dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous studies  (O'Neill et al. , 2013a and 2013b ) evaluated GrazeIn at both a herd level and an individual cow level using an independent database. The detailed evaluations of GrazeIn by O'Neill et al. (2013a and 2013b) highlighted the large error with which MY in autumn/late lactation was predicted, compared with spring and summer/early and mid lactation. As a result, it was necessary to adapt GrazeIn. The objective of this study was to adapt GrazeIn to improve the prediction accuracy of the model for GDMI and MY prediction for spring calving grazing dairy cows. The prediction accuracy was measured/assessed using parameters such as the relative prediction error (RPE).
Moorepark database description To carry out the adaptation and evaluation of GrazeIn, data from the Moorepark database  (O'Neill et al. , 2013b)  were used as model input data to simulate GDMI and MY. From an original database of 8787 individual cow measurements, a data set of 4514 cows with measured data for all required input variables was selected. From this data set, 554 cows were removed, as these were cows in herds where >30% of the herd had been removed owing to lack of complete measured data. This resulted in a data set of 3960 individual cow measurements available for use from 11 published studies spanning the years 2004 to 2009 (Table  1 ). A description of the cow, sward, grazing management and supplementation variables for the data set can be found in Tables  2  and 3. All cows in the data set were from spring calving herds resulting in stage of lactation and season being confounded.
Cows in the data set had a mean PMY peak of 32. 7 kg/cow per day (Table  2 ) and a mean actual daily GDMI of 15. 9 kg DM/ cow per day. The mean actual MY was 20. 4 kg/cow per day (Table  2 ). Cows in the data set received concentrate supplementation ranging from 0. 0 kg to 6. 2 kg DM/cow per day (Table  3 ). Figure  1  Structure of the GrazeIn model of grass dry matter intake (GDMI) and milk yield  (Delagarde et al. , 2011a; Faverdin et al. , 2011). Adaptation of GrazeIn model for grazing dairy cows Model adaptation GrazeIn was rebuilt using the C ++ programming language with the equations from  Delagarde et al. (2011a)  and Faverdin et al. (2011). Data from the Moorepark data set were used as input variables in both the original and the rebuilt GrazeIn model. The predicted GDMI and MY from both simulations were compared to test the reconstructed model.
Results Original GrazeIn model Total data set. The model predicted GDMI with an RPE of 15. 8% (Table  4 ). The majority of the MSPE was attributed to random variation (0. 85). The model had a CCC for GDMI of 0. 69 with a Pearson correlation coefficient of 0. 70 and a bias correction factor of 0. 98. GrazeIn predicted MY with an RPE of 18. 9% (Table  4 ). The majority of the MSPE was attributed to random variation (0. 95). The model had a CCC for MY of 0. 84 with a Pearson correlation coefficient of 0. 86 and a bias correction factor of 0. 97. By stage of lactation. Investigating GDMI by stage of lactation, the RPE ranged from 15. 2% to 16. 4% (Tables  5, 6  and 7 ). The bias between predicted and actual values was consistently negative indicating the model on average underpredicted GDMI during all stages of lactation.
Inclusion of IC correction coefficient in the model Total data set. Grass DMI was predicted with an RPE of 15. 0%. The majority of the MSPE was attributed to random variation (0. 95) (Table  4 ). GrazeIn predicted a mean daily MY of 21. 8 kg/cow per day with an RPE of 19. 6% (Table  4 ). The majority of the MSPE was attributed to random variation (0. 87). By stage of lactation. Investigating GDMI by stage of lactation the RPE ranged from 13. 9% to 16. 0% (Tables  5, 6  and 7 ). The majority of the MSPE was attributed to random variation (1. 00, 0. 87 and 0. 89 in early, mid and late lactation, respectively). GrazeIn predicted MY in early lactation with an RPE of 15. 4% (Table  5 ) but predicted MY of cows in mid and late lactation with an RPE of 18. 9% and 30. 5%, respectively (Tables  6  and 7 ).
Adaptation of GrazeIn model for grazing dairy cows Inclusion of IC correction coefficient and BFR deposition in the model Total data set. The prediction accuracy results of GrazeIn for GDMI were identical to the previous step (Table  4 ). GrazeIn predicted a mean daily MY of 21. 5 kg/cow per day (Table  4 ). MY was predicted with an RPE of 18. 9%. The majority of the MSPE was attributed to random variation (0. 90). By stage of lactation. The prediction accuracy results of GrazeIn for GDMI were identical to the previous step (Tables  5, 6  and 7 ). GrazeIn predicted MY in early lactation with an RPE of 15. 4% (Table  5 ) but predicted MY of cows in mid and late lactation with an RPE of 18. 3% and 27. 9%, respectively (Tables  6  and 7 ). The proportion of the MSPE attributed to the mean bias and line bias was 0.
01 and 0. 22 for early lactation, 0. 23 and 0. 08 for mid lactation and 0. 41 and 0. 08 for late lactation. Inclusion of IC correction coefficient and BFR deposition in the model and optimisation of the potential lactation curve Total data set. The model predicted GDMI with an RPE of 15. 6% (Table  4 ). The majority of the MSPE was attributed to random variation (0. 89). MY was predicted with an RPE of 16. 5% (Table  4 ). The majority of the MSPE was attributed to random variation (0. 97). By stage of lactation. Investigating GDMI by stage of lactation the RPE ranged from 14. 8% to 16. 2% (Tables  5, 6  and 7 ). The majority of the MSPE was attributed to random variation (0. 98, 0. 78 and 0. 79 in early, mid and late lactation, respectively). GrazeIn predicted MY in early lactation with an RPE of 14.
8% (Table  5 ) but predicted MY of cows in mid and late lactation with an RPE of 15. 4% and 21. 2%, respectively (Tables  6  and 7 ). The majority of the MSPE was attributed to random variation (0. 76, 0. 96 and 0. 92 in early, mid and late lactation, respectively). Inclusion of IC correction coefficient and BFRs deposition in the model and optimisation of the potential lactation curve and the IC correction coefficient Total data set. The model predicted GDMI with an RPE of 15. 0% (Table  4 ). The majority of the MSPE was attributed to random variation (0. 97). The model had a CCC for GDMI of 0. 68 with a Pearson correlation coefficient of 0. 69 and a bias correction factor of 0. 97, which was similar to the original model. MY was predicted with an RPE of 16. 7% (Table  4 ). The majority of the MSPE was attributed to random variation (0.
Discussion Various models have been developed for the prediction of GDMI and MY of grazing dairy cows (Baudracco et al. , 2010;  Delagarde et al. , 2011a; Faverdin et al. , 2011). One of these models, GrazeIn  (Delagarde et al. , 2011a; Faverdin et al. , 2011) , was identified by  Delagarde and O' Donovan (2005)  as being suitable for predicting the GDMI and MY of grazing dairy cows. They evaluated GrazeIn using Irish and French databases containing 190 and 114 grazing herds, respectively. They found that GrazeIn predicted GDMI with a lower level of error for Irish (RPE = 10%), French (RPE = 12%) and combined databases (RPE = 11%) than three other published GDMI models (Sepatou: Cros et al. (  2003   2004 )). In addition, with GrazeIn, more of the variation was attributable to random variation than was the case for the other three models.
GrazeIn predicted more accurately than the other three models because of its utilising more input factors and estimating more interactions than those of other models  (Delagarde and O' Donovan, 2005). Numerous evaluations of GrazeIn have been carried out using data from different countries  (Delagarde and O' Donovan, 2005; Delagarde et al. , 2011b; O'Neill et al. , 2013a and 2013b). O'Neill et al. (2013a)  evaluated GrazeIn at a herd level using an independent database, not only for the database as a whole, but also by season and by grazing management input variable. For the total database (522 grazing herds), GrazeIn predicted GDMI with an RPE of 12. 2% and MY with an RPE of 12. 8%. Investigation by season and input variable revealed that GrazeIn predicted MY in autumn with a large level of error compared with spring and summer and predicted MY less accurately for herds offered grass with a low FV, high UFL and high PDIE concentration compared with herds offered grass of lower quality (higher FV, lower UFL and lower PDIE concentration).
In the study by  O'Neill et al. (2013b) , GrazeIn was evaluated at a cow level using 8787 individual cow measurements from Irish studies. GrazeIn predicted GDMI with an RPE of 15. 5% and MY with an RPE of 16. 7% for the total database. There were large differences in prediction accuracies between stages of lactation. GrazeIn predicted MY in late lactation with a large level of error (RPE = 22. 9%) compared with early and mid lactation (RPE = 13. 9% and 15. 4%). The detailed evaluations of GrazeIn by  O'Neill et al. (2013a and 2013b ) highlighted the large error with which MY in autumn/late lactation was predicted compared with O'Neill, Ruelle, O'Donovan, Shalloo, Mulligan, Boland, Delaby, Delagarde and Lewis spring/early and summer/mid lactation. As a result, it was necessary to adapt GrazeIn.
, 2011). Increasing the IC of the grazing cow by 5% in the model reduced the RPE for GDMI and the bias between predicted and actual GDMI for the total data set and for GDMI prediction in the different stages of lactation. As the GDMI and MY sub-models are linked, the increased IC resulted in an increase in energy intake with the extra energy partitioned into MY. In early lactation, this resulted in an improved MY prediction owing to the underprediction of MY at this time. In mid and late lactation, however, there was an increase in the RPE of MY because of an increase in the overprediction of MY.
Conclusion The adaptation of GrazeIn improved the GDMI and MY prediction accuracy by using the original input variables, adding new equations to the model and optimising some coefficients in the model. The adaptations not only improved the prediction accuracy of GDMI and MY prediction across the total data set but also in each stage of lactation, particularly MY in late lactation. Future work on GrazeIn should aim to test whether using new input variables not previously included would increase the prediction accuracy of GDMI and MY. Further testing of the adapted model, using an independent data set, would be beneficial.
Acknowledgements This work was funded by the  Irish Department of Agriculture, Food  and the  Marine Research Stimulus Fund  ( RSF 07 526 ) and The  Irish Dairy Levy Trust. The first author was in receipt of a  Teagasc Walsh Fellowship. The authors would like to acknowledge the assistance of the researchers and students whose data were used to compile the data set used in the adaptation and to  Philippe Faverdin  ( INRA ) for his help with the adaptation process.
BiFC interaction of CAR1-YFP N and YFP C -PYL4 was observed, and this interaction colocalizes with the plasma membrane marker OFP-TM23 (Merged). Epifluorescence confocal images of epidermal N. benthamiana leaves infiltrated with the indicated constructs were merged to quantitatively estimate the colocalization of YFP/GFP and OFP fluorescence. Statistical analysis of the colocalization of CAR1-YFP N / YFP C -PYL4-interacting proteins and OFP-TM23 was done using Pearson's (r P ) and Spearman's (r S ) correlation factors  (French et al. , 2008). The degree of colocalization between the two fluorescent signals was analyzed using Zeiss software. Bars = 20 mm. (D) Relative fluorescence intensity of the BIFC interactions detected for either CAR1-YFP N or CAR4-YFP N and the indicated constructs. Each BiFC experiment was scanned and measured in 25 randomly chosen microscopic fields (n = 25) and repeated three times.
As a result, we found colocalization of both fluorescent markers (Pearson-Spearman correlation coefficients in the range 0. 55 to 0. 66; Figure  2B ). Therefore, a significant fraction of CAR-GFP proteins was localized to the plasma membrane. By contrast, GFP alone did not show colocalization with reconstituted SCFP (Pearson-Spearman correlation coefficients below zero). GFP-PYL4 alone did not show a substantial localization in the plasma membrane; however, when YFP C -PYL4 was coexpressed with CAR1-YFP N , it showed partial colocalization with the plasma membrane marker used in this experiment (reconstituted SCFP) (Figures  2A  and 2B ). Independent evidence for localization in the plasma membrane of CAR proteins came from data mining in the results published by  Demir et al. (2013). In that work, proteins associated with detergent-resistant membranes from leaf plasma membrane preparations were identified using mass spectrometry.
Confocal Laser Scanning Microscopy Confocal imaging was performed using a Zeiss LSM 780 AxioObserver. Z1 laser scanning microscope with a C-Apochromat 403/1. 20 W corrective water immersion objective. The following fluorophores, which were excited and fluorescence emission detected by frame switching in the single or multiple tracking mode at the indicated wavelengths, were used in N. benthamiana leaf infiltration experiments: SCFP (405 nm/464 to 486 nm), GFP (488 nm/500 to 530 nm), YFP (488 nm/529 to 550 nm), and OFP (561 nm/575 to 600 nm). Pinholes were adjusted to 1 air unit for each wavelength. Postacquisition image processing was performed using ZEN (ZEISS Efficient Navigation) Lite 2012 imaging software and ImageJ (http://rsb. info. gov/ij/). Epifluorescence confocal images of epidermal N.
00; digital offset, 0. 00) as well as zoom factor (1. 2) covering two to three living cells per image. As negative controls in the interaction assays, Agrobacterium expressing either CAR1-myc-YFP N or CAR4-myc-YFP N was coinfiltrated with YFP C -OST1 1-280  (Vlad et al. , 2009). Image quantification of relative fluorescence intensities was performed using ImageJ software by measuring the fluorescence intensity corrected for mean background fluorescence subtracted from corresponding areas showing no green fluorescence. Each BiFC experiment was scanned and measured in 25 randomly chosen microscopic fields (n = 25) and repeated tree times.
Phospholipid Binding Assays Calcium-dependent protein binding to phospholipid vesicles was assessed as described by  Schapire et al. (2008). A 25:75 (w/w) mixture of phosphatidylserine:phosphatidylcholine (Sigma-Aldrich) was prepared in chloroform and dried under a stream of nitrogen to obtain a thin layer. The dried lipids were resuspended in buffer A (100 mM NaCl, 50 mM HEPES, pH 6. 8, and 4 mM EGTA) and mixed by vortexing for 20 min. We pelleted the large multilamellar vesicles by 20 min of centrifugation at 16,000g, then they were resuspended in 1 mL of buffer A supplemented or not with the indicated calcium concentration. Free calcium concentrations were calculated using the WEBMAXC program (http://www. stanford. edu/~cpatton/maxc. html). The vesicles (;100 mg of phospholipids) were used immediately after preparation and mixed with the indicated His-tagged recombinant proteins (5 mg).
Next, they were incubated with gentle shaking (250 rpm) on a platform shaker. The vesicles and the bound proteins were pelleted by centrifugation for 10 min at 16,000g at 4°C, and pellets were washed twice with 0. 5 mL of buffer A. Proteins that were bound to the vesicles were revealed by immunoblot analysis using anti-His antibody and ECL antimouse peroxidase (GE Healthcare) as secondary antibody. Detection was performed using the ECL Advance protein gel blotting detection kit (GE Healthcare). Quantification of the binding was determined using ImageJ software, and mathematical analysis of calcium binding was performed based on nonlinear least-squares fitting to the three-parameter Hill's equation using Sigma Plot 12 software.
Accession Numbers The atomic coordinates and structure factor amplitudes of CAR4 in complex with calcium have been deposited in the Protein Data Bank (www. pdb. org) with identifier 4v29. Arabidopsis Genome Initiative locus identifiers for CAR1, CAR2, CAR3, CAR4, CAR5, CAR6, CAR7, CAR8, CAR9, and CAR10 are At5g37740, At1g66360, At1g73580, At3g17980, At1g48590, At1g70800, At1g70810, At1g23140, At1g70790, and At2g01540, respectively. Arabidopsis Genome Initiative identifiers for PYR1, PYL1, PYL4, PYL6, and PYL8, are At4g17870, At5g46790, At2g38310, At2g40330, and At5g53160, respectively.
ACKNOWLEDGMENTS We thank  Joerg Kudla  ( University of Münster ) for kindly providing plasma membrane markers. This work was supported by the  Ministerio de Ciencia e Innovacion, Fondo Europeo de Desarrollo Regional , and  Consejo Superior de Investigaciones Cientificas  (Grants  BIO2011-23446  to P. L. R and  BFU2011-25384  to A. A. ; fellowships to L. R. , R. A. , and A. C. I. -G. ;  BES-2009-016569 ; JAE-DOC contract to M. G. -G. ) as well as the  Senacyt-Ifarhu (Panama)  (fellowship to M. D. ).
Acknowledgements We are grateful to  René Frydman  and  Alexandra Benachi  for their efficient collaboration throughout the last ten years,  Claire Beausoleil  ( ANSES, France ),  Christophe Rousselle  ( ANSES, France ) and  Paul Fowler  ( University of Aberdeen, Scotland ) for helpful discussions,  Aurélie Gouret  for skillfull secretarial assistance and  E. Andermarcher  for editing the English manuscript. Our work on phthalates was supported by  Université Paris Diderot-Paris 7 , the  Atomic Energy Comittee (CEA) , the  National Institute for Health and Medical Research (INSERM) , the  Agence Nationale de la Recherche  ( ANR, contrat Phtalatestis ) and by the  Ministère de l'Ecologie, du Développement Durable, des Transports et du Logement  ( Programme ANTIOPES -Storm ). All the authors never received any personal or professional advantages, remunerations, financial supports or fundings from the  Chemical Industry.
Simulations The simulated data were generated using EasyPop v2. 01 software  [12]. We simulated diploid individuals in nonoverlapping generations and distributed them in 100 subpopulations of 50 individuals each. The choice of these numbers was made without fundamental principles. This, however, allowed exploring various kinds of population structure with reasonable effects of drift and migration. We simulated 20 loci with mutation rates ranging from u = 10 -9 to u = 10 -3. These mutation rates were selected as regard to the types of commonly used genetic markers such as SNPs, allozymes and microsatellite markers. The mechanism of mutation follows a KAM, where each of K possible alleles (1 to K) can mutate into any of the K-1 available alleles. Each simulation started with a maximum diversity (all K alleles evenly distributed among the 100 × 50 individuals) and ended after 10,000 generations, which was enough to reach an approximate equilibrium state  [8].
Other data from African trypanosomes, the DNA of which was amplified directly from host blood (no isolation step), were also investigated. T. evansi from Sudan, the reproductive system of which remains unclear, though assumed to be clonal  [30, 31] , was suspected to present many allelic dropouts, due to the presence of an abnormally high proportion of homozygous individuals without missing genotypes and substantial variance of F IS across loci, together with a Wahlund effect  [32]. In T. congolense, strong heterozygote deficits were found  [33] , for which the authors proposed a highly inbred sexual mode of reproduction. Nevertheless, the data displayed many missing data. Finally, T. vivax data  [34]  were assumed by authors to fit with expectations under clonal reproduction despite a large variance of F IS from one locus to another.
The presence of blank genotypes can represent strong support in that respect but will only be useful in null allele cases and Dropout 2 kind of models. Allelic dropouts are indeed unlikely to generate many homozygous profiles if any  [19] [20] [21]. It is worth noting that this tool does not provide the proportion of hidden alleles in the real datasets of clones, which is another interesting, though much more complex issue. We have proposed a rough solution in case of null alleles using the proportion of missing data, assuming all are null homozygotes. Nevertheless, the technique presented here does not represent a palliative but a useful decision criterion that can lead to the elimination of problematic loci, the re-amplification of homozygous and/or missing genotypes, or to the design of new sets of primers.
Acknowledgements This study was made possible thanks to funding from the  PEERS  ( Programme d'Excellence pour l'Enseignement et la Recherche au Sud) TAO (Trypanosomes et tsétsés en Afrique  de l'Ouest: apport de la génétique des populations) of the  AIRD (Agence Inter-établissements de Recherche pour le Développement). It was undertaken within the frame of the Lamivect ( Laboratoire mixte international sur les maladies à vecteurs de Bobo-Dioulasso ), which is supported by the  IRD (Institut de Recherche pour le Développement). The work presented is also part of the  ANR (Agence Nationale de la Recherche)  project  Clonix  " Revisiting the Population Genetics and Genomics of clonal organisms " ( ANR-11-BSV7-007 ).
Clinical Evidence of Immune Competence and Protective Immunity in Patients Treated with Teriflunomide Clinical data are consistent with evidence from preclinical in vivo studies suggesting that teriflunomide treatment does not significantly disrupt protective immunity, and its antiinflammatory activity primarily affects the pathogenic immune processes associated with disease activity. Pooled data from placebo-controlled studies of teriflunomide, including over 2400 patients from two phase 3 studies (TEMSO [NCT00134563]  [9]  and TOWER [NCT00751881]  [10] ) and one phase 2 study (NCT01487096)  [88] , showed that teriflunomide treatment was associated with a mean decrease in leucocyte counts of approximately 15 % from baseline (Fig. 3e )  [89]. However, mean absolute counts remained within the normal range, decreases occurred within the first 3 months of treatment, and levels did not change further with continuing treatment.
In long-term extensions of the phase 2 and TEMSO studies (up to 9 years of teriflunomide treatment) and consistent with the double-blind phases of the studies, there was no signal for increased malignancies or serious opportunistic infections  [90, 91]. Taken together, these findings provide evidence that teriflunomide does not have a negative impact on protective immunity over the observed treatment period. To investigate whether the generation of recall antigen responses was preserved under teriflunomide treatment, immune responses to the 2011-2012 seasonal influenza vaccination (including strains H1N1, H3N2, and B) were measured in patients with MS treated with IFNb and in patients exposed to teriflunomide for at least 6 months (TERIVA, NCT014033760)  [92]. Most patients had antibodies against each strain detectable at baseline, which was attributed to the 2011-2012 vaccine containing the same strains as the 2010-2011 vaccine.
Acknowledgments Editorial support (assistance in drafting and editing of the manuscript text, figures, and tables, as directed by authors, data checking and incorporation of comments from reviewers, and assisting with the submission process) provided by  V Lawson  at  Fishawack Communications Ltd , funded by  Genzyme , a Sanofi company. This was the only funding provided for the development of this manuscript. Disclosures and Conflicts of Interest ABO has participated as a speaker at meetings sponsored by, received consulting fees and/or received grant support from:  Amplimmune, Bayhill Therapeutics ,  Berlex/Bayer ,  Biogen Idec, BioMS, Diogenix, Eli-Lilly ,  Genentech ,  Genzyme ,  GlaxoSmithKline, Guthy-Jackson/GGF ,  Merck/EMD Serono ,  Medimmune ,  Mitsubishi Pharma ,  Novartis ,  Ono Pharma ,  Receptos ,  Roche ,  Sanofi ,  Teva Neuroscience, Wyeth.