dataesr/scientific-paragraphs-categorization huggingface dataset:
Abbreviations of locations can be found in Table  1. Lineage 1 and 2 are represented in pink and blue respectively Fig. 2  Bayesian phylogeny reconstructed with cytochrome b dataset. The support, posterior probabilities determined with MrBayes, is indicated for each node. Only the nodes with a support above 50 were retained. Blue stars indicate European samples southern populations (p = 0. 01) but not from those to the north (p = 0. 09). Chinese populations showed a higher genetic diversity, with a Hobs average with and without the two invasive Chinese populations (S19 and S20) of 0. 66 and 0. 65 respectively. The average number of allele per locus with and without S19 and S20 was 12. 05 and 11. 67 respectively. European populations had a lower genetic diversity with a mean Hobs average of 0.
Acknowledgements The authors thank  Heinke Buhtz  and  Conny Burghardt  for their excellent technical expertise and are very thankful to  Diethard Tautz  and  Arne Nolte  for discussion. David Fletcher ,  Rodolphe E. Gozlan  and  Robert Britton  were supported by the  Agence Nationale de la Recherche  ( GENESIS ANR-AF 13-ADAP-0005-001 ). The authors would like to thank the two anonymous reviewers and  Prof. Aibin Zhan  who help improving the manuscript. This work was funded through a  Small grant  from the  Fisheries Society of the British Isles  to  Emilie A. Hardouin.
Microscopy Developing grain sampled at two timepoints during grain development were fixed in 4% paraformaldehyde, 2. 5% glutaraldehyde fixative, dehydrated in a graded ethanol series and embedded in medium-grade LR White Resin as described by  Tosi et al. (2011). Sections of 1 µm thickness were cut and stained for protein with 1% Naphthol Blue Black in 7% (v/v) acetic acid for 30 s. Images were captured at 20× magnification on a Zeiss Axiophot light microscope (Zeiss, Oberkochen, Germany), and stitched into high-resolution composite images using MetaMorph Microscopy Automation and Image Analysis Software (Molecular Devices, Sunnyvale, CA, USA) and Adobe Photoshop (Adobe Systems Inc. , San Jose, CA, USA). Composite images of the entire endosperm were then analysed.
Image analysis Light-microscopy images of developing wheat grain stained for protein were analysed using a custom toolbox in ArcMap™, part of the ArcGIS™ 10. 4 software package (ESRI®, Redlands, CA, USA) (available as an ArcGIS™ python toolbox, doi: 10. 5281/ zenodo. 1066914). Images were loaded (Fig. 1A ), and an outline was manually drawn around the endosperm of the grain, just within the aleurone layer (Fig. 1B ). Two methods of image analysis were then run simultaneously: protein concentration distribution analysis, and protein body size distribution analysis. Protein concentration distribution analysis describes the overall gradients in protein concentration, whilst protein body size distribution analysis measures the area of each individual protein body, and records the distance of that protein body from the outline drawn around the endosperm.
To describe the gradient in protein within the endosperm, protein concentration distribution analysis was used to measure and compare protein concentration in five zones within the endosperm. Five concentric bands were automatically rendered inwards from the endosperm outline to create five zones of equal width covering the range of outer to inner endosperm (Fig. 1C ). These zones were then overlaid on the extracted protein body area data as determined by maximum likelihood classification (Fig. 1D ), and protein within each zone was measured. The result is a measurement of protein concentration on a by-area basis within each of the five zones in the endosperm (Fig. 1E ). The use of five zones was determined to be optimal through experimentation with different numbers of zones (from three to seven) to find the number that was geometrically optimal to describe the gradient in protein concentration via best-fit regression modelling of total protein body area per zone versus zone number (data not shown).
Data processing Post-analysis, a conversion was applied to the data collected from the protein concentration gradient analysis: nitrogen content measurements made on grain sampled from the same plant and at the same timepoint were used to calculate a conversion factor between actual grain protein content and protein content as determined through microscopy-image analysis. A unique conversion factor was calculated for each combination of treatment and experimental replicate. This conversion factor was applied to all collected data to account for any differences in protein detection efficiency between treatments, since any changes in grain water content or protein density within a protein body would result in inaccuracies in the measurement of protein concentration on a by-area basis. The equation used for the conversion was as follows:.
Statistical analysis The GenStat® statistical software (2015, Eighteenth Edition, VSN International Ltd, Hemel Hempstead, UK) was used to analyse the effect of post-anthesis temperature and nitrogen fertiliser level on the total grain count, nitrogen yield, thousand grain weight, and total grain yield by ANOVA. After inspection of F-tests for the main effects and interactions between the treatment factors, the least-significant difference at the 5% (P=0. 05) level calculated from the standard error of the difference between means on the residual degrees of freedom from the ANOVA was used to make comparisons of relevant means. GenStat® was used to fit a linear mixed model to both the distribution of protein concentration over the calculated zones, and to the protein body size distribution data using the method of restricted maximum likelihood.
Trendlines represent the result of the linear mixed modelling analysis (using restricted maximum likelihood), which was applied to the entire dataset, whilst data points represent mean protein concentration values within each zone from each of the three experimental replicates analysed. Underlying biological variation for entire dataset, s 2 =0. 178. Downloaded from https://academic. oup. com/jxb/article/69/12/3117/4961347 by KIM Hohenheim user on 22 April 2022 of temperature and nitrogen treatment at both the early and late sampling timepoint.
This shows how the effect of decreased nitrogen has a twofold impact on grain yield: reducing both Fig. 4. Results of protein body size distribution gradient analysis. Grain sampled at timepoint 1 with low-nitrogen treatment (A) and high-nitrogen treatment (B), and at timepoint 2 with low-nitrogen treatment (C) and high-nitrogen treatment (D). Dark grey squares and long-dashed lines represent the 28 °C treatment, and light grey circles and short-dashed lines represent the control 20 °C treatment. The graphs in (A-D) show results of the same model, with portions of the results being separated for clarity. Trendlines represent the result of the linear mixed modelling analysis (using restricted maximum likelihood), which was applied to the entire dataset. This analysis found that separate linear trends was the best statistical representation of the temperature by nitrogen by timepoint interactions.
Acknowledgements Rothamsted Research receives strategic funding from the  Biotechnological and Biological Sciences Research Council (BBSRC)  as part of the 20: 20 Wheat (BBS/E/C/00005202 ) and  Designing Future Wheat  ( BBS/E/C/000I0220 ) projects. George Savill  is a PhD student with the  University of Reading , and is funded by the  BBSRC ,  Lawes Trust , and the  University of Reading. The authors wish to thank  Peter Shewry  for many helpful suggestions.
Statistical analyses Graphs and statistical analyses were performed with the GraphPad Prism software. Comparison of PVL and of age between the different groups was performed using non-parametric unpaired T test (Mann-Whitney test). Comparison of the PVL in the saliva and in PBMCs was performed using non-parametric paired T test (Wilcoxon test, Ã Ã Ã = p-value < 0. 001; Ã Ã Ã Ã = pvalue < 0. 0001 and ns = not significant). Comparison of frequencies was performed by a χ 2 test.
RM and CJ are supported by  ENS Lyon. JT was supported by the  Fondation ARC pour la Recherche sur le Cancer. SA is supported by  INSERM. BJ is supported by  ANR Labex Ecofect. This work was supported by  ARC ,  Labex Ecofect and La Ligue Contre le Cancer  " programme E ´quipe Labellisee ". The funders had no role in study.
1e ). Data analysis. Lick rate was acquired at 1 kHz. Mean anticipatory licking was calculated for each trial as the mean lick rate in the 1 s delay period between odor presentation and water delivery. The differential lick rate (Δ lick rate) was computed as the difference of mean anticipatory licking between two consecutive trials, within a training day. For GCaMP activity, we focused our analysis on the phasic responses. Indeed, a majority of previous work has shown reward prediction error-like signaling in the phasic responses of dopamine neurons and technical limitations such as bleaching limit our ability to monitor long-timescale changes in baseline using calcium imaging. Fluorescence data was acquired at 1 kHz. For each trial, the relative change in fluorescence, dF/F = (F -F0)/F0, was calculated by taking F0 to be the mean fluorescence during a 1 s period before the odor presentation, such that the fluorescence measured at each time point within a trial is corrected by the average fluorescence during the 1 s period before odor presentation for that given trial.
5 ), for illustration purposes to summarize the data in one plot, each mouse's mean US response across rewards was normalized by min-max normalization when pooled together. The normalization was performed within each mouse, using the given mouse's trial 1 response as reference for the minimum and maximum values for the min-max normalization such that y = (xmint rial1 )/(max trial1min trial1 )) (Fig. 3c  and g , Supplementary Figs. 5-11). Of note, the models were not fit on the min-max normalized data but directly on mice's individual baseline-corrected GCaMP activity. Polynomial fits to the dopamine neuron activity and behavior were performed using the polyfit function in MATLAB. Computational modeling. Standard RL: We used a simplified version of the temporal difference (TD) model  11  , modeling stimuli and rewards at the trial level instead of in real time.
Acknowledgements We thank members of the  Gershman and Uchida labs  for insightful discussions;  S. Matias ,  Z. Mainen  ( Champalimaud Institute of Unknown ),  C. Burgess ,  M. Andermann  ( Harvard Medical School ), and  M. W. Mathis  for advice on fiber photometry;  Edward Soucy  (CBS neuro-engineering platform) for instrumentation assistance;  C. Dulac  for sharing resources; and  V. Jayaraman ,  R. A. Kerr ,  D. S. Kim ,  L. L. Looger , and  K. Svoboda  from the  GENIE (Genetically-Encoded Neuronal Indicator and Effector)  Project at the.
5. 1 Surface cluster calculations. Surface cluster calculations were carried out employing correlation-consistent basis sets such as the (augmented) polarized correlation-consistent triple-z (aug-cc-pVTZ) basis of Woon and Dunning, Jr. ,  53  as reported in ref. 33 for oxygen and titanium atoms, while the aug-cc-pVTZ-PP basis was used for silver,  54, 55  including a smallcore (19-valence-electron) relativistic pseudopotential. 56 When applying the DLPNO-CCSD(T) and PBE-D3 treatments, the ORCA package of programs  57  was employed (version 4. 0. 1. 2). The SAPT(PBE) calculations were performed with the MOLPRO package  58  using the density-fitting technique  39  and adopting the same computational setup as ref. 33. The exchange-correlation PBE potential was asymptotically corrected  59  with the ionization potential value reported in the NIST Chemistry Web Book for the silver dimer (7.
66 eV from ref. 60). For the cluster model of the TiO 2 (110) surface, an ionization potential value of 5. 0 eV was chosen on the basis of work function values characterizing TiO 2 (110) surfaces  61  (in between 4. 9 and 5. 5 eV). 2. 5. 2 Periodic calculations. To perform the periodic calculations, the computational setup reported in a previous study of the He/TiO 2 (110) interaction  31  was applied. For this purpose, we used the Vienna Ab initio Simulation Package (VASP 5. 4. 4). 62, 63 o describe the electron-ion interaction, the projector augmentedwave method was applied,  63, 64  using the PAW-PBE pseudopotentials supplied with the same code. The electrons from O(2s, 2p), Ti(3s, 4s, 3p, 3d) and Ag(4d, 5s) orbitals were treated explicitly as valence electrons. As in ref. 31, we used plane wave basis sets with a kinetic energy cutoff of 700 eV and a Gaussian smearing of 0.
Acknowledgements This work has been partly supported by the  Spanish Agencia Estatal de Investigacion (AEI)  and the  Fondo Europeo de Desarrollo Regional (FEDER, UE)  under Grant No. MAT2016-75354-P  and  COST Action   CM1405  '' Molecules in Motion '' (MOLIM). The  CESGA super-computer center (Spain)  is acknowledged for having provided the computational resources used in this work. Work by  D. A. M. has been partly supported by the  National Science Foundation of the USA  under grant. no. CHE-1445825  and by the  HPC Computing Center of the University of Florida. MPdLC is greatly thankful to  Arturo M. Lopez-Quintela  for very helpful suggestions and discussions.
10, 13, 14, and PDB code 2I3F), defining two subfamilies, GSL-specific and C1P-specific. Modeling of FAPP2-GLTPH relative to the GLTP structure shows conservation of residues required for sugar headgroup binding  (2). However, FAPP2 prefers simple GSLs. The in vitro transfer of GlcCer and GalCer by FAPP2-GLTPH domain is equally efficient but is reduced by ϳ50% with LacCer and is slower yet with complex GSLs, such as ganglioside GM1 that possess five sugars including N-acetylneuraminic acid, or sulfo-GalCer, sulfatide (SF)  (2). By contrast, human GLTP efficiently transfers both complex and simple GSLs  (6, 7, 9) , as reviewed in Ref. 11. Crystal structures of different holo forms of human GLTP provide insights into the structural features responsible for the recognition of GalCer, GlcCer, LacCer, and SF.
1a ) resembles GLTP complexed with 18:1 GlcCer (PDB code 3S0K)  (15)  with respect to lipid position and binding mode. The recognition center (Fig. 1b ) displays a similar network of interactions as GLTP for anchoring the polar region of GalCer  (16)  with Trp 407 serving as a stacking plate for the GSL sugar, whereas Asp 360 , Asn 364 , Lys 367 , and His 445 form hydrogen bonds with the galactose-amide moiety. In addition, the C-terminal Val 519 makes tight van der Waals contacts with two hydroxyls and the ceramide C1 atom, whereas residue Glu 403 contacts with the sugar C6Ј atom (Fig. 1b ). Both nonpolar aliphatic chains are encapsulated by GLTPH via the so-called "sphingosine-in" binding mode (Fig. 1a ) previously observed for human GLTP bound by GSLs containing an N-oleoyl-acyl chain  (12, 15).
PDB code 5KDI How FAPP2 selects simple GSLs using the GLTP-fold bers of the GLTP superfamily with known 3D structures is shown in Fig. S1a. Differences between ACD11 versus CPTP and CPTP versus FAPP2-GLTPH are highlighted in Fig. S1 (c  and d ). Poststructural sequence alignment of all six proteins (Fig. 2b ) indicates conserved/semiconserved residues of the GLTP fold. Key recognition center residues (Fig. 2b , blue letters) identify five conserved positions in the GSL-specific subfamily and five conserved positions in the C1P-specific subfamily, four of which coincide in both subfamilies. Together they define the basic six-point pattern for the recognition center in the GLTP fold, with spatial templates DNKxWH and DKxRRH (where x indicates a nonconserved residue) characterizing the GSL-specific and C1P-specific families, respectively (Figs.
To test the idea, we modeled the FAPP2⅐LacCer and FAPP2⅐SF complexes by superposition of the current crystal structure with GLTP⅐18:1-LacCer (PDB code 1SX6) and GLTP⅐3-O-SF (PDB code 3RZN and 4H2Z). In Fig. 5 (b, c, e,  and f ), the protein molecule is represented by FAPP2-GLTPH domain from the current crystal structure, but GalCer is replaced by LacCer or SF from the superimposed GLTP complexes. Because the ID loop is rigid, such models allow one to assess whether the loop and the GSL headgroup will collide in the potential complexes. For reference, the original structure of FAPP2-GLTPH⅐GalCer is shown in Fig. 5 (a  and d ). The protruding region of FAPP2 ID loop (R398 and Color codes for GSL atoms are defined in Fig. 1. b, poststructural sequence alignment of six proteins belonging to the GLTP family (see Fig.
In FAPP2_A, Lys 358 adopts the double conformation (orange 1 and 2), which either does not a salt bridge with Asp 347 (orange 2) or results in a bridge that differs from that of in FAPP2_B (orange 1). f, superposition of 17 GLTP molecules from 12 PDB entries showing the gate-region with the ⌽ 3 -residue (Leu 37 ) of the ⌽ 1 ⌽ 2 DX⌽ 3 GX?XF motif that identifies three conformational groups for GLTP, colored in green, yellow, and cyan and three representative GLTP molecules (g) from visualizing the conformational distinctions between the three groups (f).
How FAPP2 selects simple GSLs using the GLTP-fold Phe 354 and Leu 349 (⌽ 3 )) as well as by variations in Asp 347 ⅐Lys 358 bridging. In FAPP2_A, Lys 358 adopts a double-conformation (1 and 2), which either (conformer 2) does not contribute to the salt bridging with Asp 347 , or (conformer 1) results in a bridge that differs from that of in FAPP2_B. To address the issue of whether sensor conformations correlate with Asp⅐Lys bridging in the GLTP fold, we analyzed via superimposition of 17 molecular structures derived from 12 PDB entries (Fig. 7f ). The side chains shown in Fig. 7f  include the ⌽ 3 residue, Leu 37 , and two salt bridge residues, Asp 35 and Lys 46. The superposition identifies three conformational groups of GLTP, colored cyan (five molecules), yellow (six molecules), and green (six molecules), while Fig.
7g  emphasizes the distinctions between these three groups by showing three representative molecules from Fig. 7f. Two groups (cyan and yellow) share a similar Leu 37 conformation (conformer 1) but differ by Asp 35 position resulting in Asp oxygen "switching" in the Lys 46 ⅐Asp 35 salt bridge. The third group (green) displays a distinct Leu 37 conformation (conformer 2) as well as changed Cys 36 and Asp 35 positions (Fig. 7g ). The Asp 35 carboxyl protrudes toward the Lys 46 residue, which repositions to avoid clashing with Asp 35. The resulting Lys 46 ⅐Asp 35 salt bridge distance increases from 2. 6 -3. 2 Å (first two groups) to 3. 1-4. 0 Å (third group), indicating salt bridge weakening. Bridge strength "recovery" is observed in the special conformation of the GLTP gate (PDB code 3S0K), where Asp 35 protrudes even more toward Lys 46 because of a conformational switching of the conserved Gly 38 of the L1/2 loop (Fig.
Protein mutagenesis, expression, and purification Human FAPP2 GLTPH-domain (C-terminal 212 residues ϭ FAPP2-C212) was derived from PLEKHA8 ORF (519 residues; GenBank TM accession number MF465867) (2). Molecular cloning, expression, and initial purification of FAPP2-GLTPH, human GLTP (GenBank TM accession number AF209704), and all mutants involved fusion with the N-terminal His 6 -SUMO tag, which was then removed by proteolysis to achieve final purification (2). E377A/E378K double mutant and point mutations E403L and K367S were produced by QuikChange sitedirected mutagenesis kit (Stratagene) and verified by sequencing. Cloning of chimeric human GLTP, ID FAPP2 GLTP, and chimeric human FAPP2-GLTPH, ID GLTP FAPP2, containing swapped ID loops involved replacing the ID loop of GLTP (YGAEWPKVG) with that (DVAQVRNS) of FAPP2 and vice versa.
Structure determination and refinement Crystals of FAPP2-GLTPH complexed with 18:1-GalCer belonged to the P2 1 2 1 2 1 space group and contained two molecules in the AU. The structure was determined by molecular replacement, using the MOLREP program (29) against X-ray data cut at 2. 3 Å resolution and protein structure from the GLTP/24:1-GalCer complex (PDB code 2EUK) (  16 ) as a search model. Found solution was prerefined with REFMAC (30) at 3. 0 Å resolution to R work /R free ϭ 0. 53/0. 55 and used as the initial model in ARP/wARP automated model building procedure intermittent with REFMAC refinement cycles  (31). The model building performed against X-ray data cut at 1. 8 Å resolution has been completed with protein chains built by 89% and refined to R work /R free of 0. 246/0. 294 for the resolution range 15.
0 -1. 8 Å. Insertions of two lipid molecules and missing residues of polypeptide chains were made manually using electron density maps in COOT program. Further refinement to R work / R free of 0. 131/0. 179 was executed using a full data set up to 1. 45 Å resolution with REFMAC alternating with minor model rebuildings in Coot. ARP/wARP was used to add solvent molecules  (32). The final structure was validated by PROCHECKm and coordinates have been deposited in the Protein Data Bank (code 5KDI). Data collection and model refinement statistics, along with unit cell dimensions, are summarized in Table  1.
The addition of Tween 20 detergent after extended incubation provided a measure of maximum intensity achievable by "infinite" fluorophore separation. Nonlinear regression analyses using ORIGIN 7. 0 software enabled quantification of the initial lipid transfer rate, 0 , for the first-order exponential transfer process. Standard deviations were calculated at 95% confidence interval. R 2 values for all estimates were Ͼ0. 96.
d, substitution of the salt-bridged Asp⅐Lys pair in GSL-specific FAPP2-GLTPH for the H-bonded Asn⅐Ser pair in C1P-specific CPTP (PDB code 4K84) highlighting the preservation of the clasp (see Fig. S4efor unique Gly⅐Glu pair of ACD11). e, ⌽ 1 ⌽ 2 DX⌽ 3 GX?XF motif conserved in the GSL-specific family (⌽ i ϭ bulky hydrophobic residue; X ϭ any residue; ? ϭ X or none) with ⌽ 1 (Val 345 ), ⌽ 2 (Leu 346 ), ⌽ 3 (Leu 349 ), and Phe 354 residues oriented toward the hydrophobic pocket and interacting with the acyl (Acyl) and sphingosine (Sph) chains. Note that the differing lipid conformations within the two protein molecules occupying AU (different shades of orange) correlate with conformational distinctions in their hydrophobic residues (mostly, in Phe 354 and Leu 349 ), as well as in their contacts (zig-zag lines).
Remarkably, the Asp 347 ⅐Lys 358 pair also differs in two molecules. In FAPP2_A, Lys 358 adopts the double conformation (orange 1 and 2), which either does not a salt bridge with Asp 347 (orange 2) or results in a bridge that differs from that of in FAPP2_B (orange 1). f, superposition of 17 GLTP molecules from 12 PDB entries showing the gate-region with the ⌽ 3 -residue (Leu 37 ) of the ⌽ 1 ⌽ 2 DX⌽ 3 GX?XF motif that identifies three conformational groups for GLTP, colored in green, yellow, and cyan and three representative GLTP molecules (g) from visualizing the conformational distinctions between the three groups (f).
This work was supported in whole or part by  National Institutes of Health  Grants  GM45928  from  NIGMS ,  CA121493  from  NCI , and  HL125353  from  NHLBI ;  Ministerio de Ciencia e Innovacion  Grant  BFU2010-17711 ;  Russian Foundation for Basic Research  Grant  14-04-01671 ; CIC bioGUNE funds; the  Abby Rockefeller Mauze Trust ; the  Maloris and Hormel Foundations ; and the  Federal Agency of Scientific Organizations. The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This article contains Figs. S1-S4. The atomic coordinates and structure factors (code  5KDI ) have been deposited in the Protein Data Bank (http://wwpdb.