dataesr/scientific-paragraphs-categorization huggingface dataset:
1 M trisodium citrate, pH 4. 6, 0. 9 M magnesium sulfate and 0. 1 M Tris, pH 8. 5, 26% PEG 1500, 16% glycerol, respectively. Although the optimized SeMet-substituted crystals of hDHBN also yielded good diffraction data, there was no signal of selenium in the collected data, and the structure was not solved until the data were collected from crystals soaked in the optimized well solution supplemented with 0. 3 M KI. Different from the P21212 space group of gDHBN, the space groups of hDHBN were P62 and C2221. All of the structure refinements were carried out by using Coot  (51)  and Phenix  (50). SAXS-SAXS determination of gDHBN was performed at 1. 0, 3. 0, 5. 0, 7. 0, and 9. 0 mg/ml at the BL19U2 beamline of the National Center for Protein Sciences Shanghai and in HPLC mode at beamline SWING (SOLEIL Synchrotron, Saint-Aubin, France).
The buffer used was 25 mM Hepes, pH 7. 5, 500 mM NaCl, and 5% glycerol. The corresponding data were collected and processed with buffer subtraction with programs available on the beamlines (BL19U2 pipeline and FOXTROT, respectively). The programs of ATSAS suite PRIMUS and GNOM were first used to extract the experimental R g (radius of gyration) and D max (maximum particle dimension) values  (52) , and then the ab initio envelope was determined by using DAMMIF. Ab initio model quality was evaluated further using averaging with DAMAVER, and the atomic model of gDHBN was pro-posed by flexible modeling with DADIMODO  (53). Finally, the atomic model profile was calculated, aligned, and fitted to the experimental data using CRYSOL and SUPCOMB. DLS determination and gel filtration analysis-DLS measurements were performed at 25 °C using a DynaPro Nano Star instrument (Wyatt Technology Europe GmbH, Dernbach, Germany) with a microcuvette.
Acknowledgments-We thank the staff of the  BL17U1 beamline at Shanghai Synchrotron Radiation Facility  and the  BL18U1 ,  BL19U1 , and  BL19U2  beamlines at the  National Center of Protein Sciences Shanghai  for assistance with structure data collection. We are also grateful to  Dr. Aurelien Thureau  and his team for SAXS data collection at the beamline SWING (SOLEIL Synchrotron). This research was conducted within the context of the "LIA helicase-mediated G-quadruplex DNA unwinding and genome stability. ".
* This work was supported by  National Natural Science Foundation of China  Grants  11574252  and  31370798  and  Northwest A&F University Startup Funding for Xu-Guang Xi  (Grants  Z101021102  and  Z111021103 ). The authors declare that they have no conflicts of interest with the contents of this article. □ S This article contains supplemental Figs. S1 and S2 and Tables  S1 andS2. The atomic coordinates and structure factors (codes  5LUP ,  5MK5 ,  5LUT , and  5LUS ) have been deposited in the Protein Data Bank (.
, 2001). Reflectance data can be collected at multiple phases of crop development. Measurements taken at different time points provide information on different aspects of crop physiology. Given this, we hypothesize that combining data collected at multiple time points in a single prediction equation can increase prediction accuracy relative to what can be achieved using a single snapshot of reflectance. Therefore, the third objective was to quantify the effects of combining spectral data from multiple time points during the crop cycle on prediction accuracy of grain yield. To this end, for each of the methods considered, we compared the predictive performance achieved when using data from a single time point to that of models using data from multiple time points.
Image Data Image data were collected using a hyperspectral camera (VNIR Headwall Photonics Micro-Hyperspec ARS3, Headwall Photonics) mounted on a single-engine aircraft Piper PA-16 Clipper. Flights started 55 d after sowing (when most trials had 50% of plots flowering) and were repeated at 62, 69, 75, and 83 d after sowing (hereinafter labeled as T1, T2, …, T5, respectively). To achieve a resolution of 30 cm pixel -1 , flights were performed at an altitude of 300 m and a ground speed of ~34 m s -1. The hyperspectral camera had a radiometric resolution of 10 bits. It acquired images from 392 to 850 nm, subdivided into 62 evenly spaced bands at a spectral resolution of 1. 9 nm, covering the visible spectrum and part of the NIR spectrum. A filter was applied to the images to exclude pixels corresponding to a mixture of crop and soil, and to calibrate reflectance intensity.
Parameter p controls the proportion of nonzero effects (i. e. , how many predictors enter in the model and how many are zeroed out). The extent of variable selection and shrinkage of estimates of effects is controlled by the hyper-parameters {p, df, S}. In the implementation of model BayesB provided by the BGLR R-package (Pérez and de los Campos, 2014), p and S are estimated from the data and df is fixed to a value of five, which gives a t-density with thicker tails than the normal prior. Further details about this model can be found in  Meuwissen et al. (2001)  and de los  Campos et al. (2013). A summary of the regression models considered in this study is given in Table  1.
Plot images were trimmed by excluding borders of two to three pixels per plot. The plot coordinates were defined based on a grid of polygons representing the trial plots. This grid was adjusted on the map based on the actual location of certain plots in the field, measured with a Trimble R4 GPS receiver. Each of the 62 reflectance bands was measured using a mean value obtained from the central plot pixels. Several VIs (NDVI, CWMI, mND, MTCI, MCARI2, and PRI) were derived from reflectance data at selected bands (Table  1 ).
No. of inputs Estimation method MTCI, terrestrial chlorophyll index; MCARI2, new modified chlorophyll absorption ratio index; PRI, photochemical reflectance index; OLS, ordinary least squares estimation; PLS, partial least squares regression; BayesB, A Bayesian shrinkage and variable selection method. ‡ For the vegetation indices, formulae are reported with subindex indicating reflectance wavelengths (in nm). For the rest of the models, the input is either the 62 reflectance wavelengths or latent variables derived for them. § The number of latent variables was chosen using an internal (to the training dataset) cross-validation. The numbers in the table represent the range of the number of latent variables obtained in the different models considered. For further details, see Supplemental Table  S2.
Estimation Methods and Software All the analyses were performed using R software (R Core Team, 2016). The OLS regressions were fitted using the lm function of R. The BayesB model was implemented using the BGLR package  (Pérez and de los Campos, 2014). Simplified versions of the scripts used for analysis are provided in the Supplemental Methods. The PLS regression was implemented using the PLS R-package  (Mevik and Wehrens, 2007). The number of latent variables used in PLS was chosen using internal validation. For instance, in the 11-fold CV, for each fold, data from the 10 trials that constituted the training set were further partitioned into 70 (training) and 30% (testing) sets, and PLS was fitted with 1, 2, …50 LVs to 70% of the training data. We used each of these PLS models (with 1, 2, …50) to predict yield in the 30% of the data saved for testing and chose the number of LVs that gave the highest prediction correlation in the testing set.
Acknowledgments The authors acknowledge financial support from  CIMMYT  and from  ARVALIS-Institut du vegetal. The development and maintenance of BGLR was supported by  NIH  grant  R01GM101219. The authors thank  Drs. Timothy Beissinger  and  Jean-Luc Jannink  for comments provided to earlier versions of the manuscript and  Alma McNeb  for her editorial suggestions. The authors also acknowledge valuable comments provided by anonymous reviewers and Associate Editor  Dr. Jesse Poland.
Pregnant women could be enrolled, but data were considered separately. The size of the population in this proof-of-concept study was not designed to provide answers regarding the prevalence of WD among hospitalized patients with psychiatric disorders (which is thought to be higher than that in the general population)  [10]  but was determined according to the challenge of performing laboratory tests with complete clinical and possible neurological and hepatic assessment in a large sample of hospitalized patients with psychiatric disorders; and the documented variabilities of Cp and serum Cu levels in the general hospitalized population  [16, 17]. Two evaluators established consensus diagnoses according to ICD-10 criteria. The diagnosis was systematically assessed by two trained psychiatrists.
They used all the information available from direct interviews of patients and examination of case notes. Symptoms were evaluated with M. I. N. I. semi-structured interviews  [18]. Information about alcohol or substance abuse, psychiatric morbidity, and treatments was recorded. The patients who reported that they did not use substances were considered as nonusers. A clinical assessment was performed in order to disclose neurological or hepatic signs. Clinical and biological data were compiled into case vignettes, and diagnoses were made blind regardless of biological data.
Mutation detection in the ATP7B gene: sequencing and MLPA analysis Genomic DNA was extracted from 10 mL of EDTA anticoagulated blood using salt precipitation method (Nucleon BACC3 RPN 8512, GE Healthcare). For mutation analysis, PCR amplification of the 21 exons and flanking regions of the ATP7B gene was performed using intronic primer pairs and the PCR conditions previously described  [22]. Bi-directional sequencing was performed on AB 3730 Genetic Analyser with dyetermination chemistry, using SeqScape software. Nucleotide changes were detected by comparing the sequence obtained in the chromatogram with the normal gene sequence (NM_000053. 3; Homo sapiens chromosome 13 complete sequence). Patients with one mutation in the ATP7B gene were tested using MLPA assay (SALSA MLPA KIT Wilson disease, MRC-Holland).
Demographic and clinical data From the population of 305 patients initially included, a total of 269 patients completed the study (mean age of 41 ± 22 years, 118 females and 151 males). Thirty-six patients were excluded from the study because of missing data. The following ICD-10 diagnoses were observed: schizophrenia (n = 116), bipolar disorders (n = 37), alcohol abuse (n = 37), depressive and/or anxiety disorders (n = 27), behavioral or personality disorders (n = 27), intellectual disability (n = 7), autism (n = 5), anorexia nervosa (n = 2), cannabis addiction (n = 4), and miscellaneous neurological disorders (n = 7). All patients received psychotropic treatment (monotherapy or combination therapy): Other non-psychotropic temporary treatments were not mentioned.
ATP7B mutation detection Fifty-one patients (15%) showed both atypical psychiatric signs and Cp ≤ 0. 18 g/L (borderline or critical values) and Cu concentration ≤0. 88 mg/L. None of them presented with the clinical signs of neurological WD. Among the 29 patients who gave their informed consent for genetic study, four presented with only one ATP7B mutation (heterozygous state) identified by direct sequencing and MLPA analysis. Three different missense mutations were identified: p. His1069Gln, c. 3207C>A (exon 14); p. Pro1379Ser, c. 4135C>T (exon 21); and p. Thr1434Met, c. 4301C>T (exon 21), (Table  1 ). These variants were classified as disease-causing mutations in the HUGO Wilson's disease database (http://www. uofa-medical-genetics. org/ wilson/index. php) and the Alamut or Polyphen sites.
Acknowledgements The authors would like to thank the  Hospices Civils de Lyon  (for Study management),  Florence Apruzzese  (for English editing assistance), le  Conseil Scientifique et de la Recherche du Centre Hospitalier le Vinatier , and  Julien Maurin  and  Guenaëlle Piguet-Lacroix  (for technical assistance). MOPSY consortium was involved in the clinical and biological support of the study.
Acknowledgements Thanks to  Rosa Scopetra  who alerted us about Leiden's specimen. Thanks to  Danielle Hanrahan  (A),  Lesley Walsingham  (K),  Grégoire Flament  (P), and  Jacqueline Fambart-Tinel  ( NOU ) for scans of specimens and access to MacKee's books, and to  Fred Barrie  (MO) and two anonymous reviewers for nomenclatural advice, as well as to  Jens G. Rohwer  for comments improving the manuscript.
Those originating from North or Central Europe are also channelled. Then, strong daily temperature variability is a feature of this climate. Stable atmospheric conditions often lead to thermal inversion with cold temperature over the plain that host crops and pastures. Even if the region offers good potential (soil, farmer practices, and economic opportunities) for the cultivation of peas, the winter climate is a major constraint that limits its development. We used the daily surface temperature recorded by Météo-France over Burgundy-Franche-Comté since 1961. Daily maximal and minimal temperatures (October-March) were recorded by the Météo-France Station Network (MFSN) from 1961 to 2015 in order to analyse the impact of autumn and winter surface temperature warming at regional scale. The obtained dataset, with more than one hundred stations (among there thirty complete), permitted to accurately assess the temperature patterns at a daily time step.
g. Boulard et al. , 2015). For each grid the longitude, latitude and height from Digital Elevation Model (DEM) were used as the first order external drift predictors (Fig. 1 ). We assume that the residuals are second order stationary with a constant mean and that the covariance is only determined by the distance vector, i. e. the lag. More details on the automatic variogram fitting may be found in  Hiemstra et al. (2009). A station-gridpoint comparison was performed at seasonal scale to assess the quality of the interpolated data. The stations with less than 10% of missing data over the 1961-2015 period were compared with their nearest-neighbouring points. While all stations available each day were used in the interpolation process. Completed daily stations and gridded (interpolated) data were used to feed a parametrized winter frost stress model for pea crop.
The observed frost damage is evaluated on a pea crop with a note from 0 (no damage) to 5 (death of all plants). Dossier Fig. 1. Flow chart providing an overview of the methodological steps implemented in the framework of this study. The chart consists of two interrelated sections, pointing out for (right) parametrization and assessment of the frost stress model for winter pea crop and (left) daily climate data interpolation at 12-km-grid resolution from 1961 to 2015. KED is the kriging with external drift method which is similar to regression kriging  (Hengl et al. , 2007). An automated interpolation based on the AUTOMAP R packages (R Core Team, 2016;  Hiemstra et al. , 2009)  has been used in this study. This study focuses on three main parameters of the pea crop: (1) two frost stress resistance level (À13 °C and À23 °C), (2) twelve rates of acclimation (from 28 to 50 days) and (3) four dates of sowing during October (1, 10, 20 and 30) (Fig.
1) for both temporal (Fig. 5 ) and spatial patterns (not shown). Frost grid-based stress is however smoothed compared to station-based that may be attributedas pointed in Figure  3  to the capability of the gridded data to account for local climate conditions. Despite slight biases (Tab. 1), grid-based frost stress estimates is able to satisfactorily reproduce for various frost resistance levels the patterns of the pea frost stress. Consequently, grid-based frost estimates were used to simulate the evolution of pea frost stress with climate warming from 1961 to 2014.
Acknowledgements. Funds for this study were provided by the  PSDR program INRA-Région Bourgogne (PROFILE and ProSys ). This work was also supported by the  Terres Inovia and Terres Univia-UNIP. We thank the CCUB cluster for the computations and the  CRC team. We also thank the teams of the  INRA Experimental Units in Dijon and Mons. The authors thank  Météo-France , especially  Annick Auffray  and  Denis Thévenin , who provided the temperature and precipitation data, as part of an agreement with the  University of Burgundy.
015 and p < 0. 001, respectively; the dataset was broken in two halves, as the response was not linear over the entire period). Thus, 30 generations of frequent selfing substantially decreased the additive variance for quantitative traits, providing a positive answer to our first question. As no selection was applied on shell roundness over these 30 generations, this effect likely resulted from an acceleration of genetic drift. Gene diversity at microsatellite markers (7 microsatellites, 654 geno-typed individuals) was reduced in S relative to C lines (�27%, 0. 271 versus 0. 376; Figure  S2 ) and was not significantly different from the loss in quantitative variance, since the corresponding reduction in response to selection (realized heritability, 0. 19 versus 0. 33, � 42%) does not lie outside the estimation error around the neutral estimate (p = 0.
ACKNOWLEDGMENTS We thank  B. Pélissié ,  T. Bataillon ,  H. Fréville ,  S. Maurice , and  J. Ronfort  for discussions; two anonymous reviewers for comments; and the  SMGE (CEFE)  and  Environmental Genomics platform (LabEx CeMEB ) for support with the molec ular analyses. This work was a part of  E. N. 's PhD  project and was funded by the  Agence Nationale de la Recherche  ( ESHAP ,  ANR 12 BSV7 0015 , and  AFFAIRS ,  ANR 12 ADAP 005  to P. D. ) and by  CNRS.
Dynamic Light Scattering (DLS) Viral particles were purified as described previously  (Schellenberger et al. , 2011a). Mean particle diameters and polydispersity of GFLV-F13 alone or complexed to Nb23 or to Nb23:EGFP was estimated by DLS using a Zetasizer NanoZS (Malvern, France) and Nanostar (Wyatt, CA). Five successive measurements were performed using three independent virus and protein preparations with virus at 0. 1 mg/mL in Tris buffer (50 mM Tris, 100 mM NaCl, pH 8. 3), Nb23 at 0. 1 mg/mL and Nb23:EGFP at 0. 9 mg/ mL. Scattered intensities were recorded at 20 °C and data were processed with DTS software (www. dtssoftware. com, version 6. 01) or DYMAMICS (www. wyatt. com/products/software/dyna mics. html, version 7. 1. 8. 93), respectively. All particles were monodisperse. Reverse Transcriptase-qPCR (RT-qPCR) analyses GFLV RNA1 and EGFP/Nb23:EGFP transcripts were quantified by RT-qPCR relatively to the expression of cyclin-dependent kinase homolog (GI:849067, N.
PCR was performed in triplicates using 0. 5 lL of reverse transcription reaction and 2. 5 lM gene specific primers in a total volume of 10 lL LightCycler 480 SYBR Green Master I mix on a LightCycler 480 system (Roche, France) with cycling conditions of 5-min denaturation at 95 °C followed by 40 cycles at 95 °C for 10 s, 60 °C for 15 s and 72 °C for 15 s. Relative gene expression levels were calculated by means of the linear regression of efficiency method using LinRegPCR software (version 2013. 0) (Ruijter et al. , 2009). Primer sequences are available upon request.
Fluorescence microscopy For cellular-level scale observations, water-mounted leaf discs of six-to 7-week-old N. benthamiana plants were imaged with a LSM 780 laser scanning confocal microscope attached to an observer Z1 microscope (Zeiss, Germany) equipped with a 209/ 0. 8 Plan-Apochromat objective and using excitation and emission wavelength filters set to 488 nm and 499-521 nm. For macroscopic observations, leaves were imaged with a MacroFluo Z16 APO(A) macroscope (Leica, Germany) using excitation and emission wavelength filters of 538-562 nm and 570-640 nm for red channel. Images were processed using Zen 2011 imaging software (Zeiss) and Photoshop CS5 (Adobe).
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Recherche Agronomique (INRA) 'Plant Health  and the Environment' division, the  Agence Nationale de la Recherche (ANR)  awards  COMBiNiNG  ( ANR-14-CE19-0022 ) and  VinoBodies  ( ANR-14-CE19-0018 ), the  European Regional Development Fund (ERDF)  in the framework of the  INTERREG V Upper Rhine programme Vitifutur , Transcending borders with every project, the  Conseil interprofessionel des vins d'Alsace , the  Comit e interprofessionel du vin Champagne , the  Bureau interprofessionnel des vins de Bourgogne  and the  Conseil interprofessionnel du vin de Bordeaux. C. H. was supported by a fellowship from the  R egion Alsace. L. A. was supported by a  CIFRE grant  from the  Institut Franc ßais de la Vigne et du Vin , subsidized by the  ANRT  (CIFRE convention number  2012/0929 ).
We identified 85 woody species with populations incipiently, semi or fully domesticated by pre-Columbian peoples (hereafter domesticated species listed in Database S1). We found that 20 of these 85 domesticated species are hyperdominants: five times higher than the number of hyperdominant species expected by chance. We then tested if forests closer to archaeological sites and rivers have higher abundance and richness of domesticated species. Forest composition was evaluated in association with numerous types of archaeological sites, including pre-Columbian habitation sites (with and without anthropogenic soils), earthworks (mounds, causeways, raised fields, terraces), rock art (paintings and petroglyphs) and identified eco-archaeological regions (1, 24) (see Fig. S2 ). We included ecoarchaeological regions in the analysis because they indicate environmental settings with large and abundant pre-Columbian earthworks  (25).
2 , Fig. S3 ). The total number of individuals of domesticated species per hectare (abundance) ranged from 0-292 and the total number of domesticated species (richness) from 0-19. The relative abundance ranged from 0-61 % and the relative richness from 0-19 %. Forests with a diverse assemblage of domesticated species tended to have a high abundance of these species (Fig. S4 ). The abundance of all domesticated species was, however, mostly due to 20 hyperdominant species. Domesticated hyperdominant species were more widespread across Amazonian forests than non-domesticated hyperdominant species. We found that 70 % of the 20 domesticated hyperdominant species studied here occur in all Amazonian regions (Database S1) versus only 47 % of the 207 nondomesticated hyperdominant species  (17). Most of domesticated species that are hyperdominant have incipiently domesticated populations, rather than fully domesticated ones.
Acknowledgments: This paper was made possible by the work of hundreds of different scientists and research institutions in the Amazon over the last 80 years. This work was supported by  ACCA/ACA -Asociación para la Conservación de la Cuenca Amazónica/Amazon Conservation Association ;  Alberta Mennega Stichting; ALCOA Suriname; Banco de la República; CELOS Suriname ;  CAPES (PNPG) ;  CAPES Ciencia sem Fronteiras  ( PVE 177/2012 );  Conselho Nacional de Desenvovimento Científico e Tecnológico of Brazil (CNPq)  Projects  CNPq/FAPEAM -INCT CENBAM  ( 573721/2008-4 ),  PPBio Manaus  ( CNPq 558318/2009-6 ),  CNPq -PPBio-AmOc  ( 457544/2012-0 ),  CNPq -PQ  ( 304088/2011-0  and  306368/2013-7 ),  Hidroveg Universal CNPq  ( 473308/2009-6 ),  Projeto Cenarios FINEP/CNPq  ( 52. 0103/2009-2 ), CNPq -SWE  ( 201573/2014-8 ), and  CNPq Universal  ( 307807-2009-6 ),  CNPq Universal  ( 479599/2008-4 ),  CNPq Universal   458210/2014-5 ,  CNPq Universal  303851/2015-5; Colciencias; FAPEAM projects with  FAPESP  ( 09/53369-6  and  465/2010 ) and  PRONEX-FAPEAM  ( 1600/2006 );  Gordon and Betty Moore Foundation ;  Guyana Forestry Commission ;  Investissement d'Avenir grant of the French ANR  ( CEBA :  ANR-10-LABX-0025 );  IVIC of Venezuela ;  Lincoln Park Zoo ;  Margaret Mee Amazon Trust ;  Margot Marsh Foundation ;  Marie Sklodowska-Curie/European Union 's  Horizon 2020  ( 706011 );  MCTI-Museu Paraense Emílio Goeldi -Proc.