deprived from energy-rich food and edible plants  [2] , these environments are very propitious for the development of diseases  [4]  and might also compromise thermoregulation  [6]. In this work we used a Bayesian method  [20, 21]  to identify SNPs in a genome-wide dataset showing overly large or low extent of differentiation between tropical and non-tropical forest populations. We sought to infer signals of convergent evolution by comparing native populations from tropical forests (Biaka, Mbuti, Karitiana, and Surui) with genetically related populations living elsewhere (Mandenka, Yoruba, and Pima) combined in four different population sets (PSs). The analysis suggested some SNPs, genes, and a few biological pathways in which convergence could be inferred.
CCL28 5 Modulate immunity to viral infection  [49]  and skin-related inflammatory diseases  [50]. NNT 5 Produces high concentrations of NADPH at mitochondria and the resulting energy is used for biosynthesis and in free-radical detoxification  [51]. HSF2 6 Involved in the activation of heat-shock response genes under conditions of heat  [52]. PKIB 6 Associated to the aggressive phenotype of prostate cancer  [53]. SERINC1 6 Unknown. FKBP6 7 May play a role in modifying the susceptibility to idiopathic spermatogenic impairment  [54]. NSUN5 7 Deleted in Williams-Beuren syndrome (vascular system and calcium metabolism problems)  [55]. TRIM50 7 May be involved in the Williams-Beuren syndrome  [56]. doi:10. 1371/journal. pone. 0121557. t002 regions are the main focus of our discussion. For five out of the six clusters with sufficient SNP coverage in the smaller supplementary dataset, we observed significant SNPs with the same type of selection-convergence or selection in one continent-as observed in the analysis of the HGDP data.
The exception is cluster 7 where the signal of convergent adaptation does not hold when we replace Pima by Zapotec. In this case, selection in Africa is the best-supported model. Based on the agreement between results generated with the two datasets, results for clusters 1, 2, and 4-6 should thus be considered as very robust, but results obtained for cluster 7 should be considered with some caution. We can see two reasons for an absence of congruent results for cluster 7. First, it could be that our supplementary analysis did not have enough power to detect signal given the lower number of SNPs available in Zapotec. Else, our initial results for cluster 7 could be due to the own evolutionary history of the Pima and have nothing to do with adaptation to tropical forests. This could be due to an environmental pressure shared between West African populations and Pima, but not present in Zapotec.
Acknowledgments The authors would like to thank  Drs. Maria Luiza Petzl-Erler ,  Eduardo Tarazona Santos ,  Nelson J. R. Fagundes  and the three anonymous reviewers for their valuable comments on previous versions of the manuscript,  Prof. Andres Ruiz-Linares  for kindly making available the Zapotec dataset, and  Prof. Sandro L. Bonatto  for his contribution to the project. Detailed results regarding the list with all outlier SNPs before and after the sliding-window approach, their nearest gene (if located in a genic region), q-value, and best-supported model of selection as well as the table with gene set enrichment results are available upon request.
This study presents a recent assessment of the impact of assimilation of the GNSS derived ZTD observations into the AROME 3D-Var model. Two data assimilation experiments have been performed for a recent summer period of July 18, 2013 to August 20, 2013. The first experiment uses the ZTD from the stations and analysis centers available in real time through the E-GVAP. In the second experiment, additional stations processed by the University of Luxembourg have been added to the observations from E-GVAP. A third experiment without the assimilation of any GNSS ZTD observations has been performed to serve as a baseline for the first two experiments and to provide an impact assessment of GNSS ZTD data assimilation in the AROME model. From the output of these three experiments, various parameters have been extracted and statistics for the comparisons between those have been calculated.
Conclusions An impact assessment of the assimilation of GPS derived ZTD observations in the AROME 3D-VAR NWP model was conducted by experiments without GPS ZTD assimilation, with the assimilation of operational EGVAP ZTD solutions, and with the assimilation of ZTD observations from UL01 in addition to EGVAP solutions. The analysis and first-guess departures of the ZTD observations selected for assimilation from the UL01 solution were found to be almost unbiased and following a Gaussian distribution comparable to the ZTD EGVAP data and in agreement with underlying hypotheses of the 3D-Var assimilation system. The mean and standard deviation of the analysis departures of UL01 ZTD was found to be slightly higher than those of the EGVAP only solution. From the three assimilation experiments, 30-h forecasts of the AROME model were run.
Forecast scores for 2-meter temperature and 2-meter relative humidity were computed over the whole domain and rather neutral results were found. The GPS ZTD data seems to reduce slightly the bias in relative humidity but increase the bias in temperature. The assimilation of UL01 ZTD observations in addition to EGVAP solutions seems to have either a neutral or negative impact on the forecast scores. These preliminary forecast scores will be complemented by precipitation scores for specific severe weather events that took place during the period.
At Météo-France, a convection permitting NWP model AROME  [3]   The goal of this study is twofold. First, a recent assessment of the impact of GPS ZTD data assimilation in the current version of the operational 3D-Var data assimilation system for the AROME model (which has undergone various upgrades and improvements since the past assessment studies) has been provided. Secondly, the impact of increasing the GNSS network through the addition of the UL01 solution has been examined. 750 and 720 physical grid points in the eastwest and north-south directions respectively. The domain is vertically divided in 60 layers with the center of the uppermost layer located at 1 hPa. Figure  2  shows the domain of the AROME model. The colored dots in Figure  2  show the GNSS stations processed by various E-GVAP analysis centers used for the assimilation of ZTD in AROME.
Impact of Forecast Forecast scores were computed from the three experiments to study the effect of assimilating various GPS ZTD datasets on atmospheric forecasts for the period of July 18, 2013 to August 20, 2013. Figure  5  shows the bias and the standard deviation of the forecast scores for 2-meter temperature and Figure  6  shows the same for 2meter relative humidity averaged over the whole domain of the AROME model. The scores have been computed by comparing the forecasts to a dedicated 2-meter analysis using surface observations from SYNOP and RADOME  [4]  networks. It can be seen that the results are rather neutral. The GPS ZTD data reduces slightly the bias in relative humidity but increases the bias in temperature. The impact of assimilating UL01 ZTD observations in addition to EGVAP is either neutral or slightly negative.
Acknowledgements This impact assessment was carried under the  STSM ECOST-STSM-ES1206-041113-036253  granted by  COST Action   ES1206  ( GNSS4SWEC ). The work at the  University of Luxembourg  has been funded by the  Fonds National de la Recherche, Luxembourg  (Reference No. 1090247 ). We thank the  BIGF, CODE, EUREF, IGS, SPSLux , and WALCORS communities for GNSS data and products. Presented at: Workshop  ES1206 -GNSS4SWEC   | Munich ,  Germany | February   26-28 , 2014.
An LTAG for Vietnamese We ran extraction algorithms on the Vietnamese treebank and extracted two treebank grammars. The first one, G 1 , uses the original tagset of the treebank. The second one, G 2 , uses a reduced tagset, where some sets of tags in the treebank are consolidated, as shown in Table  2. The grammar G 2 is smaller than G 1 and it is presumed that the sparse data problem is less severe when G 2 is used. We count the number of elementary trees and tree templates. The sizes of the two grammars are in Table  3. Recall that a template is an elementary tree without the anchor word. There are 15 035 unique words in the treebank and the average number of elementary trees that a word anchors is around 3. 07. We also count the number of context-free rules of the grammars where the rules are simply read off the templates in an extracted LTAG.
Preprocessing pipeline Before being parsed, a text is fed to a chain of preprocessing modules including a sentence segmenter, a word tokenizer and a tagger. In particular, we have integrated the following preprocessing modules into the parser: • vnSentDetector -a sentence detector which segments a text into sentences; • vnTokenizer -a tokenizer which segments sentences into words or lexical units  (Le-Hong et al. 2008 ); • vnTagger -a part-of-speech tagger which tags each word of a sentence with its most appropriate syntactic category (Le-Hong et al. 2010). [ 166 ] We have adapted an LTAG parser developed at the LORIA 10  laboratory to construct a deep syntactic parser for Vietnamese. This parser was initially used to parse French text  (Roussanaly et al. 2005). Given a sentence, the parser outputs all possible constituency parses and their corresponding derivation trees.
Performance without POS tagging First, the parser is evaluated without using a POS tagger. That is, the module vnTagger is not integrated into the parser. In this setting, each word occurrence of an input sentence is tagged with all possible tags that have been assigned to it in the training set. Unknown words are tagged as common nouns (label N). We first evaluate the performance of the constituency analysis. The results are shown in Table  5. 14 In addition to the familiar precision and recall ratios, other measures are reported to help analyze the results: 15 • Complete match ratio is the percentage of sentences where recall and precision are both 100%. About 13% of the test sentences match completely. The complete match ratio for sentences of 10 words or less is 17. 57%. • The average crossing ratio is the number of constituents crossing a test constituent divided by the number of sentences of the test corpus.
Performance with POS tagging The results reported in the previous subsection make possible a preliminary evaluation of the grammar and the performance of the parser. Nevertheless, the condition under which the experimentation is carried out is rather harsh since the parser has to try all possible syntactic categories of each word of an input sentence. The experiments in this subsection are closer to real use conditions, in that each sentence is first processed by a tagger to remove POS-tagging ambiguity -each word is assigned a unique tag. We have thus a sole sequence of words/tags and it is used as input to the syntactic parser. The tagging is done by the vnTagger module. We proceed with the evaluation of this parser version in a similar way as presented for the previous version without POS tagging.
acknowledgements This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG. 15. 04. Any opinions, findings and conclusion expressed in this paper are those of the authors and do not necessarily reflect the view of VNU. We are grateful to our three anonymous reviewers for their insightful comments, which helped us improve the quality of the article in terms of both presentation and content. Finally, we thank the copy editors of the Journal of Language Modelling for their great job on the manuscript. [ 179 ].
SST data are generally resampled to about 1 • resolution before being assimilated with a typical sampling interval of 1 day. These spatial resolutions and temporal intervals generally seem to be sufficient for reconstructing the variability associated with ENSO. The horizontal distribution of SST is well observed from satellites and calibrated using in situ observations including the data from the mooring buoys. Gridded datasets of observed SST are provided from several operational centres. Assimilation of subsurface temperature observations is also essential for ENSO monitoring and S-I forecasting because variations of thermocline depths play important roles in the ENSO mechanism. In particular, there is a general consensus that baroclinic Kelvin wave activity along the Equator frequently affects occurrences of El Niño/La Niña.
This kind of information cannot be obtained without comparing the simulation fields with a long time series observation record. Therefore the long time series provided by TAO/TRITON moorings are extremely valuable to validate longterm simulations such as ocean reanalyses which cover several decades and include interannual variability of the tropical ocean. The TAO current data are also often exploited for validation although they are assimilated in few ocean DA systems. Figure  4  shows an example from Mercator. The representation of the Equatorial Undercurrent (EUC) was a known weakness in version 2 of the global 1/4 • system operated by Mercator for MyOcean, the European project providing products and services for all marine applications (http://www. myocean. eu; accessed 22 May 2015; accessed 22 May 2015).
To assess the improvement of EUC in the version 3 of the system, the analyzed zonal velocity profiles were compared with the current measurements at TAO buoys. Both versions of the system use a static SEEK filter  (Lellouche et al, 2013  give a description of the system configurations). The figure demonstrates that the EUC in the version 3 system is strengthened and agrees with the TAO measurements much better than that in the version 2 system. This improvement is attributed to the use of a more realistic mean dynamical topography and its error statistics applied for assimilation of the along-track SSH data in the version 3 system. Thus the TAO current data are valuable as independent data for validating ocean DA fields.
Observing system experiments (OSEs) at NOAA (NCEP and GFDL) The Climate Program Office of NOAA called for a coordinated OSE at NCEP and GFDL to assess impacts of TPOS for S-I forecasting. At NCEP, the operational seasonal forecast model, referred to as Climate Forecast System version 2 (CFSv2)  (Saha et al, 2013)  is used. Although the operational version of CFSv2 is initialized by a weakly coupled ocean and atmosphere reanalysis (sections 2. 1 and 2. 3), this was not done for the OSE project discussed here. Instead, the global ocean data assimilation system (GODAS;  Behringer and Xue, 2004)  was used to assimilate oceanonly data. In situ temperature and salinity profiles are assimilated in the reference run and the model SST is nudged strongly to the NOAA daily optimum interpolation SST (OISST;  Reynolds et al, 2007).
GFDL uses the ensemble coupled data assimilation (ECDA) system  (Zhang et al, 2007). In ECDA an ensemblebased filtering algorithm is applied to the GFDL's fully coupled climate model, CM2. 1, which is one of two GFDL CMIP3 models  (Delworth et al, 2006). In situ temperature and salinity profiles, winds, sea level pressure and temperature data from the NCEP reanalysis 2  (Kanamitsu et al, 2002) , and a weekly version of Reynold's OISST are assimilated in the reference run. To assess the relative roles of the TAO/TRITON and Argo data in constraining the upper ocean thermal structure and improving ENSO forecasts, four OSE runs were performed. The four runs and their designations include:  CTL: no ocean profiles assimilated, ALL: all ocean profiles assimilated, noMoor: all ocean profiles assimilated except the mooring profiles, noArgo: all ocean profiles assimilated except Argo.
RMSE in NINO-3 is reduced by 37% in GODAS and 60% in ECDA. Smaller impacts of Argo than those of all in situ data in the equatorial regions in ECDA implies that TAO/TRITON data themselves are sufficient to constrain oceanic fields in the system. The reason of the poor performance of GODAS in NINO-4 (160 • E-150 • W; 5 • N-5 • S) and the off-Equator regions (negative impacts of moorings and smaller impacts of all in situ data than those of Argo alone) is discussed in  Xue et al (2015). In order to evaluate the impacts of in situ observations on the skill of hindcasts, hindcast experiments were initialized from the four OSEs around 1 January, 1 April, 1 July and 1 October during 2004-2011. From each start time, an ensemble of 6 (10) coupled forecasts with perturbed initial conditions was integrated up to 12 months ahead using CFSv2 (CM2.
Observing system experiments at JMA/MRI JMA/MRI also conducted a series of OSEs to evaluate the relative impact of Argo floats and TAO/TRITON buoys on the ocean DA fields and ENSO forecasts using an operational seasonal forecasting system  (Fujii et al, 2011 (Fujii et al, , 2015)). The system uses the nearly-global ocean DA system, MOVE-G (Multivariate Ocean Variational Estimation System Global Version; Fujii et al, 2012), for the ocean initialization, in which a multivariate 3D-Var scheme  assimilates satellite SSH, gridded SST, and in situ temperature and salinity profiles, including data from Argo, XBT, and tropical moorings. Impacts of TAO/TRITON and Argo data on the ocean heat content in the equatorial Pacific in the DA system is quantified using seven OSE runs, namely, noArgo (the same as in 3.
1. 1), Argo20, Argo40, Argo60, Argo80 (20, 40, 60, 80% of Argo data and all available data from other than Argo are assimilated), noTTA80 (TAO/TRITON data and 20% of Argo data are withheld), and TTeqA80 (TAO/TRITON data outside 2. 5 • S-2. 5 • N and 20% of Argo data are withheld). The accuracy of these runs is evaluated by the RMSE against the 20% of Argo float profiles that are withheld from all OSE runs (Figure  7 ). This figure clearly demonstrates that the impact of Argo data on salinity is larger than that on temperature. The impact of Argo is largest in the NINO-3 region and smallest in the TRITON region. It also indicates that the accuracy monotonically improves with an increasing number of assimilated Argo floats from 0 to 80% for both temperature and salinity in NINO-3 and NINO-4 regions, indicating that any further increase in the number of Argo floats has the potential to further improve the accuracy of the DA system in those regions.
The accuracy of the run without TAO/TRITON (noTTA80) is roughly similar to the ARGO40 run in NINO-3 and NINO-4 regions, implying impacts of TAO/TRITON is similar to 40% of Argo data. It should be noted that we evaluated here the impacts of TAO/TRITON complementary to all other available data, including Argo and altimetry data, and that the intrinsic impact of TAO/TRITON is larger  (Fujii et al, 2015). Accuracy of the salinity field is not degraded if data from extra-equatorial buoys outside of 2. 5 • S-2. 5 • N are withheld (TTeqA80) in the two regions, although assimilation of extratropical buoys has some impacts on temperature there. The impact of TAO/TRITON data is as large as (larger than) that of 80% of Argo data in the TRITON region, and the data from extra-equatorial buoys also have some impact.
We also quantified the impact of TAO/TRITON and Argo data on the forecasts of NINO-3 and NINO-4 SST indices using four OSE runs, namely, ALL, noArgo (the same as in 3. 1. 1), noTT (TAO/TRITON data are withheld), and TTeq (TAO/TRITON data outside of 2. 5 • S-2. 5 • N are withheld). We performed 13month, 11-member ensemble hindcasts from each OSE run using the coupled model. The hindcasts were started from the end of January, April, July, and October in 2004-2011. TAO/TRITON data reduce the RMSE of the NINO-3 and NINO-4 indices for lead months from 1 to 4 (L1-4) by 3. 5 and 5. 8%, respectively (Figure  8 ). The impact on NINO-3 increases for lead months from 5 to 8 (L5-8) and from 9 to 12 (L9-12). In contrast, the impact on NINO-4 is not changed for L5-8 and disappears for L9-12. Assimilating equatorial buoy data (2.
5 • S-2. 5 • N) alone (TTeq) increases the RMSEs by 3-6% compared to ALL, except for NINO-4 for L9-12. It should be noted that the RMSEs in TTeq are larger than noTT for NINO-3 for L1-4 and L5-8, which may indicate the importance of assimilating equatorial and extra-equatorial (outside of 2. 5 • S-2. 5 • N) buoys simultaneously, especially for relatively short-lead-time forecasts of the NINO-3 index. Impacts of Argo are larger than those of TAO/TRITON on both NINO-3 and NINO-4 for all lead times except for NINO-3 indices for L1-4 and NINO-4 for L5-8. The impact of Argo on NINO-3 is enhanced with increasing lead time, but for NINO-4 the impact is not monotonic being smaller for L5-8 than for shorter and longer lead times. Although this study has evaluated the impacts of TAO/TRITON and Argo data through OSEs, it should be noted that the error statistics and MDT used in the DA process of MOVE are also estimated from in situ observations.
As expected, in both areas the agreement with the mooring data is poorest when the mooring data are withdrawn. Withdrawing the altimeter also degrades the consistency with the mooring data. Withdrawing Argo has a neutral impact in the eastern Pacific, but, surprisingly, reduces the RMSE in the western Pacific. This may be caused by the different spatial sampling of the mooring and TAO data. It can also be due to the inability of the DA to blend them adequately (for instance, by not having adequate spatial decorrelation scales or representativeness error). Further work is needed to disentangle these two possibilities by, for instance, checking if this is a common feature in other DA systems, and if it is the case in model-independent evaluation. When computing the fit with respect to all in situ observations (and not only data from moorings), the fit to in situ temperature profiles is degraded in the eastern tropical Pacific whenever any data type is withheld (not shown).