dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial and high-time-resolution properties of the velocities, magnetic field, and 3-D electric field within plasma turbulence are examined observationally using data from the Magnetospheric Multiscale mission. Observations from a Kelvin-Helmholtz instability (KHI) on the Earth's magnetopause are examined, which both provides a series of repeatable intervals to analyze, giving better statistics, and provides a first look at the properties of turbulence in the KHI. For the first time direct observations of both the high-frequency ion and electron velocity spectra are examined, showing differing ion and electron behavior at kinetic scales. Temporal spectra exhibit power law behavior with changes in slope near the ion gyrofrequency and lower hybrid frequency. The work provides the first observational evidence for turbulent intermittency and anisotropy consistent with quasi two-dimensional turbulence in association with the KHI.
Acknowledgments This work was funded by the  NASA   MMS  project and  STFC(UK)  grants  ST/K001051/1  and  ST/N000692/1. S. J. S. thanks the  Leverhulme Trust  for its generous support via a  Research Fellowship. A. P. is thankful to LASP for support. The French involvement (SCM instruments) on MMS is supported by  CNES ,  CNRS-INSIS , and  CNRS-INSU. The authors thank the entire  MMS  team for their work on the mission and  Takuma Nakamura ,  Christopher Chen , and  Lorenzo Matteini  for useful discussion. Data are publicly available from the MMS Science Data Center at http://lasp. colorado. edu/mms/sdc/.
Fluorescent microscopy analysis Cells were grown until OD600 0. 1, and fixed with an equal volume of a 1×PBS solution containing 5% paraformaldehyde and 0. 06% glutaraldehyde. After overnight incubation at 4°C, the cells were washed twice in PBS and then incubated in a solution of 1 μg ml -1 HOESCHT 33258 (Thermofisher). After 20 min incubation, the cells were washed in 1×PBS, spread out on agarose pads and observed immediately using a Leica DM6000 microscope, a coolsnap HQ CCD camera (Roper) and Metamorph software. Chromosomal loci and NGFP-ParB were observed when cultures reached an OD600 between 0. 05 and 0. 1. Cells were then spread out on agarose pads and observed immediately using a Leica DM6000 microscope, a coolsnap HQ CCD camera (Roper) and Metamorph software. Image analysis was performed using the MATLAB-based software MicrobeTracker Suite  [48].
ChIP-seq analyses Library preparation and sequencing were performed by the IMAGIF facility (I2BC, Gif sur Yvette). Sequences were aligned against the reconstituted genome of our PAO1 strain (available on request). The sequencing results were analyzed as described in  [49]. Briefly, the number of reads for the input and IP data was smoothed over a 200 bp window (the estimated size of the fractionated DNA), normalized to the total number of reads, and enrichments fold were calculated for each base as the ratio of the number of reads in the IP fraction and the number of reads in the input fraction. Peak calling was done using Matlab functions. Regions presenting a tenfold enrichment were selected, except in the Δpar123 strain, for which an enrichment of 15 was preferred. The rational was to select for the approximatively 10 most abundant regions.
Acknowledgments We thank  L. Le Chat  and  F-X. Barre  for sharing Matlab functions,  V. Lioy  for help with highthroughput sequencing analysis, and past and present members of the  Boccard laboratory  for insightful discussions. We are also grateful to  B. Michel ,  Y. Yamaichi ,  J-L. Ferat  and  S. Duigou.
For example, the same protein was named selenoprotein R by one group  (12) , but discovered concurrently and designated by another group as selenoprotein X (13). This protein was then functionally characterized  (14)  and renamed MsrB1 (for methionine-R-sulfoxide reductase 1) (  15 ), but all three designations persist in the literature and/or databases. Another problematic example is the nomenclature used for thioredoxin reductases. The names for the first thioredoxin reductase, which had been known decades before its selenoprotein nature was discovered  (16) , are generally internally consistent, although they differ in the abbreviations used, e. g. TR1 and TrxR1  (17). The second and third thioredoxin reductases discovered, however, were named inconsistently by the authors, wherein the mitochondrial thioredoxin reductase was designated as TrxR2  (18)  and TR3  (19) , and the testis-specific thioredoxin-glutathione reductase has been alternatively labeled as TR2  (19) , TrxR3, or TGR.
g. , Cspg4 and Pdgfra) and not expressing myelin genes (e. g. , Mog and Mag), while Plp1-GFP sorted OLs were characterized by the expression of differentiation genes and the absence of progenitor markers  (Moyon et al. , 2015). DNA methylation mapping was performed by enhanced reduced representation bisulfite sequencing (ERRBS), which combined restriction enzyme digestion (i. e. , MspI) of genomic DNA with bisulfite sequencing and provided single-base resolution and highly quantitative data on the methylation state of cytosines throughout the genome. The mean coverage was 1. 47 million individual CpGs in the mouse genome (Table  S1 ), with the majority detected at gene promoters (Figure  S1C ). Among those, we identified 62,807 differentially methylated CpGs as OPCs differentiated (q value <0.
The overwhelming overlap (p < 10 À244 ) of our dataset and the published in vitro RNA-seq study  (Zhang et al. , 2014)  further strengthened the validity of our analysis (Figure  S2D ). To characterize the transcriptional consequences of genomewide distribution of DNA methylation, we overlapped the differential transcript expression with DNA methylation differences at promoters and represented the analysis as quadrant plot, based on statistical power (Figure  2B ). The most statistically significant differences (based on number of events and difference at the two developmental stages) were identified in two quadrants (I and IV). The highest significance was detected in quadrant IV (p = 8. 1 3 10 À19 ), which defined hypermethylated genes with decreased expression during differentiation and included genes related to neuronal lineage (e.
Consistent with the detection of normal lineage specification, we did not detect any upregulation of neuronally enriched gene categories. Because OL differentiation is characterized by alternatively spliced events  (Kevelam et al. , 2015; Nave et al. , 1987) , we interrogated our RNA-seq dataset in control and mutant cells (Figure  7A ). It has been suggested that DNA methylation in specific genomic regions is critical for exon skipping and intron retention splicing events  (Gelfman et al. , 2013; Yearim et al. , 2015). Consistent with these data, we detected severe impairment of exon skipping and intron retention splicing in mutant OPC (Olig1 cre/+ ; Dnmt1 fl/fl ;Pdgfra-GFP) compared to wild-type (Figure  7B ). These events occurred in GO categories identified as: myelination, lipid metabolism and cell cycle (Figure  7C ).
Gene Expression Analysis Approximately 250 ng of total RNA per sample was used for library construction with the TruSeq RNA Sample Prep Kit (Illumina) and sequenced using the Illumina HiSeq 2000 and the Illumina HiSeq 2500 instruments according to the manufacturer's instructions for 50-bp (OPC-OL) or 100-bp (Dnmt1cKO) paired-end read runs. High-quality reads were aligned to the mouse reference 10 Cell Reports 15, 1-13, April 26, 2016 genome (mm10), RefSeq exons, splicing junctions, and contamination databases (ribosome and mitochondria sequences) using the Burrows-Wheeler Aligner (BWA) algorithm. The read count for each RefSeq transcript was extracted using uniquely aligned reads to exon and splice-junction regions. The raw read counts were input into DESeq2 v1. 2. 5  (Anders and Huber, 2010)  to normalize the signal for each transcript and to ascertain differential gene expression with associated q values.
ACKNOWLEDGMENTS Thanks to  A. Alonso ,  Y. Li ,  X. Zhang ,  Y. Xin , and the  Flow Cytometry Core CyPS (Pitie-Salpetriere Hospital, Paris)  for experimental support. Thanks to  Drs. A. Sharp ,  C. Watson ,  J. Feng , and  L. Wrabetz  for insightful discussions and  Dr. J. Svaren  for comments on the manuscript. This work was supported by  NIH-NINDS  grants  R37NS042925  and  NS-R0152738  (P. C. ) and F31NS077504 (J. L. H. ), the  UK Multiple Sclerosis Society  (R. J. M. F. ), and  NIH-NIMH  grant  R01MH090948  (J. Z. ).
We have performed a comprehensive study of genomic homozygosity, using high-depth WGS data for 90 individuals, and Illumina HD SNP data from 800 individuals representing 80 breeds. These data were coupled with extensive pedigree data analyses for 11 breeds that, together, allowed us to compute breed structure, demography, and molecular measures of genome diversity. Our comparative analyses characterize the extent, formation and implication of breed-specific diversity as it relates to population structure. These data demonstrate the relationship between breedspecific genome dynamics and population architecture, and provide important considerations influencing the technological and cohort design of association and other genomic studies.
The pedigree analysis represents six of these groups, whereas the SNP and WGS analyses include breeds from all seven groups. The nonsporting group has the lowest representation overall, with seven breeds for both the SNP and WGS datasets, and no breeds in the pedigree data. The working group has the highest representation in the SNP and WGS datasets, with 19 and 16 breeds, respectively. Combined, the WGS, SNP and pedigree datasets represent 112 dog breeds. Genetic variation at 1,510,327 LD-pruned loci aggregated from WGS of 90 dogs representing 80 breeds was analyzed, providing an impressive breadth of diversity by which to compare breeds and individuals. Complementing this, ten dogs from each of 80 breeds were genotyped using the current industrystandard canine HD SNP array with 173,662 potential sites of variation, providing definition within breeds, but potentially lacking in private variation.
Additionally, SNP chip analysis might call a region as homozygous despite the potential for heterozygosity between genotyped SNPs, whereas, in WGS, essentially every SNP is genotyped, leaving no missed heterozygosity. In order to compare these two disparate datasets, multiple parameters (see Materials and Methods) were used to calculate homozygosity from the WGS data of single-dog breeds varying both the window size and the allowed heterozygosity within the window. Using the metrics of a 70 kb window and zero heterozygotes, approximately equivalent to a five SNP window from the chip data calculations, the single-dog WGS predicted greater LnH than the shared SNP chip LnH values across all breeds. The shared values of LnH from the SNP chip analysis most closely resemble the single-dog WGS LnH values when calculated with parameters of 1000 kb minimal length and zero allowed heterozygotes, although the breed pattern is not correlated (P=0.
899). In addition to single breed representatives, the WGS collection included six breeds for which two dogs were sequenced and two breeds for which three dogs were sequenced. For these breeds, shared LnH and RoH were calculated in the same manner used to assess shared values in the SNP-genotyped breeds. Across all eight breeds, the LnH of the first dog was lower using WGS data than for the SNP analyses. However, the relationship was reversed with the addition of the second dog from each breed such that the shared LnH Fig. 2. Genetic diversity parameters reflected in the pedigree databases. The ratio of effective to actual ancestors (f e /f a ) is measured on the primary axis and suggests the occurrence of a bottleneck event when over one, which is indicated with a dashed white line. The abbreviations used for each breed can be found in Table  S1.
Population characteristics reflect breed history As expected, pedigree records for all breeds were erratic and often incomplete prior to breed establishment in their country of origin. In spite of these inconsistencies, considerable across-breed patterns, as well as breed-or situation-specific fluctuations, were identified through pedigree analysis (Fig. 5 ). Breed recognition by a kennel club registry both requires and facilitates pedigree tracking, thus improving records for each breed relative to the point at which the origin registry granted breed status. Concurrent with establishment of a breed, we observe that levels of inbreeding increase steadily and often immediately. Additional increases are noted when the breed achieves registry recognition. This implies that organization of a breed reduces the available gene pool, first by closing the registration database or 'studbook' to the introduction of nonbreed-associated genetic variation, imposing an artificial population bottleneck.
2 ). By evaluating breed history and years of birth of influential ancestors, we can speculate as to the cause of most bottlenecks. We find that, in each case, 10-29% of the most influential ancestors in a breed could be traced to a 5-to 10-year period. These time periods coincide with import/export events in the case of the Borzoi, the recognition of the breed or breed club for the Golden Retriever and Norwich Terrier, and a drastic increase in popularity and population size in the Labrador Retriever. The databases used for this study were largely US-centric, hence bottleneck events at breed formation or coincident with importation of breeds to the US would be expected, but were not necessarily observed for all breeds. This likely represents events that predate the earliest available records.
Implications of homozygosity decay When shared homozygosity measures are calculated within a breed by sequentially increasing the number of dogs in the dataset from one to ten individuals, the LnH that is lost with each additional dog is assumed to be private to the individual or not fixed within that breed. Conversely, there is a point at which the homozygosity identified as shared across a pool of individuals is likely shared within the entire breed (Fig. 3 ). By utilizing the extent to which the LnH decreases with each additional dog added to the shared LnH calculation, an exponential rate of decay can be calculated, which can in turn be used to predict the number of individuals required to represent the entire range of variation within a given breed as inferred from the current SNP array.
Using the ten dogs of each breed genotyped on the Illumina HD SNP array, a standard non-adjusted association analysis was conducted with PLINK software  (Purcell et al. , 2007)  using high-decay-rate Papillons as controls and high-decay-rate Miniature Poodles as cases, and separately, with low-decay-rate Shetland Sheepdog controls and low-decay-rate Scottish Terrier cases (data not shown). With ten cases and ten controls, the comparison of Papillon with Miniature Poodle assigned highest significance to the region surrounding RSPO2 on CFA13. The equivalent analysis of lowdecay-rate Shetland Sheepdog and Scottish Terrier breeds did not indicate significant association with RSPO2. Subsequently, with sequential reduction of one random dog per breed from the highdecay-rate analysis, the RSPO2 region maintained the highest relative significance from n=10 through n=7, although the P-value did drop below genome-wide significance.
5% to 25. 5%. In the two breeds for which we used three WGS dogs to calculate shared LnH, the third dog reduced the initial LnH by an additional 8% in each case. These values might, therefore, support a cost/benefit argument for the inclusion of WGS from two dogs per breed, instead of only one, when considering study design. Despite the two genomic methods occasionally producing discordant values for breed-specific RoH and LnH, the significant correlation between the two distributions across all 50 breeds for which both SNP and WGS data was analyzed suggests that either SNP chip analysis or WGS of a small number of dogs would be sufficient to estimate degree of homozygosity when considered relative to other breeds. Although WGS from multiple individuals will produce continually more accurate data, through comparison to the database of breed homozygosity measurements provided here, single-dog sequencing homozygosity measurements can be sufficient to predict breed-wide genomic structure.
Pedigree datasets Data used for pedigree analysis in this study were provided by private breed databases for the following 11 breeds: Australian Cattle Dog, Belgian Sheepdog, Bernese Mountain Dog, Borzoi, Basenji, Golden Retriever, Labrador Retriever, Norwich Terrier, Nova Scotia Duck Tolling Retriever, Papillon and Portuguese Water Dog. Due to breed history, the Belgian Sheepdog database also includes several Belgian Tervurens and Belgian Malinois, and the Golden Retriever database includes early contributing breeds including several Flat-coated Retrievers, Irish Setters and Labrador Retrievers. The abbreviations used for each breed throughout this study can be found in Table  S1. Breeds for pedigree analysis were selected based on the availability of high-quality database information. To be entered into this study, the breed database had to include at least 10,000 individuals over >10 effective generations (g e ), calculated as described in the Pedigree analysis section, and reflect the modern breeding population of the breed by including at least 10% of the AKC-registered dogs for that breed, determined by registration data for 2014.
Exceptions were made for the Labrador Retriever and Golden Retriever breeds, which, due to their immense popularity, reflected only 0. 47% and 6. 55% of AKC registrations for those breeds, respectively. Despite this, the database reference population sizes for Golden Retriever and Labrador Retriever ranked first and third highest of included pedigree breeds. Breed databases were predominantly compiled by volunteer enthusiasts using registries, studbooks, historical records, dog-show entries and personal breeder records. Initial pedigree analysis required an internal error check whereby improbable relationships are flagged for manual correction. Breeds were removed from study inclusion when the pedigree database contained insurmountable inconsistencies. When data regarding country or year of birth was missing from the raw database, estimated values were extrapolated based on equivalent data from siblings, offspring, dam and sire, and assuming a standard generation interval of 2 years.
Demographics from the datasets are provided in Table  1 , demonstrating that these breeds reflect the variation present in American populations of modern dog breeds with regard to country of origin, population size, popularity and history. Pedigree databases ranged in size from 12,962 (Norwich Terrier) to 311,260 (Golden Retriever) dogs. The AKC began accepting breeds for registration in the 1880s and the earliest recognized pedigree breed in this study is the Borzoi from 1891 (http://www. akc. org/press-center/facts-stats/page-3/). The databases used in these analyses, however, record parentage as far back as the 1830s (Labrador Retriever), 1840s (Golden Retriever) and 1860s (Borzoi). Of the breeds included in the study, the Nova Scotia Duck Tolling Retriever was the most recent to gain recognition by the AKC, obtaining breed status in 2003.
Pedigree data for this breed, however, dates back to 1930. North America is listed as the predominant region of birth or initial registration for all 11 breeds, representing between 37. 04% (Norwich Terrier) and 76. 29% (Portuguese Water Dog) of dogs within the pedigrees of the modern dogs. The dogs not listed as being born in North America derive primarily from Europe, Asia and Oceania. All dogs born between 2005 and 2015 make up the reference population for each breed. The direct ancestors of those dogs were traced back through successive parent-offspring relationships to the earliest dogs within the respective database. Importantly, use of a reference population to represent the modern breed removes any individuals present within the database that do not genetically contribute to the development of the breed as it currently exists.
Pedigree analysis Pedigree completeness, inbreeding coefficients, and effective numbers of founders, ancestors and founder genomes were calculated using PEDIG software  (Boichard, 2002). On the basis of these calculations, population founders are construed to be individuals with no parental data, assuming that this indicates the creation period of that breed. However, because some of the pedigrees are lacking complete data for individuals at time points postbreed-formation, this is not completely applicable in our data set. To avoid confusion in terminology, we will refer to the PEDIG founder calculations instead in terms of 'earliest documented relative' (EDR). Thus, we account for individuals that might be founders in the traditional sense, as well as those individuals who have contributed genetically to the breed in more recent years but lack documented pedigree data.
Whole-genome sequencing data generation WGSs were compiled using data from 90 purebred dogs representing 80 distinct breeds. Seventy-two breeds were represented by one sequenced dog each, two dogs were sequenced for each of six breeds (Chow Chow, Bernese Mountain Dog, Greyhound, Rottweiler, Scottish Terrier, West Highland White Terrier), and three dogs were sequenced for each of two breeds (Flatcoated Retriever, Irish Water Spaniel). Data were obtained via the Short Read Archive (ncbi. nlm. nih. gov/sra) from previously published studies, or sequenced for this study by the National Institutes of Health (NIH) Intramural Sequencing Center (NISC) using the Illumina TruSeq DNA PCR-Free Protocol (Cat. # FC-121-3001) from DNA samples provided by the Ostrander laboratory (Table  S2 ). Previously unpublished data from 27 sequenced dogs are deposited in the Sequence Read Archive (http://www.
ncbi. nlm. nih. gov/sra). Libraries were sequenced on the Illumina HiSeq 2000 platform with 100 bp paired-end fragments of 300-500 bp. Paired data was aligned to the CanFam3. 1 reference genome (http://genome. ucsc. edu/ cgi-bin/hgGateway?db=canFam3) with the BWA 0. 7. 10 MEM algorithm  (Li and Durbin, 2009) , sorted with SAMtools 0. 1. 10 (Li et al. , 2009) , and screened for putative PCR duplicate reads with PicardTools 1. 119 (https:// github. com/broadinstitute/picard). Sequences were locally realigned based on documented and novel insertions-deletions  (Axelsson et al. , 2013 ) using GATK 3. 2-2 (DePristo et al. , 2011) , and training sets of dbSNP and Illumina Canine HD chip positions were used for base quality recalibration. HaplotypeCaller was used in 'gVCF' mode  (Van der Auwera et al.
WGS-based analysis of homozygosity The list of 7,095,427 SNVs from the 90 WGS dogs was pruned for excessive linkage using the 'indep' function of PLINK v1. 07  (Purcell et al. , 2007) , with a window size of 50 SNPs, a window step of five SNPs and a variance inflation factor of 2. The pruned variant set of 1,510,327 SNPs was used to calculate RoHs and LnH for each WGS using the 'homozyg' function of PLINK  (Purcell et al. , 2007). To determine optimal conditions for which SNP and single-dog WGS homozygosity measurements are most comparable, homozygosity was calculated per dog from the WGS data with parameters set at a window size of 10 kb, 70 kb, 100 kb and 1000 kb, each with allowed heterozygosity of zero, one, five, or ten heterozygotes per window, equaling 16 total homozygosity conditions for each WGS.
The scenario with 70 kb minimum LnH with zero allowed heterozygotes was designed to most closely mimic the parameters set for the SNP chip homozygosity analysis. Shared LnH and shared RoH were calculated for the breeds for which WGS was obtained from two dogs (Chow Chow, Bernese Mountain Dog, Greyhound, Rottweiler, Scottish Terrier, West Highland White Terrier) and three dogs (Flat-coated Retriever, Irish Water Spaniel) utilizing the same methodology and criteria implemented with the SNP data and applied to the pruned WGS.