dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). These sensors are responsible for induction of the transporter genes at the appropriate glucose concentration  (Ozcan and Johnston, 1999). Both the Hxt transporters and the hexose sensors belong to the Sugar Porter, or SP, protein family (TC 2. A. 1. 1) as described in the Transport Classification Database (http://www. tcdb. org/). In addition to transporting glucose via facilitated diffusion, many of the Hxt proteins are also able to transport fructose using the same mechanism. However, some S. cerevisiae strains, such as EC1118, also contain a high-affinity fructose/H + symporter (Fsy1), which probably originated from another organism  (Galeote et al. , 2010). This protein is distantly related to Hxt but belongs to the same SP family. In addition, high-capacity, low-affinity transporters for fructose (Ffz1 and Ffz2) were recently discovered in the fructophilic yeasts Zygosaccharomyces rouxii and Z.
Identification and phylogeny of SP genes in the Y. lipolytica strains and in the Yarrowia clade Two types of genome data sets were used in this study, both obtained in our laboratory at INRA. Genomes of species of the Yarrowia clade, i. e. Y. yakushimensis CBS10253, C. galli CBS9722, Y. phangngensis CBS10407, C. alimentaria CBS10151, and C. hispaniensis CBS9996, were annotated and manually curated as described  (Meunchan et al. , 2015). Scaffolds for Yarrowia lipolytica strains H222 and W29 were automatically annotated using the manually curated E150 genome as a source (Devillers, Brunel, Morin, Neuvéglise, unpublished results). In both cases, pseudogenes were annotated. Homologues of Y. lipolytica E150 SP genes were found by BLASTp  (Altschul et al. , 1990)  with a cutoff E-value of 1e À10.
Identified SP genes in the clade species have been deposited in the European Nucleotide Archive under the accession numbers LT669770 to LT669786 and LT671678 to LT671744. Alignments of SP proteins were performed with MAFFT  (Katoh et al. , 2002)  or Clustal Omega  (Sievers et al. , 2011). Using the package Seaview v4. 4. 1  (Gouy et al. , 2010) , and following alignment editing with Gblocks if appropriate, both Neighbor-Joining and Maximum-Likelihood trees were constructed. For the latter, the LG amino-acid substitution model was corrected by a C-law distribution with four categories of evolution rates; both invariable sites and the a-parameter of the C-law distribution were optimized according to the data. Branch support was estimated with aLRT  (Anisimova and Gascuel, 2006).
The Sugar Porter family in Y. lipolytica strains E150, W29, and H222 We assessed SP genes in three different strains of Y. lipolytica. For E150, reexamination of the YETI database  (De Hertogh et al. , 2006)  after successive genome curation (Génolevures consortium) and reannotation (available on the GRYC website) eventually identified 23 intact SP genes and 3 pseudogenes (Table  1 ), in place of the previous 27 SP genes (of which 4 were pseudogenes). Specifically, an additional truncated pseudogene, YALI0A11550, was discovered whereas two pseudogenes, YALI0AC04708 and YALI0C10571, were no more predicted. To investigate the molecular basis of fructose uptake in Y. lipolytica, we mined the draft genome of strains W29 (which grows slowly in fructose) and H222 (which grows as efficiently in fructose as in glucose)  (Lazar et al.
, 2013). Transcripts of the selected genes were detected by RT-PCR: A, actin; 1 to 6, YHT1 to YHT6, respectively; D, D01111; Gl, GLK1; Hx, HXK1. of four species closely related to Y. lipolytica (namely, Candida galli, Yarrowia yakushimensis, Yarrowia phangngensis, Candida alimentaria) and one species at the root of this clade (Candida hispaniensis). All these species grow well on both glucose and fructose  (Michely et al. , 2013). We used BLAST to retrieve the SP genes and pseudogenes in each species, then constructed phylogenetic trees of the relationships among the recovered genes (Fig. S6A ). The mostparsimonious evolutionary scenario contained 11 lineages of SP genes. It revealed that the final number of SP (pseudo)genes was shaped in each species by several duplication or loss events (Fig.
Acknowledgments We thank  Prof. Eckhard Boles  (Goethe-Universität Frankfurt a. M. ) for S. cerevisiae strain EBY. VW4000, and  Dr. Markus Künzler  ( ETH Zurich ) for the plasmids we used to express genes in S. cerevisiae. Many thanks to trainees  J. Ralèche  and  K. Auzou  for their interest in the subject and lab assistance, to  S. Thomas  for initiating graphical representation of drop tests, and to  Jessica Pearce  and  Lindsay Higgins  for their language editing services.
Funding This work was supported by the  French National Research Agency ,  Investissements d'avenir program  [grant  ANR-11-BTBR-0003 ] and the  French National Institute for Agricultural Research (INRA). Z. Lazar  received financial support from the  European Union  in the form of an  AgreenSkills Fellowship  (Grant Agreement No. 267196 ;  Marie-Curie   FP7 COFUND People Program ). Travel fees for short scientific visits of members from both units were funded by  PHC Polonium  [grant  29068RE ].
Analytical variability. As a first step in the characterisation of the stability of child metabolic phenotypes, analytical variability of NMR data was assessed to inform on the robustness and stability of the NMR platform. Repeat analysis of a representative pooled sample (quality control, QC sample) was conducted. The percentage coefficient of variation (CV%) of the QC sample was calculated for integrals of NMR signals representing individual metabolites (raw integral divided by the internal reference, before creatinine normalisation) and showed an average CV% of 7. 2% and median 7. 7%. Metabolites with a low signal to noise ratio (S/N) in QCs, such as N-methylpicolinic acid, 3-aminoisobutyrate, N 1 -methylnicotinamide and acetone, presented a CV% over 10%. Other metabolites, such as lysine, hippurate, trimethylamine-N-oxide (TMAO), showed the best analytical stability with a CV% below 3%.
Methods Study population. The Human Early-Life Exposome (HELIX) project aims to integrate novel exposure assessment and 'omics' technologies to characterise early-life exposure to multiple environmental factors and associate these with child health outcomes  37. HELIX comprises 6 existing birth cohort studies (32,000 mother-child pairs) across Europe, of which 1,200 mother-child pairs were selected for phenotyping including exposure and 'omics signatures. Smaller nested panel studies (n = 150 children and pregnant mothers) collected in-depth personal exposure data and repeat biological samples for a weekly period across two seasons. This study focused on a subset of 20 healthy children from the Spanish part of the panel study, nested with the INMA (INfancia y Medio Ambiente) cohort in Spain  38.
For each sample, 128 transients were collected into 64 K data points using a spectral width of 12,000 Hz with a recycle delay of 4 s, a mixing time of 100 ms, and an acquisition time of 2. 73 s. A line-broadening function of 0. 3 Hz was applied to all spectra prior to Fourier transformation. All 1 H NMR spectra were automatically phased and baseline-corrected using Topspin 3. 2 software (BrukerBioSpin, Rheinstetten, Germany). The 1 H NMR spectra of urine were referenced to the TSP-d 4 resonance at 0 ppm. Data processing. NMR spectra were imported into the Matlab 2014a (MathWorks, Massachusetts, US) computing environment, and were aligned using the recursive segment-wise peak alignment method, an algorithm based on cross-correlation  39. The pool QC sample spectrum was used as reference for alignment.
For the standard inversion-recovery pulse sequence experiment (180° pulse -τ -90° pulse), variable relaxation delay, τ , was chosen logarithmically to cover values from 0. 001 to 5 seconds. For each τ, 4 transients were collected into 64 K data points using a spectral width of 12,000 Hz with a recycle delay of 32 seconds  40. T 1 values are presented in SI Table  1. = × × M Standard I I N N C [ ] [ ] (1) Of the 44 identified metabolites, 18 of them were of low abundance or were only detected in a subset of samples, and therefore reliable estimation of their longitudinal relaxation time (T 1 ) was not possible. Metabolites were included in the subsequent data analyses based on quantified values (n = 26) and semi-quantified integral values (n = 18) and were normalised to creatinine. Finally, metabolite levels were logtransformed for the ICC calculation and the decomposition of variance analysis, in order to meet assumptions of normality and homoscedasticity in statistical tests and to minimise the influence of extreme values.
Metabolite annotation. Assignment of endogenous urinary metabolites was made by reference to published literature data  41, 42  , online databases (HMDB)  43  , statistical total correlation spectroscopy (STOCSY)  44  and using ChenomxNMRsuite profiler (ChenomxInc, Edmonton, Canada) and/or confirmed by 2D NMR experiments on a selected sample including homonuclear 1 H-1 H correlation spectroscopy (COSY),  1  H-1 H total correlation spectroscopy (TOCSY) and 1 H-  13  C NMR heteronuclear spectroscopy (sample selected based on high abundance of doublet at 8. 72 ppm). Spike-in experiments using authenticated chemical standards were required for certain final metabolite annotations.
ICC is a measure of the reliability of repeated measures over time, defined as the ratio of between-subject variance to total (between-subject plus within-subject). Between and within-subject variance was calculated from the mean squares in the analysis of variance using one-way ANOVA fixed effects model using 'psych' R package 'ICC' function (from CRAN repository). We report the ICC for Single_raters_absolute  45. The relative importance attributed to between-individual and diurnal variations were estimated across all metabolites based on variance decomposition models. Using linear mixed effect models, by-subject random slopes were modelled for within-subject factors. The time of the day (morning/night; diurnal) was added as a fixed effect. This regression model was calculated for each metabolite using the R package 'lme4' and the function lmer  46.
Acknowledgements This research received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no 308333 -the  HELIX  project. We would like to thank also all the study participants and the full roster of the  HELIX  Project Investigators that can be found at http://www. projecthelix. eu/es/sobre-helix/beneficiarios. We also would particularly like to thank  Xavier Basagaña  for his statistical advice.
Subjects The current investigation was a observational study of elite Gaelic football players competing at the highest level of competition (National League Division 1 and All-Ireland). Data were collected for 22 players (Mean ± SD, age: 24. 3 ± 6. 1 years; height: 180. 2 ± 7. 3 cm; mass: 81. 6 ± 7. 5 kg) across a one-week training camp during the competition season. The senior level playing experience of the current squad was 8. 5 ± 4. 3 years. The study was approved by the local institute's research ethics committee and written informed consent was obtained from each participant.
Training data was analysed post session with retrospective analysis conducted on all sessions. Each file was then trimmed so only data recorded during each session and specifically each drill when the player was on the field was included for further analysis. Data were exported into a customised spreadsheet (Excel, Microsoft Redmond, USA). This spreadsheet allowed for the analysis of distance covered in the following categories: total distance (TD; m); high speed running (≥17 km•h -1 , HSD; m), sprint distance (≥22 km•h -1 ; SPD; m), and maximal velocity (km•h -1 ).
To account for the effect of fatigue/wellness on changes in running performance, these relationships were adjusted to reflect any changes in fitness measures (HRex; HRR; LnSD1) with partial correlations. The following criteria were used to interpret the magnitude of the correlation (r) between the different measures: ≤0. 1, trivial; >0. 1-0. 3, small; >0. 3-0. 5, moderate; >0. 5-0. 7, large; >0. 7-0. 9, very large; and >0. 9-1. 0, almost perfect. If the 90% CL overlapped positive and negative values, the magnitude was deemed unclear  (Hopkins et al. , 2009). Statistical analyses were performed using SPSS for Windows (Version 22, SPSS Inc. Chicago, IL, USA) with statistical significance set at an accepted level of p<0. 05. shown in Figure  2. There were significant day-to-day variations in training load measures (Co-efficient of variation, CV: 51%; p ≤ 0.
These trees were planted and measured during several years in an experiment reported by  Segura et al. (2008). Briefly, 246 trees (two replicates) were planted in March 2004 at the Melgueil INRA Montpellier experimental station 5 x 1. 5 m apart in an east-west orientation in six-tree microplots randomly scattered throughout the field. All the trees were grown with minimal training, that is, they were not pruned and the trunks were staked up to 1 m. They were regularly irrigated using a microjet system to avoid soil water deficits. Pests and diseases were controlled by conventional means in line with professional practices throughout this study. This dataset provides 6,150 average measurements from the individual trees for the four geometrical traits. The interval of variation for each trait is reported in Table  1.
A report that summarizes the equations and parameters used in the model is available as supplementary information. The source code is available on INRA software repository (git, tag 1. 4, commit SHA 4d5c30d3) and the VLE-RECORD environment  (Bergez et al. , 2013; Quesnel et al. , 2009)  is used as simulation platform. SUNFLO was evaluated on both specific research trials (40 trials, 110 plots) and agricultural extension trials that were representative of its targeted use (96 trials, 888 plots). Over these two datasets, the model was able to simulate significant G × G interaction and rank genotypes  (Casadebaig et al. , 2011; Casadebaig, Mestries, et al. , 2016). The prediction error for grain yield was 15. 7% when estimated over all data (9% -30% in individual trials). From these two evaluations, it was determined that SUNFLO is accurate enough to support optimization methods, i.
To capture basic climate variability in these models, data was collected from five diverse sunflower production regions in France in 2012 (Avignon, Toulouse, Reims, Poitiers, and Dijon) using neighboring stations (< 5km) of the French meteorological network (Meteo-France). In summary, denoting c 1 , c 2 , c 3 , c 4 , c 5 the five climatic conditions, the performance can be defined as: P (x) = 1 5 5 i=1 Yield(x, c i ), (2) with x = {T DF 1, T DM 3, T LN, LLH, LLS, K, LE, T R} (see Table 2). A collection of 89 sunflower hybrid cultivars was phenotyped for phenology (two traits), architecture (four traits), and response to abiotic stress (two traits)  (Casadebaig et al. , 2011; Lecoeur et al. , 2011). The corresponding model parameters are presented in Table  2. The minimal and maximal bounds were determined for 89 hybrids and assumed to represent the cultivated genetic diversity.
Feasibility Datasets of observed phenotypes were available for both models and used to define a function that indicates the likelihood of a new phenotype. These measures provide us with a rough indication of the domain of potential existence of the traits combinations (both in terms of bounds and co-occurrence), and a simulated phenotype may be considered less unrealistic as it is far from the cloud of observations. Therefore, we require a function that measures the proximity of a simulated phenotype to the observed phenotypes. A simple yet sensible solution consists in fitting a multivariate probability density function to the observations. Such a function would be maximal at the center of the observation cloud, decrease when moving away from the center, and naturally take into account the correlations between traits.
This metamodel is then used as a guide to sequentially choose the most interesting simulations to run (as in the classical EGO algorithm for single objective problems of  Jones et al. , 1998). The optimization process relied on the R statistical computing environment (R Core Team, 2016) and R package DiceKriging (v1. 5. 5,  Roustant et al. , 2012)  and GPareto (v1. 0. 3, Binois and Picheny, 2016). 50 initial simulations were based on a latin hypercube design  (McKay et al. , 1979)  to build the initial metamodel and 50 simulations were iteratively added to the experimental design (for a computational time around 24 hours). For SUNFLO, population-based optimization algorithms that require an important number of simulations were used, since computational time was not critical and such algorithms are known to be efficient and reliable  (Collette and Siarry, 2003).
Discussion The use of phenotypic measurements of existing cultivar populations and simulation-based optimization allowed us to virtually recombine plant traits to identify a set of optimal phenotypes that maximize performance and feasibility (Figure  2 , Tables  3 4 ). This result is first discussed from a biological perspective, i. e the morphological and physiological characteristics in the optimal set of phenotypes for apple tree and sunflower. We then discuss the influence of the input dataset on the outcome of our approach and how the feasibility criterion could be defined in relation to the type of genetic material (e. g recombinant inbred lines or hybrids). Finally, we discuss how this multi-objective optimization method is a less data-intensive alternative than emergent molecular breeding methods such as genomic selection or gene-based modeling to estimate the breeding value of plant phenotypes.
Is the feasibility function a reasonable representation of the biologic reality? The feasibility function is entirely dependent on the dataset at hand. This implies that the diversity of optimal phenotypes selected by the optimization process results from the trait variance present in the considered population. This variance corresponds to heterosis (biparental progeny), in which case it also depends on chosen parents, or to genetic variability (collection of hybrids). When more polymorphisms are available, with multiple alleles at a locus, more diverse phenotypic traits can be obtained. The genetic distance between the parents will also impact this capacity; more distantly related parents increase the chance of different alleles at a locus. In this study, the variation observed in the apple tree case resulted from a biparental segregating population with low parental relatedness  (Allard et al.
Acknowledgements The authors are grateful to the students ( Ewen Gery ,  Bertrand Haquin ,  Claire Barbet-Massin ) and staff from  INRA  and  ENSAT (Colette Quinquiry ,  Michel Labarrère ,  Pierre Maury ) and  Terres Inovia (Pascal Fauvin ,  Philippe Christante ,  André Estragnat ,  Emmanuelle Mestries ) that helped to constitute the phenotypic database, helped in modeling and simulation (RECORD team from  INRA ,  Helène Raynal ,  Eric Casellas ,  Gauthier Quesnel ) and provided climatic datasets (AgroClim team from  INRA ). We also thanks  V. Segura ,  S. Martinez  and  G. Garcia  for apple tree phenotyping, the  OpenAlea team  for their support in model development and  L. Han  for the first step of sensitivity analyses. We are also grateful to  Jennifer Mach  and  Shawn Carlson  for their insightful inputs while editing the manuscript.
Compound 6s is eluted first on silica gel and is isolated in 29% yield with high purity. The purification of the second diastereomer 6a is more difficult and requires additional purification steps as described in the experimental section. Compound 4 (6a) is obtained with the same yield (29%). Compounds 6a and 6s have been fully characterized by 1 H, 13 C NMR spectroscopy and HRMS. 1 H NMR and 13 C NMR spectra of compound 6a and 6s are given in Supporting Information (Figures S1-S4 in the Supporting Information). X-Ray structures of compounds 6a and 6s: Single X-ray quality crystals have been obtained for both diastereomers 6a and 6s in a mixture of chloroform and ethanol. Their crystallographic data of are summarized in Supporting Information (Table  S5  in the Supporting Information). Both compounds crystallize in a P21/n space group.
X-ray crystallography: Suitable crystals for all four compounds were selected and mounted on a Gemini kappageometry diffractometer (Agilent Technologies UK Ltd) equipped with an Atlas CCD detector and using Cu radiation ( l= 1. 5418 Å). Intensities were collected at 150 K for anti-5 and syn-6 by means of the CrysalisPro software Reflection indexing, unit-cell parameters refinement, Lorentz-polarization correction, peak integration and background determination were carried out with the CrysalisPro software. [22]   An analytical absorption correction was applied using the modeled faces of the crystal. [23] The resulting set of hkl was used for structure solution and refinement. The structures were solved by direct methods with SIR97 and the least-square refinement on F 2 was achieved with the CRYSTALS software.
Corresponding fluorochrome-labelled isotype control antibodies were used for staining controls. Cells, resuspended in 300 ll of buffer (0Á15 M NaCl, 1 mM NaH 2 PO 4 H 2 O, 10 mM Na 2 HPO 4 2H 2 O and 3 mM NaN 3 ) were analysed in a flow cytometer (Becton Dickinson, Heidelberg, Germany) using the corresponding CELL QUEST software.
Statistical analyses All data were analysed by SPSS 17. 0 (IBM Corporation, Armonk, New York, USA). The results are presented as means AE SD. Normality of data was assessed by D'Agostino & Pearson and Shapiro-Wilk test. For normally distributed groups of data, one-way analysis of variance followed by Bonferroni's post-test or unpaired two-tail Student's t-test were used to compare the differences between groups. Significance was defined as P < 0Á05 for all tests, except those subsequently corrected by Bonferroni.
Results We obtained sequence data for a total of 166 Pteronymia specimens, representing 41 of the species recognized prior to our revision, and 47 of the species recognized after our revision (Table  1 ; see Supplementary Table  S1 ). Species with no molecular data were P. alcmena, P. alicia, P. calgiria, P. fumida, P. glauca and P. peteri. A total of 87 morphological characters were coded for 52 species (after taxonomic revision, see below). Taxonomy. Molecular phylogenies of all Pteronymia specimens generated by maximum likelihood and Bayesian inference were largely congruent (see Supplementary Figs  S1-S2 ), and showed that the genus Pteronymia is monophyletic. Several internal nodes had moderate to low support. The molecular data suggested that several changes to the species-level taxonomy were warranted.
asopo (rev. stat. ). The status of several other recently described  40  Venezuelan taxa of P. oneida remains to be determined, and for the moment they are retained in P. oneida. Pteronymia picta (Salvin, 1869). This species formerly included taxa ranging from Costa Rica to central and western Colombia. Specimen locality data from Colombia are too imprecise and unreliable to confirm whether the Colombian P. picta dispar Haensch, 1905, apparently restricted to the northern Cordilleras Occidental and Central, is sympatric or not with P. picta picta, the range of which appears to broadly encompass that of dispar in Colombia. The molecular data suggest that P. dispar (rev. stat. ) is not closely related to P. picta picta and Central American P. picta notilla Butler & H. Druce, 1872. After our taxonomic changes the genus Pteronymia now comprises 53 species, making it the most diverse ithomiine genus.