dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the high rate of nonresponse associated with the collection of multi-actor data  (Kalmijn & Liefbroer, 2011) , the study employed a non-probabilistic sampling design. Families were recruited with assistance from undergraduate students from the higher education institution where the researchers are based. Each recruited family received an envelope containing the three questionnaires for the participating family members, along with a plain-language statement and a written informed-consent form. The first page of the questionnaire instructed the target participants to complete the booklets individually and not to discuss the content of the questionnaire with one another. In order to protect the respondents' privacy, separate envelopes were provided that could be sealed and used for each completed questionnaire.
Research Goal 2: Investigating Discrepancies in Reported Amount of Mediation For each identified mediation strategy, one variable was constructed by calculating the aggregated mean value, including all respective items. Table  4  shows the univariate results for each constructed variable, together with the Cronbach's alpha, indicating the internal consistency for the respective items. When working with dichotomous items only (as is the case for the aggregated variables interaction restrictions, access restrictions, technical mediation, and interpretative mediation), SPSS automatically offers the KR20 measure for internal consistency, which is more appropriate when working with dichotomous items  (Sijtsma, 2009). The results show a low internal consistency score for the items measuring technical mediation for the child's and both parents' reports.
Only two-parent families with at least one child between 13 and 18 years old were recruited, thereby excluding single parents. Alternative participant recruitment and data collection strategies might be needed to ensure whether the findings remain true in single-parent families. A second limitation is that the internal consistency of some of the constructed mediation variables was low. For technical mediation, a low consistency score was found for each informant; for interpretative mediation, a low score was found for the mother; and for supervision/co-use, a low score was found for the child. While there is no gold standard for how high a Cronbach's alpha score should be for adequate reliability, a low score does raise concerns about internal consistency. An explanation for the low internal consistency scores was that only two items were used to measure technical mediation and interpretative mediation.
We used hexanal diluted from 1 : 10 000 to 1 : 10 and limonene from 1 : 1000 to 1 : 10 as the latter has a lower vapour pressure value (1. 4 kPa for hexanal vs. 0. 19 kPa for limonene at 25 °C). We choose those two odorants because they are chemically very different (non-ramified saturated aldehyde vs. cyclic unsaturated terpene) and therefore act upon a wide range of olfactory receptors. Electroolfactogram voltage signals were recorded using an Xtra-Cell 2 channels amplifier (DIPSI, Chatillon, FRANCE) used in a DC current-clamp configuration (I = 0), low-pass bessel filtered at 1 KHz and digitised at a rate of 2 kHz using an Digidata 1322a A/D converter (Axon Instruments, Molecular Devices, Union City, CA, USA). A reference Ag/AgCl electrode was placed in the olfactory bulb. Recordings were made with glass micropipettes of 4-5MO filled with a saline solution.
Under voltageclamp mode, the signals were initially filtered at 10 kHz and then at 2. 9 kHz; under current-clamp mode, signals were filtered at 2. 9 kHz. Action potential firing was recorded in current-clamp mode at 10 kHz sampling frequency for spontaneous activity recordings and 50 kHz for current injection recordings. Data were analysed using custom-made routines written in Matlab (The MathWorks, Natick, MA, USA). We measured the following characteristics of the responses to odorants in voltage clamp: maximum amplitude, rise time (duration in ms from 10 to 90% of the maximum amplitude), time at 50% (duration in ms of the response width at 50% of the maximum amplitude), total current elicited (area under the curve in pAs). In current clamp, firing frequency was analysed using custom-made routines written in Matlab.
Acknowledgement This work was funded by the  Institut National de la Recherche Agronomique (INRA). We would like to thank  Birte Nielsen  for her help improving the manuscript, all lab members for fruitful discussions,  Patrice Dahirel  and  Aur elien Raynaud  for animal care and treatments and  Didier Durieux  for technical help. XG and DJ would like to thank the  CSGA animal facility  for animal care.
3D modeling To build the models of the U3 snoRNP from Chaetomium thermophilum and from S. cerevisiae with Rsa1p 239-265 , we used software PyMOL 1. 3  (67)  to superimpose the crystal structure of the S. cerevisiae Snu13p-Rsa1p 239-265 ((50); PDB ID: 4NUT) to the cryo-EM structure of the U3 snoRNP in the C. thermophilum ((48) PDB ID: 5JPQ) and S. cerevisiae  (49) , PDB ID: 5WYJ and 5WYK) 90S preribosomes.
These data illustrated the rapid elimination of the intron during the maturation process and confirmed the accumulation of spliced U3 precursors with a long 3 extrem- presenting the set of specific primers used for reverse transcriptase (RT) and polymerase chain reaction (PCR) steps in order to discriminate the maturation state at the 3 extremity and the splicing status for each RNA species. The theoretical sizes of the RT-PCR products amplified with the various pairs of primers from the unspliced and spliced U3 RNAs are indicated (B) Detection by RT-PCR of the intronic sequence contained in the U3 snoRNA precursor which 3 end is not processed. Reverse transcription was performed from a total RNA extract using the RT long primer. PCR analyzes were performed using the primer couples 5 PCRa/3 PCR (upper panel) and 5 PCRb/3 PCR (lower panel), respectively.
The present results confirmed that the recruitment of Rsa1p is a crucial event for production of fully matured U3 2,3,4 RNA. This need for Rsa1p may be explained by the presence of two suboptimal motifs in RNA U3 2,3,4: the U3 B/C motif with truncated helices and the WT U3 C /D motif already shown to have a low affinity for Snu13p (K d > 1500 nM for the small yU3C /D RNA substrate)  (34). RSA1 disruption and complementation by Rsa1p R 249 /A are associated with a decrease in the cellular level of U3 2,3,4 and with the accumulation of its precursors cleaved at positions +12 or +18. U1 snRNA was used as a loading control. (B) Radioactivity in the bands of gel was quantified with the ImageQuant software after exposure of a phosphorimager screen. The signal of U3 2,3,4 precursors was divided by the signal of the mature U3 2,3,4.
(C) Northern blot analysis of the amounts of U3 U3 2,3,4 RNAs. The U6 snRNA was used as a loading control. Radioactivity in the bands was quantified with the ImageQuant software after exposure of a phosphorimager screen. The signal of the U3 2,3,4 RNA was divided by the signal of the U6 snRNA and the ratio in cells expressing the WT U3 2,3,4 RNA was considered as having a value of 100%. Ratios of the two other strains are expressed relative to this value. events occurring on the pre-U3 +18 and +12, which are products of a pre-U3 3 end processing  (64). In this model, the particle assembly was proposed to displace the Lsm proteins and Lhp1p from their binding site in order to favor the final 3 -end trimming by the exosome. In the present study, the RT-PCR reaction based on hairpin oligonucleotidepriming of cDNA synthesis revealed that the removal of the intron can occur on longest pre-U3s, prior the maturation of the 3 extension (Figure  2 ).
(C) Northern blot analysis of the amounts of U3 U3 2,3,4 RNAs. The U6 snRNA was used as a loading control. Radioactivity in the bands was quantified with the ImageQuant software after exposure of a phosphorimager screen. The signal of the U3 2,3,4 RNA was divided by the signal of the U6 snRNA and the ratio in cells expressing the WT U3 2,3,4 RNA was considered as having a value of 100%. Ratios of the two other strains are expressed relative to this value.
FUNDING Centre National de la Recherche Scientifique (CNRS) ;  University of Lorraine (UL) ;  Agence Nationale de la Recherche  [ ANR-06-BLAN-0208 ;  ANR-11-BSV8-01503 ]. French Ministère de l'Enseignement Supérieur et de la Recherche  fellowship (to B. R. , N. R. ). Funding for open access:  University of Lorraine  and  CNRS.
Ultrasound Measurements The first visit (dating scan) was between 8 + 0 and 12 + 6 wk, and subsequent visits for fetal biometry were scheduled at approximately 4-wk (±1 wk) intervals at 14, 18, 24, 28, 32, 36, and 40 wk. All scanning appointments were arranged at the time of the dating scan and study enrollment. All participants were scanned in the lateral recumbent position. The compulsory ultrasound measurements obtained at all visits included the following biometric parameters: biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), and humerus length (HL). At each examination, all measurements were obtained three times from three separately generated ultrasound images and uploaded electronically (with the associated images) to the data management system.
FL was measured from an image of the full femoral shaft in a plane close to 90 degrees to the ultrasound beam. The distal femoral epiphysis was excluded. Similarly, HL was measured from an image of the full humeral shaft in a plane close to 90 degrees to the ultrasound beam. The participating centers used identical ultrasound machines during the project (Voluson Expert E8, General Electric, Kretz Ultrasound, Zipf, Austria) equipped with two curvilinear transabdominal transducers (4-8 MHz and 1-5 MHz) and a transvaginal transducer (6-12 MHz), observing that the energy output was set so that thermal index (TI) was <1. 0. The TI was automatically recorded and transmitted to the web-based data management system by the ultrasound machine. Measurement results were stored electronically, with the images together with all information collected from the mother and the perinatal outcomes.
Data Management Data were collected via a web-based data management system developed by Centro Rosarino de Estudios Perinatales, Rosario, Argentina. All data (clinical, anthropometric, nutritional, and fetal biometry measurements plus 2-D/3-D images) were stored in a central server compliant with good clinical practice. Data transmission was encrypted to assure data integrity and patient confidentiality. Access to the web system was password protected, and only authorized users had access. Data changes were documented by a complete audit trail record kept automatically by the web system (recording when, by whom, and why data were changed). Data entered into the web system were checked by the coordinating unit at Centro Rosarino de Estudios Perinatales for completeness, accuracy, reliability, and consistent intended performance.
Different kinds of validation procedures were carried out (checking missing values and outliers, cross-checks, cross-time verifications among scanning appointments, and protocol compliance). Measurements and 2-D/3-D images corresponding to fetal biometry had special processing. In collaboration with General Electric Healthcare, Germany, ViewPoint software was installed at all participating centers, allowing a standard interface/procedure for scans and an automatic transfer of fetal biometry measurements/images to the web-based system. Thus, all fetal biometry measurements considered by the protocol were automatically transferred instead of being entered manually (except for D. R. Congo; there, a complete checking of values was done by the comparison of images and values entered into the webbased system).
e. , a substantial upward shift of all the lowest percentiles (10, 5, 2. 5, and 1) in the direction of supernormal. Given the plethora of measurements, we prioritized clinical usefulness in the analyses and results presented here (e. g. , EFW and common biometric measurements) and left the following for secondary studies and publications: transverse cerebellar diameter, fetal foot length, 3-D ultrasound acquisitions, maternal anthropometric measurements except height and weight, the second and third sets of dietary 24-h-recall data (at 28 and 36 wk of gestation), and newborn anthropometric measurements except birthweight.
Data Analysis and Statistical Methods Descriptive statistics were calculated for the women's characteristics at study entry, for mode of delivery, for birth events, and for fetal, neonatal, and maternal conditions, by country and overall. Protocol compliance was evaluated by comparing the dates of the windows of gestational age defined in the protocol with the dates of actual measurements. The ultrasound measurements were used to estimate reference curves for individual parameters (BPD, HC, AC, FL, HL, FL/HC, FL/BPD) and EFW based on Hadlock et al. 's formula 3  [24]. Reference curves were fitted using quantile regression for reference models, as described by Wei et al. [25]  from the work of Koenker  [26, 27]. The development of reference curves has up to now in general used parametric models, based on assumptions about distribution and on transformation of the observations to normal distributions.
Logarithms of ultrasound parameters and EFW were used for the fitting. This was done only to achieve better numerical accuracy and faster convergence of the fitting algorithm. After the fitting, the results were retransformed to the original scale. To describe growth asymmetry, we used the Bowley coefficient of asymmetry  [29] , based on differences of semi-quartile ranges relative to the quartile range, for the gestational ages 15 and 40 wk. Data were analyzed using SAS Software version 9. 4 (SAS Institute, Cary, North Carolina, US) and JMP Pro 12 (SAS Institute, Cary, North Carolina, US).
Participants A total of 1,439 women were enrolled between October 2009 and September 2014, with data collection being completed with the last childbirth in April 2015. Of these, 52 (3. 6%) withdrew consent, leaving 1,387 women and their fetuses participating in the study. Table  1  shows the numbers of women recruited, those withdrawing consent, those lost to follow-up, and those having miscarriages or intrauterine deaths, by country. Among women lost to follow-up and with miscarriage or intrauterine death, 10 and 15, respectively, did not contribute ultrasound information. All women other than those withdrawing consent were included in the growth curve analyses if they contributed ultrasound information, with the number in this analysis being 1,362.
The WHO study had a similar recruitment but retained in the analysis pregnancies with maternal, fetal, and neonatal clinical conditions, based on the principle that reference intervals should reflect as closely as possible the population to which they will be applied. Furthermore, we assessed the effect of removing such pregnancies from the dataset and found no identifiable effect on the percentiles. As seen from Table  17 , it is as if rigorous selection and exclusions have limited effect, and other uncontrolled factors are responsible for the variation between studies and countries. Apart from random error, systematic error due to differences in ultrasound measurement techniques could influence the differences between the studies. However, these studies had well-trained ultrasound operators specifically instructed for the research procedure using internationally accepted techniques, and this should minimize such error.
While including ten countries in the present WHO study was a strength compared to previous studies, it still has limitations. The ten population samples, including two in South-East Asia and two in Africa, were included to increase generalizability, but they are still a very limited sample of the global human population. Africa alone has a greater genetic diversity than has the rest of the world  [44] , and anthropometric variation on that continent is substantial. The present study showed population differences within the pooled dataset, and so the extent to which the results can be extrapolated to other populations, which possibly have other growth dynamics, is at present unknown. A limitation of the study is that ultrasound measurements were accompanied by a corresponding gestational age exposed on the screen, which could have led to undue changes in the management of the pregnancy and pregnancy duration.
Acknowledgments We thank  Mario Merialdi  and  George Bega  for their contributions during the first stages of the study. We thank  General Electric  for loaning the ultrasound machines and for providing technical assistance during the study. We thank  Josede Souza Ramos  for his contributions to the programming of descriptive tables and creation of analysis files. The Argentina team would like to acknowledge ultrasonographers:  Sergio Ricci ,  Debora Montoya ,  Pablo Cañon ,  Mario Marchetti ; nutritionists:  Silvina Tosticarelli ,  Silvia del Cerro ; physicians at the primary care health centers:  Malvina Giuliani ,  Marisa Menighini ,  Karina Martı ´nez ,  Virginia Dı ´az ,  Ana Paula Bogino ,  Paola Salina ,  Fernanda Candio ,  Duilio Filiberto ,  Marı ´a Belen Bosch ,  Eliana Juan ,  Guadalupe Moron ,  Mariela Giraudo ,  Natalia Blanco ,  Berenise Macagno ; neonatologists at  Maternidad Martin :  Ofelia Casas ,  Gabriela Puig ,  Lorna Andreuzzi ,  Marcelo Rodrı ´guez ,  Susana Morales ,  Silvia Carazzone ,  Andrea Bobatto ,  Marina Duarte ; obstetricians at  Maternidad Martin :  Daniel Crosta ,  Silvia Carbognani ; and staff at the  Centro Rosarino de Estudios Perinatales :  Edgardo Abalos ,  Liana Campodonico ,  Cristina Cuesta ,  Hugo Gamerro ,  Lucia Darder ,  Carla Salas ,  Renata Zanello ,  Fernando Burgueño.
https://doi. org/10. 1371/journal. pone. 0181050. g001 The distribution pattern of Verticillium dahliae propagules was studied for potato  [35] , cauliflower  [36] , and mint  [37]. In this work, we develop a tool for assaying the spatial pattern in plant-pathogen-environment conditions and discuss the mean spatial linking patterns by evaluating breeding. To document the spatial interactions in breeding for disease resistance, we introduce application of geostatistics technique on novel experimental design. We illustrate this method and demonstrate its general utility by analyzing a single dataset; namely, a map of leaf symptom severity in a sunflower field under pathogenic attack by the soilborne fungus V. dahliae. The dataset was acquired over a two-year span.
To obtain F1-hybrid cultivars scores, the coefficient of correlation was calculated between the symptomatic ẑðs pred Þ predicted disease values. Univariate statistics were calculated by using the RGui software. Geostatistical semivariograms were calculated with the packages gstat and geoR from RGui  [42]. Spatial indices were calculated by using the software package Spatial Analysis by Distance Indices (SADIE)  [40].
Acknowledgments Project was supported by  French Ministry of Higher Education and Research :  CIFRE 2013/ 1431 ,  ANRT , http://www. anrt. asso. fr/fr/espace_cifre/accueil. jsp. Syngenta provided support in the form of salaries for authors [HM, JB, VMT], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Plant material One hundred sweet sorghum accessions (S1 Table) from the germplasm bank of the Embrapa Maize and Sorghum breeding program were used. These sorghum accessions were classified as historical lines, modern lines and landraces according to the genealogy and historic background available in the GRIN (Germplasm Resources Information Network) database  [57]. The historical lines (HL) are those developed and used between 1850 and the early 1900 0 s, and frequently have unknown origin and lack of concrete information about genealogy  [19]. Modern lines (ML) correspond to those that have been genetically improved and have pedigree information. Landraces are those accessions collected in Africa and Asia that are often phenotypically diverse, but may exhibit some genetic similarity. The landraces were classified as LIS (Landrace World Collection-ICRISAT sorghum collection), LMN (Landrace Meridian Mississippi-USDA sorghum collection) and LSSM (Landrace Sorghum Seed Montpelier-CIRAD sorghum collection).
Molecular markers data Leaf samples were collected from five plants per accession and the DNA extraction was performed using the Dneasy 1 Plant Mini Kit (QIAGEN, Germantown, Maryland, USA). The quality and quantity of extracted DNA was checked in agarose gel and NanoDrop 1 ND-1000 Spectrophotometer. Genotyping-by-sequencing (GBS)  [55]  was performed by the Institute for Genomic Diversity at Cornell University. Genomic DNA was digested individually with ApeKI, and the bar-coded DNA samples were pooled and sequenced in a HiSeq2000 platform (Illumina Inc. , San Diego, California, USA). Sequencing data were separated for each accession and aligned to the BTx623 Sorghum bicolor reference genome  [62, 63]  version 2. 1, using the Burrows-Wheeler Aligner (BWA) software  [64]. SNPs were called using the GBS pipeline available in the software TASSEL  [65].
Variance components were estimated via residual maximum likelihood (REML)  [71, 72]. Heritabilities were calculated as: h 2 ¼ s 2 G s 2 G þ s 2 R r where s 2 G is the genetic variance; s 2 R is the residual variance; and r is the number of replications. All mixed models analyses were performed using the software GenStat v15  [73]. Then, phenotypic correlation between morphological and agro-industrial traits was estimated based on the Pearson's method, using the R package Hmisc (R Core Team 2015).
Diversity analyses Genetic diversity analyses in the sweet sorghum accessions were conducted separately using the phenotypic and the molecular data. Initially, for the phenotypic data, all morphological and agro-industrial variables were standardized. Then, the dissimilarity matrix between lines was calculated using the Average Euclidean distance  [74]. The relative contribution of each morphological and agro-industrial trait for the diversity analysis was evaluated based on the Mahalanobis distance (D 2 ), according to the method proposed by Singh  [75] , using the software Genes  [76]. Subsequently, genetic distances between the sweet sorghum accessions were calculated based on the SNP data using the identity-by-state (IBS) coefficient  [77]  in the software TASSEL. This measure of similarity takes into account the number of identical alleles, whether or not inherited from a common ancestor.
Based on the phenotypic and the molecular dissimilarity matrices, two separate cluster analyses were performed through the Neighbor-Joining method  [78]  using the software DARwin  [79]. Different clusters of sweet sorghum accessions were identified according to the nodes present in the Neighbor-Joining trees. The Mantel test  [80]  was performed, using the software Genes, to test the significance of the correlation between the phenotypic and the molecular dissimilarity matrices, considering ten thousand random permutations and a 5% significance level. Averages of the agro-industrial traits were estimated for each cluster obtained through the phenotypic and the molecular diversity analysis, and were compared using the Duncan 0 s test  [81]  at a 5% significance level. In addition, a principal component analysis (PCA)  [82]  was performed, based on the molecular similarity matrix, in order to infer the population structure in the sweet sorghum accessions, using the package pcaMethods for the R software  [83] , available at the Bioconductor software  [84].