dataesr/scientific-paragraphs-categorization huggingface dataset:
Considering the high variability of patterns and the lack of availability of high-quality pictures for some breeds, only those with confident solid coat colors were considered. Three groups were created: a Black group (79 animals and three breeds), a White group (375 animals and six breeds), and a Red group (93 animals and three breeds). Breeds and samples sizes are in Table  2b. For each group, a F ST analysis was carried out by considering the following pairwise comparisons: White vs. Black and Red, Black vs. White and Red vs. White. These comparisons were performed using the script described by Talenti et al. [12]  for which 500-kb windows with overlaps of 250 kb were considered. For each comparison, the top four windows that contained at least four SNPs, corresponding to the 0. 9996 percentile of the overall distribution were considered.
The genes within these windows were screened using the coat color gene website of the International Federation of Pigment Cell Society (http://www. espcr. org/micem ut) to investigate their direct or indirect associations with coat colors in mice and shades of skin color in humans. SNPs that were located on either side of candidate genes for both purpose and coat color panels (± 5 Mb = short and ± 10 Mb = long) were used as sets of variables with the same animals for canonical discriminant analysis (CDA), which was performed with the CANDISC procedure implemented in SAS-stat software (SAS Institute, Inc. , vers. 9. 4). These analyses allowed the identification of the SNPs that contributed most to the discrimination between groups. These contributions were summarized as canonical functions (CAN), which were linear combinations of the original variables (discriminant SNPs).
Signatures of selection associated with bioclimatic environmental variables To study the influence of the environment on the distribution of adaptive genetic variation, a landscape genomic approach using the Samβada software  [36]  and F ST analyses were applied to identify genotypes that are significantly associated with environmental variables. For this purpose, we used a reduced subset of samples with Country is indicated when necessary for the analysis known geographic coordinates of the sampling point. This reduced dataset contained 2661 animals from 28 countries (Table  3 ). Finally, an additional filtering step was applied before performing the F ST analyses (see below).
Landscape genomic analyses The environmental conditions were characterized using the bioclimatic variables from the WorldClim database (http://www. world clim. org/) (see Additional file 3: Table  S1 ). These data were available worldwide at a resolution of one arc-second and represented an average of the conditions from years 1950 to 2000. The values of these variables were extracted for the coordinates of each sampling point using the QGIS 2. 14. 7 software and were centered-reduced. A buffer area around each sampling point was generated to integrate, into the model, the environmental variability of the area that could influence an individual. Here, a radius of km was selected which assumed that the goat could move in a circular area of 10 km in diameter, centered on the sampling point. Within that area, in order to consider only the area corresponding to the potential home range of the goats, a land cover discrimination based on the Global Land Cover 2000 classification was applied  [37].
On this basis, the areas corresponding to artificial surfaces (urban land cover) and water bodies were removed. For the remaining areas, the bioclimatic variables were retrieved and for each one, seven statistical measures (minimum, maximum, mean, standard deviation, range, median and mode) were computed. The Samβada software  [36]  was used to compute the parallel processing of multiple univariate logistic regressions between each genotype versus each environmental variable. All univariate models were computed and filtered considering a significance threshold of 0. 05 before Bonferroni correction. Then, a second step of filtering was applied to retrieve only the SNPs for which at least two genotypes were significantly associated with an environmental variable and showed simultaneously: (1) a very strong effect of the environment on the genotype (absolute value of the β1 regression coefficient higher than the 0.
99 percentile of all absolute values of β1 of the significant models), (2) a high statistical significance of the association (G and Wald scores higher than the 0. 95 percentile of all G and Wald scores of the significant models) and (3) a strong goodness of fit of the models, with an Akaike's information criterion (AIC) lower than the 0. 10 percentile of all AIC of the significant models. The Enrichr database  [38, 39]  was used for the genes detected with the Samβada analyses to identify the major biological processes that involved the identified genes (GO biological process) by considering significant clusters with a P value lower than 0. 05. F ST analyses Each animal of the working dataset with a known GPS geographic location was assigned to a Köppen climate group (Tropical, Dry, Temperate, and Cold;  [40] ) through the website climate-data.
org. Then, we applied the following filters: (i) for each climate group, only breeds with at least 10 animals allocated were considered, and (ii) if the same breed was in two or more groups only the animals that belonged to the groups of known breed origin were considered and the others were discarded. The assignment of each individual to a Köppen group and the subsequent filtering of animals provided a dataset of 1689 animals (141 for the Tropical group, 796 for the Dry group, 632 for the Temperate group and 120 for the Continental group), with no redundant breeds across the groups; assignments are summarized in Table  4. MDS (multidimensional scaling) of the filtered animals was performed using the Plink 1. 9 software  [41]. Single SNP F ST was performed by comparing each group to the remaining groups merged together.
The top 20 SNPs of each comparison, corresponding to the 0. 9995 percentile of the distribution were considered and compared with the previous results of the landscape genomics analysis that had been filtered based on a significance threshold of 0. 05 before Bonferroni correction. Only the selected F ST SNPs with a G score or Wald score and β1 regression coefficient higher than 0. 99 quantile were retained for further analysis. Allele frequencies of the SNPs that were shared by different groups were calculated and compared using the Plink 1. 9 software  [41].
Selection of goat populations for the analysis of signatures of selection based on worldwide genetic diversity A dataset of goat breeds ensuring robust modelling was developed prior to FLK and hapFLK analyses. This dataset included 61 breeds, which overall represented all the genetic diversity present in the complete dataset (Fig. 1 ). The genetic diversity in this set of breeds mirrored their geographical origin and was consistent with a radiation from the domestication center. Thus, each population was assigned to one of the 11 sub-continental groups, and each sub-continental group was analyzed independently. In Fig. 1 , the root of the population tree, which is located at the center, corresponded to the ancestral population.
Signatures of early adaptation FLK analysis on the filtered geographical dataset based on the estimated ancestral allele frequency of geographical groups detected 62 SNPs with evidence for outlying differentiation among groups (Fig. 3 ) and (see Additional file 3: Table  S3 ). The phylogenetic tree seemed to confirm the relationship between sub-continental blocks. Among the 62 SNPs, two signatures of selection were found on chromosome 1 close to the SOX14 (SRY-box 14) gene and on chromosome 16 close to the NOCT (nocturnin) gene. Another signature of selection   S3 ).
A separate signature of selection approximately 5 Mb away from this region was also found in the Italian local breed Ciociara Grigia, which is used for milk production. Brito et al. [13]  recently identified other genomic regions that may be associated with milk production, using a partially overlapping subset of breeds  [13]. This difference may be due to a different breed composition of the dataset used and underlines the fact that there may be some breed-specific regions related to milk composition. Therefore, more tailored experiments are needed to decipher the genetics behind milk production in goat.
(a): LONG and (b) SHORT: (b). Left: CDA plot. Right: Correlation value of the SNPs used for the analyses for CAN1 and CAN2. Figure S20. CDA for the region on chromosome 5 near the ADAMTS20 gene detected for the group of coat color breeds. (a): LONG and (b) SHORT: (b). Left: CDA plot. Right: Correlation value of the SNPs used for the analyses for CAN1 and CAN2. Figure S21. MDS plot of the filtered dataset considering components 1 and 2. Animals are colorcoded based on the Köppen classification of groups: Tropical (green), Dry (red), Temperate (orange), Continental (blue).
Acknowledgements Authors are grateful to all breeders and AdaptMap members who provided data to the initiative (http://www. goatadaptm ap. org/) that are described in Stella et al. [113]. The authors from the  University of Milan  are grateful to  Stefano Frattini  who was supported by the  GenHome  project, for supporting coordinating meetings. Support for  Francesca Bertolini  was provided by  State of Iowa , hatch and Ensminger funds. Financial support provided in part by the  Illumina Greater Good  grant is appreciated.
By analysing a database of 26 years of observations of Jupiter with the Nançay Decameter Array, we unambiguously identify the radio emissions caused by the Ganymede-Jupiter interaction. We study the energetics of these emissions via the distributions of their intensities, duration, and power, and compare them to the energetics of the Io-Jupiter radio emissions. This allows us to demonstrate that the average emitted radio power is proportional to the Poynting flux from the rotating Jupiter's magnetosphere intercepted by the obstacle. We then generalize this result to the radio-magnetic scaling law that appears to apply to all plasma interactions between a magnetized flow and an obstacle, magnetized or not. Extrapolating this scaling law to the parameter range corresponding to hot Jupiters, we predict large radio powers emitted by these objects, that should result in detectable radio flux with new-generation radiotelescopes.
2001; Zarka 2007). Unlike bodies embedded in the solar wind, interaction of Jupiter's magnetosphere with the Jovian moons is dominated by the flow of magnetic energy. This flow proceeds via Alfvén wave excitation at Io  (Saur et al. 2004) , and magnetic reconnection at Ganymede which possesses an intrinsic magnetic field  (Kivelson et al. 2004 ). Here we analyze the database of 26 years of radio observations of Jupiter with the Nançay Decameter Array, built by  Marques et al. (2017a). In Sect. 2, we detail the unambiguous detection of Ganymede-Jupiter (hereafter G-J) decameter emission from this database. Over 350 G-J emission events are detected, that constitute the basis for the first quantitative study of their energetics (intensity, duration and power) (Sect. 3), that is then compared to the energetics of I-J emissions.
2017a). Radio emissions induced by the other Jovian satellites have been searched among non-Io emissions. Past searches of G-J emissions in spacecraft (Voyager, Galileo, Cassini) observations  (Zarka et al. 2017, and refs. therein)  provided statistical hints of their existence, in data sets spanning intervals of no longer than 2 years (the longest one having been recorded by the Galileo spacecraft). A recent re-analysis of Voyager and Cassini observations provided convincing event-by-event identifications of Ganymede-and Europa-induced radio arcs in the time-frequency plane, but without studying the intensity of these emissions  (Louis et al. 2017a). The recent construction of a 26-year-long database 1  from daily observations of Jupiter with the Nançay Decameter Array from 1990 to 2015  (Marques et al.
2017a ) allowed us to build occurrence probability diagrams versus CML and Ganymede's orbital phase (hereafter Φ Ga ), one order of magnitude more sensitive than in previous studies due to the much broader statistical basis. In addition, because each emission event is identified individually in the database (e. g. as non-Io-A, Io-C, etc. ), the search for G-J emissions can be done separately for each non-Io emission type, which proved to be more efficient. Figure  2  shows the CML-Φ Ga occurrence probability diagrams built for each non-Io component. A first analysis showed that no emission event with frequency >33 MHz is detected in regions of enhanced probability in the CML-Φ Ga plane for non-Io-A and non-Io-B (northern) emissions. This is due to the fact that the northern footprint of the Ganymede flux tube lies northward of the northern high-amplitude magnetic anomaly at the surface of Jupiter crossed by the northern Io flux tube footprint, meaning that lower electron cyclotron frequencies are reached compared to I-J emissions.
As a first step, we attempt to correct the measured event durations for this bias. Figure  5  shows the distribution of the durations of all emissions in our 26-year database, from which we derive a linear approximation of the variation of the average emission duration < D > as a function of R: < D > (min. ) = 43. 2 -7. 6 × (R -5. 2AU). (1) We can use it to correct the duration of each emission event to first order by computing: D cor = D + 7. 6 × (R -5. 2AU), (2) with D and D cor in minutes. We finally obtain in Figure  4b  the statistical distribution of event durations normalized to the average distance of 5. 2 AU. All of the following plots and discussions refer to these corrected durations only. To compare the normalized distributions (i. e. with an histogram maximum=1) of I-J, non-Io, and G-J event durations of Fig.
It is fitted with straight lines (in lin-log scale) whose slopes are indicated with the same colour code at the top of the plots. When α varies from 0. 5 to 2. 0, the slopes regularly increase (the straight line fits turn counterclockwise). The value of α for which the red (respectively, blue) slope is closest to zero is   5. Emission durations vs. Jupiter-observer distance. Dots represent the duration of all emission events in our 26-year database. The histogram in black, corresponding to the scale on the right side, displays the number of dots per 0. 1 AU interval. The black solid line is the median duration per 0. 1 AU interval. The blue solid line is the robust mean duration per 0. 1 AU interval (computed by excluding iteratively the points outside of a m ± 3σ interval), and the blue dashed lines indicate m ± 1σ.
Discussion Based on the recent 26-year database of Jupiter observations with the Nançay decameter array built by  Marques et al. (2017a) , the prominent I-J component was revisited (Figs. 1a  and 3a, c ) and the G-J component was detected unambiguously  (Figs. 1b and 3b, d, and Zarka et al. 2017). More than 350 G-J emission events were detected in the interval 1990-2015 (Table  1 ), making it possible to statistically characterise their duration, intensity, and, therefore, energetics (average power), and compare them to those of I-J emission events. We have found that G-J emissions have typical intensities only ∼0. 5 dB lower than I-J or non-Io emissions (Fig. 4a ). This suggests that CMI operates at relatively uniform efficiency around Jupiter, where the various radio components are produced, whatever the origin of the accelerated electrons, leading to similar intensity distributions for all radio components.
Acknowledgements. PZ acknowledges the support from the  Programme National de Planétologie, Programme National Soleil-Terre , and  Action Spécifique SKA-LOFAR of CNRS/INSU, France. MSM acknowledges the support from  CNPq  project  154763/2016-0-PDJ , Brazil. EE acknowledges the support from project  CNPq /PQ 302583/2015-7 , Brazil. CL acknowledges the support from the  Labex Plas@Par , funded by the  Agence Nationale de la Recherche  as part of the  programme "Investissements d'avenir"  under the reference  ANR-11-IDEX-0004-02. The Nançay Radioastronomy Station acknowledges the support of the  Conseil Régional of the Région Centre in France. The  Nançay Decameter Array  acknowledges the support from the  Programme National de Planétologie  and the  Programme National Soleil-Terre of CNRS-INSU.
All the above options were incorporated into the app, which also allowed for the selection of the side of involvement (left or right). Each question is accompanied by an image with the associated areas of the acetabulum highlighted to facilitate fracture interpretation and classification (Fig. 3 ). Thirty-five cases from the records of our acetabular fracture referral center database (containing records of >450  acetabular fractures between 2007 and 2015) were chosen by the operating surgeons (P. J. and G. R. ) and were classified according to the Letournel system on the basis of the initial radiograph, the CT scans (2D and 3D reconstructions), and the surgical findings. Cases were disregarded and replaced when an agreement on classification could not be reached. Fourteen simple fracture patterns and 18 complex fracture patterns were chosen to constitute the study sample, matching the proportion of each fracture type in our series and in the literature  20.
ATAC = Total time for interpretation Accuracy • Number of cases : The ATAC represents the actual power of the method to obtain an accurate diagnosis. To evaluate intraobserver reliability, the classification of these fractures was performed twice during each phase, but only the first reading in each phase was used for accuracy testing. To minimize recall bias, the interval between each reading was 4 weeks and the order of the cases was randomized for each reading. Statistical analysis was performed with use of SPSS 18. 0 software (IBM). The accuracy of the classifications for each group and each reading was compared with use of the Fisher test. The time needed for the readings was compared with use of the Student t test. Intraobserver agreement in each group, as well interobserver reliability between groups, was calculated with use of the interclass correlation coefficient (ICC).
89 when the app was used, regardless of the experience of the examiners. Furthermore, the interobserver coefficient was also analyzed in our study, and all experience groups showed significant improvement, with a coefficient of >0. 8. This finding emphasizes that the use of this app increases homogeneity between different examiners in the same group, thus standardizing acetabular fracture classification. Finally, it is important to note that the use of the app greatly facilitates classification. The time needed for classification was cut in half compared with that needed when the standard method was used. To our knowledge, no other authors have reported on the time required for acetabular fracture classification, so while it was impossible to compare our data with previous findings, it seems logical that the use of more-complex decision trees would increase the time needed for classification.
Pharmacokinetics and metabolism 5] [16]  Oral (5 mg/kg of body weight in rats and 1 mg/kg of body weight in dogs) and intravenous (bolus dose of 2 mg/kg of body weight in rats and 0. 5 mg/kg of body weight in dogs) formulations were utilized in the studies. The safety and pharmacokinetics of grazoprevir were evaluated by Brainard et al in healthy males after single-dose and multiple-dose administration. 17 An oral dose of 2-1,600 mg or placebo in fed or fasted state was administered to 24 healthy males in the single dose group. An oral dose of 100-1,000 mg or placebo once daily for 10 days was administered in 40 healthy males in the multiple-dose group. No serious adverse events occurred in the study, with one subject experiencing flu-like illness unrelated to the drug. Following single administration of an oral dose, the pharmacokinetic data revealed Tmax values of 2-5 hours and a terminal halflife of 15-34.
Pharmacokinetic data were evaluated in six patients with HCV in another study, one with genotype 1 and five with genotype 3, which yielded higher drug exposure as compared to healthy controls (1. 2-2. 1 fold). 18  non-randomized, open-label Phase II study (C-SALT trial) assessed the efficacy, safety, and pharmacokinetics of grazoprevir plus elbasvir in patients with HCV genotype 1, 4, or 6 infection and Child-Pugh-B (CP-B) cirrhosis. 19  Pharmacokinetics of 30 patients with HCV genotype 1 infection and CP-B cirrhosis who received grazoprevir 50 mg and elbasvir 50 mg as separate entities was compared to that of 10 noncirrhotic patients with HCV genotype 1 who received grazoprevir 100 mg/elbasvir 50 mg combination product. Plasma samples over 24 hours at treatment week 4 were collected in a subset of CP-B and non-cirrhotic patients.
Acknowledgements Guest editors would like to thank all authors who contributed their research and ideas to this special issue, and as organizers of the InterDrought V conference (Hyderabad, 2017) extend this also to speakers at that meeting. We are also grateful to our organizations and to a number of donors, especially the  United States Agency for International Agricultural Development, Indian Council of Agricultural Research and ICRISAT  for their special support and co-organizing the conference. R. K. V. is thankful to his colleagues, especially  Ms Annapurna Chitkineni  for her help in organizing the conference and  Dr K. Himabindu  and  Dr Rakesh Kumar  for their help and assistance in editing the special issue. We dedicate this special issue to the memory of  Abraham Blum  who passed away on 10 March 2018.
Finally, we fit GLMMs for colony productivity using the package MASS  (Venables & Ripley, 2002). We included retrieval rates as a fixed effect and colony identity as a random effect. Because the productivity data were overdispersed (test for overdispersion: W = 8. 758, p < 0. 001), we used quasipoisson distributions in the GLMMs. We generated all plots using the package ggplot2  (Wickham, 2009).
Study design Physiological prospective randomized crossover study (Fig. 1 ), using noninvasive assessments, was approved by the Ethics Committee of St. Michael's Hospital (REB #14-338) and registered on clinicaltrial. gov (NCT02262871). Patients provided their written informed consent. The study took place in a respiratory ward where NIV is usually delivered at St. Michael's Hospital, Toronto, from January 2015 to February 2017.
Patients Fifteen adult patients with CF pulmonary exacerbations who had been stabilized over median 3 days under NIV and medical therapy were subsequently enrolled in the study (Additional file 1: Fig. 1 ). Patients were enrolled a median (interquartile range) of 5 (5-7) days after hospital admission. Characteristics of the subjects are detailed in Table  1. The NIV settings were those previously used by the clinicians and with which the patients had been stabilized. At baseline, all patients received oxygen via nasal prongs (range 0. 5-10 L/min). All patients completed the study, and one patient did not have ultrasound measurements due to the unavailability of the machine at the time of data collection. In two patients, there were technical difficulties with the transcutaneous CO 2 measurements.