dataesr/scientific-paragraphs-categorization huggingface dataset:
The maximum delay of administration time between any of the two doses in the same patient varied between 3 and 101 minutes (average 30 minutes). Individual concentrations are plotted in figure  4. Data were adequately fitted by a one-compartment model with lag time and first-order absorption and elimination. Inclusion of lag time improved the fitting, and the value of 0. 3 hour was fixed. Interindividual variability was estimated for apparent volume of distribution (V/f) and apparent oral clearance (CL/f). Residual variability was best described by a constant variance model. The influence of four different measures of size was compared. The use of ffM for CL/f and V/f showed greater improvement in OfV than TBW or LBW. The use of allometric three-quarter fixed-power scaling and the use of NfM did not improve the model.
Discussion The present pilot study was conducted to evaluate the efficacy of mazindol (1 mg per day for 7 days) in children with ADHD. We observed a significant reduction in ADHD RS-IV and CPRS-R:L total scores, concordant with the clinicianrated CGI-I scale. Pharmacokinetic analysis showed that a one-compartment model with lag time and first-order absorption and elimination was adequate for data modeling. It also showed that ffM and age were significant covariates contributing to mazindol pharmacokinetic variability in children. Although frequent, adverse drug effects did not require drug interruption. No relation between pharmacokinetics and response was evidenced. Mazindol is an imidazoisoindole derivative unrelated to amphetamine. 15, 16 It is not metabolized to an amphetaminelike compound, but acts by blocking dopamine and norepinephrine reuptake similarly to amphetamine.
Acknowledgments This study was supported by a grant from  Assistance Publique -Hôpitaux de Paris (APHP). Genopharm helped to support this study and provided the tablets of mazindol. The authors wish to thank the nurses, technicians, and statisticians involved in this study. The authors also wish to thank  Ms Delphine Prieur  (assistant) and  Dr florence Tubach  ( URC Bichat ), who contributed to the study design and methodology, and MededGlobal Solutions france. Preliminary results of this study were presented at the 56th Annual Meeting of the  American Academy of Child and Adolescent Psychiatry , October 27-November 1, Honolulu, HI, and at NeuroTalk, June  25-28 , 2010, Singapore.
0036765), respectively, and d is the delta notation in parts per 1000 (%) relative to the standard. The N percentages in each P and AA fraction of the samples were determined using the elemental analyser, with atropine as the standard. Urea concentrations in plasma were determined with a commercial kit using an enzymatic method (Urea kit S-1000, Biomerieux, Craponne, France). Calculations to quantify total N amounts in the various N fractions of tissues, plasma and urine that corresponded to the model compartments are described in detail in Text S2. Table  S2  summarizes all the experimental data used for model calibration.
In our representation, in line with the classical formalism  [71] , the 15 N abundance of the instantaneous flux f j,i (df j,i ) coming from compartment i is defined by the 15 N abundance of compartment i and the fractionation factor e j,i according to the following equation: df j,i = dN i +e j,i. Consequently, a negative e value reflects a higher affinity of the metabolic pathway for the lighter isotope (i. e. , a preferential transfer of 14 N rather than 15 N), whereas a positive e value indicates a preferential transfer of the heavier isotope. The model was implemented using MATLAB's SimBiology toolbox (version 2. 2) and, for the time-course simulations of N and d 15 N state variables, the ODE system was solved using the MATLAB stiff solver ode15s. The complete set of ODEs describing the system can be found in Table  S1.
Acknowledgements We are grateful to  Veronique Jamilloux  and  Matthias Zytnicki  for methodological advices and technical assistance. We thank  Eva-Maria Willing ,  George Coupland  and  Korbinian Schneeberger  for granting the use of the A. alpina genome, and  Xi Wang  for sharing the A. thaliana tiling array data. We also thank  Vincent Colot , HerveVaucheret and  Detlef Weigel  for valuable advices. This work was supported by two grants from the  French national research agency  ( ANR-08-KBBE-012 ,  ANR-2011-BSV6-00801 ) attributed to  Hadi Quesneville.
Data sets. Genome sequences were obtained from the following sources: A. thaliana ecotype Col-0 (TAIR10 release) (http://www. phytozome. com/arabidopsis. php); A. thaliana ecotypes Ler-1, Kro-0, Bur-0, and C24 (http://www. 1001genomes. org/); A. lyrata (v1. 0, http://www. phytozome. com/alyrata. php); C. rubella (initial release, http://www. phytozome. com/capsella. php); A. alpina (https://urgi. versailles. inra. fr/download/AaGenomeV4. tar. gz); T. parvula (v2. 0, http://thellungiella. org/data/); T. halophila (initial release, http://www. phytozome. com/thellungiella. php); B. rapa (v1. 2, http://www. phytozome. com/napacabba-.
Tower-based measured WUE can be used to evaluate the reliability and accuracy of estimates of global WUE from satellite-based approaches. The primary objectives of this study are 1) to investigate the latitudinal trend in WUE of terrestrial ecosystems across different plant functional types with data from FLUXNET; 2) to evaluate global patterns of terrestrial WUE using satellite remote sensing-based GPP and ET products, which provides a new framework for global research on carbon and water cycles. In this study, we used measurements of CO 2 and H 2 O fluxes combined with other hydrometric measurements of 32 sites covering six major plant functional types that represent the major ecozones of the northern Hemisphere. These sites range from 20uN to 70uN in latitude and provide 137 site-years in total (Table  1  and Fig.
The lower annual WUE of crops could also be affected by non-productive phases with higher soil evaporation compared to permanent vegetation. 1. The interannual variability is represented by the standard error bar. This figure was produced using Origin 8. 0. To further investigate the global patterns of terrestrial water-use efficiency, we also used satellite remote sensing-based data from the recently revised MODIS GPP and newly developed ET products between 2000 and 2013. These were used to infer mean annual terrestrial WUE over a period that closely corresponds to tower-based measured WUE. Multiyear mean annual GPP, ET, and WUE showed strong regional variations corresponding to climatic variations in water availability but still presented consistent latitudinal gradients similar to data from the tower network (Fig.
Methods Data acquisition. Our analysis is based on continuous observations of land surface exchanges from 32 flux towers situated in biomes of different plant functional types distributed around the Northern Hemisphere from 20uN to 70uN (Fig. 1  and Table  1 ), giving a total of 137 site-year dataset to reduce the uncertainty of year-to-year variation. Specifically, these flux sites encompassed broad-leaved evergreen forest (3 sites), needleleaf evergreen forest (9 sites), broad-leaved deciduous forest (6 sites), mixed forest (3 sites), grassland (5 sites) and crop (6 sites). The FLUXNET level 4 product provides measurements of canopy-scale water vapour flux, CO 2 flux, meteorological variables (including radiation, air temperature, relative humidity, soil temperature, and soil water content ), and estimates of gross primary photosynthesis derived from the measured NEE fluxes  [34] [35] [36].
These data were quality checked, and data gaps due to system failure or data rejection were filled using standardized methods to provide complete and standardized data sets  20, [37] [38]. To estimate and analyze the global patterns of terrestrial water-use efficiency, we also used the remotely-sensed data from MODIS gross primary production (GPP) and developed ET at the global scale. The old Collection 4 GPP product was found to have considerable errors due to problems in the inputs. Zhao et al (2005)   23  rectified these products by improving the data processing methods and modifying parameters in the algorithm used, which generated the improved Collection 5 (C5) MOD17 estimates. The 8-day composite 1-km fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) data from the MODIS sensor were used as remotely sensed vegetation property dynamic inputs to the algorithm.
Data gaps in the 8-day temporal MODIS FPAR/LAI caused by cloudiness were filled with information from accompanying quality-assessment flags. For daily meteorological data required to drive the algorithm, 6-hourly National Center for Environmental Prediction/ Department of Energy (NCEP/DOE) reanalysis II data were implemented. Monthly and annual GPP averages were derived by summing up each 8-day period. In addition, Mu et al. (  2011 )  24  improved a satellite remote sensing-based ET algorithm to assess global terrestrial ET using MODIS and global meteorology data by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface.
Acknowledgments This study was jointly supported by the  National Natural Science Foundation of China  ( 41030745 ,  41401221 ,  41271500 ), the  Key Research Program of the Chinese Academy of Sciences  ( KZZD-EW-10-04 ), and the  Natural Science Foundation of Jiangsu Province, China  ( BK20141058 ). This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the  AmeriFlux, CrarboEurope-IP and AsiaFlux networks. A large number of technicians, graduate and doctoral students are acknowledged for help in site management, data collection and elaboration. We also thank the group of  Prof. Qiaozhen Mu  at the  University of Montana  to provide satellite-derived global record of land surface evapotranspiration product. In addition,  Dr. Ke Zhang  at the  University of Oklahoma  also provided useful suggestions to process these big data.
BAC sequencing and sequence assembly BAC clones from the Sr2 locus of Hope were isolated using probes from the previously characterised 3B locus of Chinese Spring and were sequenced by a BAC-by-BAC shotgun method at 10× coverage using Applied Biosystems capillary sequencing. Base calling, quality trimming and assembly of the raw sequence reads into contigs were done using the PHRED/PHRAP/CONSED package  [20, 21]  and GAP4  [22]  from the Staden package was used for data integration and sequence finishing. The BACs isolated from the non-enriched Hope genomic DNA library were sequenced using Roche 454 sequencing platform. A hybrid assembly of the sequence from 454 and Sanger sequencing was done using Newbler v. 2. 3. Additional gap closure was performed on the assembled scaffold sequences by sequencing pooled BAC DNA and an 8 kb paired end library using Roche 454 sequencing platform.
Gene annotation: gene prediction Genes were annotated using the gene prediction software packages FGENESH (www. softberry. com), AUGUSTUS (http://bioinf. uni-greifswald. de/augustus/) and GENSCAN (http://genes. mit. edu/GENSCAN. html). Gene calls were also made using CLC Genomics workbench v, 7. 04. The predicted gene sequences were used for BLASTX and BLASTP analysis against protein databases (NCBI protein and Swiss-prot databases) to identify candidate genes and determine whether they were full length. Intron-exon structure of predicted genes was refined by BLAST comparisons of the predicted gene sequences with the Expressed Sequence Tag (EST) database at NCBI (http://www. ncbi. nlm. nih. gov/dbEST/).
A pooled sample containing 50% each of DNase-treated RNA isolated from leaves collected at 15 hours and five days after inoculation was sequenced by the Australian Genome Research Facility. RNA-seq was quality trimmed and aligned to the Hope Sr2 genomic sequence using CLC-Genomics Workbench. Expressed regions of the genomic sequence, for which RNA-seq reads aligned at 100% identity, were annotated by BLASTN comparison with DNA sequence databases.
In total, the 18 BAC clones formed a single contig of 968 kb, and included the distal and proximal markers RKO_1 and DOX_1 that recombined with Sr2. The same interval in CS was 567 kb long  [24]  indicating that the two haplotypes were substantially divergent and included deletion/insertion events. The DNA sequence of the 968 kb Sr2 region (Genbank KP244323) was determined by sequencing all 18 BAC clones.
Gene annotation of the Hope Sr2 genomic region sequence and comparison to CS Gene annotation of the 968 kb Sr2 region was done using gene prediction software and by comparison with the published CS genomic sequence covering the region (Genbank accession FN645450). In addition, sequences from the Hope Sr2 region that were expressed in pathogen challenged leaves of Hope were identified by alignment of RNA-seq data with the Sr2 region DNA sequence. Table  1  lists sequentially all identified genes and their sequence coordinates in the Sr2 genomic region of Hope compared to the corresponding region of the previously annotated CS sequence. The annotation identified 34 genes and gene fragments from the Hope Sr2 cosegregating region between the RKO_1 and DOX_1 flanking markers whereas only 17 genes and gene fragments were identified in the corresponding region of CS.
This section of the Sr2 locus contained several genes of unknown function (hypothetical proteins, Table  1 ) that were identified by alignment of the genomic sequence with RNA seq data. These expressed sequences were not annotated as putative genes in the published CS sequence (FN645450), and are listed with the accession number of the most similar GenBank EST in Table  1. For CA696601, CN010964, CA694126, HP626985, CN010964 and BM138011, the sequences in Hope were predicted to encode small proteins (e. g. 84 amino acids for CN010964), but in CS the putative genes were truncated. Overall, the DNA regions that encode these predicted proteins showed 80 to 92% DNA sequence identity between the Sr2 haplotypes. CD882879 encoded a predicted protein of 374 amino acids in Hope and 378 amino acids in CS.
Mapping Sr2 with respect to newly derived DNA markers To establish the region that co-segregated with Sr2, PCR markers developed from polymorphic regions of the Hope and CS sequences were mapped in the high resolution mapping family developed by Kota et al. [14]. At the proximal end of the Sr2 region, the marker from the MSF_1 gene (CS gene id ctg0011b. 00010. 1) was separated from Sr2 in two recombinants, while the marker from the RKO_1 gene (CS gene id ctg0011b. 00030. 1) Figure Dot-matrix plot of the Hope (3B) Sr2 region with the corresponding region on CS (3B; part of ctg11, GenBank FN645450). Section 1 denotes region between 45-360 kb, section two includes 360-915 kb region. was separated by one recombinant. At the distal end, RGA_1, derived from a nucleotide bindingleucine rich repeat gene (NB-LRR), was separated from Sr2 in two recombinants, while DOX_1 (CS gene id ctg0011b.
Acknowledgements We are thankful to  Xiaodi Xia  and  Sutha Chandramohan  for providing excellent technical assistance. We are grateful to our colleagues  Jarmila Číhalíková ,  Romana Šperková  and  Zdeňka Dubská  for assistance with chromosome sorting and DNA amplification. JD, HK and HŠ were supported by the  Czech Science Foundation  (award no. P501/12/G090 ) and by the grant  LO1204  from the  National Program of Sustainability  I. This project (Triple rust initiative:  CSP00161 ) is supported by the  Grain Research and Development Corporation  and is part of The  Australian Cereal Rust Control Program.
Sequences were assembled and analyzed by using ChromasPro version 1. 34 software (Technelysium Pty. Ltd. , Tewantin, Queensland, Australia). Ninety-four ticks were collected by using the dragging method; these ticks belonged to 2 species: 85. 1% (80/174) were I. ricinus ricks (43 females, 22 males, and 15 nymphs) and 14. 9% (14/94) were Rh. sanguineus adult ticks (11 females and 3 males). We screened only the 80 I. ricinus ricks. Rh. sanguineus ticks were kept alive to establish laboratory colonies for other experiments. Overall, 5. 0% (4/80) of I. ricinus ticks were positive for Borrelia spp. and 8. 75% (7/80) were positive for Rickettsia spp. Using a standard PCR specific for the flaB gene, we identified B. garinii in all ticks positive by quantitative real-time PCR (100% similarity, 736/736 bp) (GenBank accession no.
Introduction The Computational Modeling in Biology Network (COMBINE)  [1]  coordinates the development of open standards and file formats in computational systems biology and related fields. Recent decades have witnessed a major shift in biological science, with massive amounts of quantitative data increasingly being generated at high speed. Multiple sources of data, such as measurements of the activity and states of various components of cells and tissues, are being integrated into computational models that today help researchers investigate the dynamic properties of living systems. Various online resources disseminate curated information related to pathways (e. g. , WikiPathways  [2] , PANTHER pathways  [3] , KEGG  [4] , Reactome  [5] , Pathway Interaction Database  [6] ) and computational models (e.
g. , BioModels Database  [7] , Physiome Model Repository  [8] ). The growth of these resources has led to the creation of open standards to facilitate the exchange and interoperability of models and data, as well as the development of computational software tools. The Systems Biology Markup Language (SBML)  [9]  covers computational models of biological processes, describing variables, their relationships and initial conditions. BioPAX, the Biological Pathway eXchange format  [10] , focuses on the representation of biological pathways at the molecular and cellular levels. The Systems Biology Graphical Notation (SBGN)  [11]  is a set of visual languages enabling the graphical representation of biological processes. Additional standardization efforts under the COMBINE umbrella include CellML  [12] , aimed at storing and exchanging computer-based mathematical models; NeuroML  [13] , a language for the description of detailed models of neural systems; and the Simulation Experiment Description Markup Language (SED-ML)  [14] , an XMLbased format for encoding the configurations and procedures necessary to reproduce computational experiments on models.
A number of outreach activities have been implemented throughout the past several years to promote open computational standards for biology. Joint tutorials took place as 1-day satellite workshops of the International Conferences on Systems Biology (ICSB) 2012 in Toronto, Canada  [20]  and 2013 in Copenhagen, Denmark  [21]. At the core of these workshops, an international team of tutors introduced the most widely used computer tools, model databases and data management platforms in the field of systems biology and provided instructions in how these tools support COMBINE standards. Both tutorials were well attended with 80-100 participants. In September 2013, the members of the community gathered in Paris for their annual forum meeting (Fig. 1 ). The COMBINE Forum was hosted by the Computational Systems Biology of Cancer group of the Institut Curie  [22].
Of particular interest are two new additions to the SBML Test Suite  [112] : a graphical standalone desktop program that allows users to run an SBML-compatible application through a series of test cases, and an online database of SBML Test Suite results. Dr. Hucka also described how the SBML specification is still growing, and now includes many so-called packages that add constructs on top of SBML Level 3 Core, such as the ability to compose models hierarchically (using the Hierarchical Model Composition package), represent constraint-based models in a toolindependent format (using the Flux Balance Constraints package), and represent Petri net and logical models (using the Qualitative Models package). The next speaker was Frank T. Bergmann (California Institute of Technology, United States, and University of Heidelberg, Germany) who described the rationale behind the development of SED-ML  [14] , a markup language designed for encoding simulation setups, and ensuring the exchangeability and reproducibility of experiments.
Modeling physiology The session on modeling Physiology featured presentations of various initiatives to modeling physiological processes which interact with COM-BINE core standards. Randal Britten (New Zealand) presented FieldML via video link from the Auckland Bioengineering Institute. Development of FieldML is closely linked to that of CellML through the Physiome project. Ilya Kiselev (Institute of Systems Biology, Russia) presented BioUML  [75] , a simulation environment that can be used online or installed locally. BioUML pro-vides many features including integration with biological databases, visualization and scripting support. Martin Golebiewski (Heidelberg Institute for Theoretical Studies, Germany) closed the day with his presentation of how the Virtual Liver network  [76]  attempts to integrate data across different biological scales.
Community building (Day 3) Data representation and simulation Day 3 of COMBINE 2013 started with a session on how data access can be provided within simulation setups. Frank T. Bergmann summarized previous discussions that took place in meetings relating to SED-ML  [14, 80]  and in between the different working groups over the last year. He highlighted the importance of linking data to simulation experiment descriptions inside SED-ML. The lively discussion led to the conclusion that several core SED-ML components need to be given access to experimental data (the SED-ML Model, the SED-ML Task and the SED-ML DataGenerator). This extension of the current standard for simulation description would in the future enable tools to (1) encode parameterization of models from experimental data, (2) to take advantage of experimental data when executing simulation experiments and (3) to use experimental data in the postprocessing of simulation outcomes.
At the previous HARMONY meeting in 2013 it was decided that all experimental data referenced by SED-ML would be represented in NuML, the Numerical Markup Language. Joseph Dada (University of Manchester, United Kingdom) gave in his presentation an update on NuML, explaining how NuML was founded from elements of SBRML, the Systems Biology Results Markup Language  [81] , in order to facilitate its use by other standardization efforts. NuML provides a flexible structure for encoding numerical information that still is searchable and annotatable. Joseph Dada also talked about libNuML  [82] , a library for reading, writing and manipulating data in NuML format on all operating systems. Following the approach of libSBML, LibNuML is written in C++ and re-uses the XML parsing layer of libSBML. His talk was fol-Standards in Genomic Sciences lowed by a discussion on how to encode data in NuML to illustrate its structure.
The final talk of this session was given by Maciej Swat on how data is used in PharmML (see Section 3. a). PharmML already encodes several data sources, particularly in its Observation model and its Trial design. Initially, this information was saved in a tabdelimited text file that could only be understood with prior knowledge. PharmML now encodes the information in a structured manner. The session continued with a demonstration on how precisely the data is stored in PharmML, and with a comparison to NuML that had been presented before. After these presentations the remaining time was used to establish next steps. The approach discussed at HARMONY on integrating data in SED-ML was accepted so far. The next stage will be prototyping. The second simulation session focused on which new features may be required in SED-ML.