dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Dr J Pairault  for providing 3T3-L1 cells, and  Dr Isabelle Dugail  ( INSERM UMRS 1166 , team 6  Nutriomics, Hopital La Pitié Salpétrière, Paris ) and  Dr Pascale Zimmerman  ( Centre de recherche en cancérologie de Marseille and University Leuven ) for generous gifts of antibodies. We thank  Dr J-C Gimel  for providing access to NTA technology and for helpful discussion regarding the analysis of the results.
Funding This work was supported by the  French National Research Agency  through the program  MilkChEST  no. ANR-12-BSV6-0013-04 ,  GIS APIS-GENE , by a regional grant from  Région Pays de la Loire  and by a research grant from  Société Francophone du Diabète (SFD). MD is funded by a  Ph. D. fellowship from the  French  research ministry and AF is financed by a Ph. D. fellowship from  Région Pays de la Loire.
Results are shown for the four independent adipocyte-derived EV biological preparations (total EVs, lEVs and sEVs) analysed by nano-LC-MS/MS analysis. PCA of identified proteins for each EV subtype shows a clear separation of lEVs from sEVs and total EVs. (C-E) Gene ontology (GO) terms of protein families enriched in lEV and sEV preparations. Identified proteins in lEV and sEV fractions, respectively, were classified according to cellular component (C), biological process (D) and molecular function (E) categories defined by the GO consortium using the Panther classification system database. (C) Cellular component GO analysis reveals an enrichment of membrane and organelle components for lEVs, and in the extracellular region and extracellular matrix for sEV. (D,E) GO categories with greater than five-fold enrichment are presented for lEV and sEV populations following classification according to biological process (D) or molecular function (E).
The ffmpeg iii open source server, for both streaming and transcoding, are built in separate VMs. The media contents (HLS fragments) are generated using ffmpeg transcoding servers, and are then streamed using ffmpeg streaming server (hosted in a separate VM). The floating IP address of the instance was chosen from the same IP subnet range of the edge cluster, so that ES can access the data from the cloud VM. The CC function was omitted, as the SLA level implementation was not considered in the testbed. VM3 was configured using Proxmox VE and acts as edge cluster controller -EO. VM3 also includes a DHCP server with authentication. To automate the orchestration process, a script is used to: i) monitor the session changeover of the clients from one edge to the other using the authentication server logs; ii) handle the container migration.
V. CONCLUSION AND FUTURE RESEARCH DIRECTIONS In this paper, we proposed a framework which leverages MEC to support diverse applications in smart city scenarios. To always ensure high QoE, the "follow me edge" concept is introduced. According to this concept, services move across edge servers as per the movement of its respective users. The proposed framework is validated using a real-life testbed. Edge mobility was tested using different storage types, different container sizes and different edge resources. Interesting results were obtained suggesting the migration latency depends on different used techniques. The obtained results also demonstrate that short migration latency does not necessarily guarantee high QoE. It becomes apparent that the complexity of the system arises as a tradeoff between short migration latency at the cost of possible data loss.
The main advantage of the model we proposed in this work is that it allows us to create a groupwise parcellation using linear operations. Assuming direct seed correspondence across subjects, as in the HCP data set, our model lets us remove the subject variability of each seed's tractogram by calculating the expected value across subjects: E s (g( Tsp )) = E s (β c + ˜ c + ˜ s ), = β c + ˜ c + E s (˜ s ) = β c + ˜ c. ( 7 ) where the last equality is due to E s (˜ s ) = 0 (Eq. 5). Since in our model the variabilities are normally distributed (Eq. 5), we can estimate the expected value across subjects by averaging a seed's tractograms across subjects. This allows us to create population-representative tractograms for each seed free of across-subject variability, which then can be clustered to create a groupwise parcellation.
Experiments and Results In the previous section we presented a model for the cortical extrinsic connectivity and a clustering technique to parcellate the whole brain. Our technique allows us to create single subject and groupwise parcellations, encoded with different levels of granularity in a dendrogram. Now, we show the results of applying our technique over the HCP dataset. First, we explain how the preprocessing step of tractography was made. Then, we elaborate in detail how we applied our technique. Later, we show that our groupwise technique creates results consistent when parceling different groups. Also, we show that our techniques creates parcels in accordance with those by Thiebaut de Schotten et al. (  2016 ) when parceling only the frontal lobe. Then, we present a proof-of-principle that our parcels are related to brain anatomy and functional specialization.
Human Connectome Project Dataset A total of 138 subjects (65 males and 73 females, ages 31-35) were randomly selected from the group S500 of the Human Connectome Project (HCP). For information on the acquisition protocols please refer to  Van Essen et al. (2012). Every subject has been already preprocessed with the HCP minimum pipeline  (Glasser et al. , 2013). Also, each subject's cortical surface is coregistered and represented as a triangular mesh of approximately 32000 vertices per hemisphere  (Glasser et al. , 2013). For each vertex, the corresponding label from Desikan's Atlas is known  (Desikan et al. , 2006). Finally, the group S500 contains tfMRI information representing the average response to functional stimuli in 100 unrelated subjects (U100)  (Barch et al. , 2013).
(25)  qRT-PCR reactions were performed using the QuantiTect SYBR Green Kit (Qiagen) in four independent samples utilizing a Rotorgene 5 (Qiagen), as described previously. (30)  All qRT-PCR test samples were normalized to the geometric mean of three housekeeper genes (cyclin D1 [Ccnd1], glyceraldehyde 3-phosphate dehydrogenase [Gapdh], and phosphoglycerate kinase 1 [Pgk1]), as described previously). (30)  Threshold cycle (C T ) values were obtained from the start of the log phase on Rotorgene Q Series Software and C T values analyzed in Microsoft Excel 97-2010 using the Pfaffl method. (30)  Data for mutant mice were expressed relative to wild-type mice, expressed as 1. For confirmation of the effects of splicing, cDNA was PCR amplified using gene-specific primers (Supplemental Table  S1 ), utilizing methods previously described (Sigma-Aldrich).
(7) The peak mean fluorescence ratio of the Ca 2þ i transient response after each individual stimulus was measured using Cytomation Summit software (Beckman Coulter) and expressed as normalized responses, as described. (7) Nonlinear regression of concentration-response curves was performed with GraphPad Prism (GraphPad, La Jolla, CA, USA) using the normalized response at each  [Ca 2þ  e ] for each separate experiment for the determination of EC 50 (ie,  [Ca 2þ  e ] required for 50% of the maximal response). The maximal signaling response was measured as a fold-change of the peak transient Ca 2þ i response to the basal Ca 2þ i response measured at 0 mM Ca 2þ e. The maximal signaling responses for variant and mutant AP2s proteins were expressed as a percentage of the wild-type AP2s protein maximal signaling response.
Structural and in vitro signaling studies of the Tyr20Asn and Ile123Asn AP2s variants Of the two missense variants, the Tyr20Asn was located in exon 2 and was due to a T-to-A transversion, and the Ile123Asn was located in exon 5 and was also due to a T-to-A transversion (Fig. 1 ). Bioinformatic analysis using Polyphen-2 and Mutation-Tasting software  (26, 27)  predicted the AP2s variants (Tyr20Asn and Ile123Asn) to be damaging and likely disease-causing (Polyphen-2 score 1, MutationTasting score 0. 99 for both variants). In addition, the evolutionary conservation of the Tyr20 and Ile123 residues in AP2s (Fig. 2A, B ) and absence of DNA sequence abnormalities that would alter the Tyr20 and Ile123 residues from >66,000 exomes (combined from the NHLBI-ESP and ExAC cohorts)  (36)  indicated that the Tyr20Asn, which is located 5 residues C-terminal from the FHH3-associated mutations at Arg15, and Ile123Asn variants could represent pathogenic mutations rather than benign polymorphisms.
1  and 2 ), which involved evolutionarily conserved residues and were predicted to be damaging and disease-causing, had not been previously reported in the ExAc or NHLBI-ESP databases, that contain data from >66,000 individuals,  (36)  thereby further supporting the likelihood that they may be disease-causing mutations. However, these missense ENU-induced variants, Tyr20Asn and Ile123Asn, were found not to alter CaSR-mediated signaling by in vitro assays (Fig. 3 ), and the molecular mechanism for this tolerance of the altered residues at codons 20 and 123 is likely owing to the position of the respective residues within the AP2s protein. Thus, Tyr20 lies within a region distal to the region of the AP2s that interacts with dileucine motifs and is not close to Fig. 6. Plasma calcium, phosphate, and PTH studies in Ap2s1 +/del17 mice.
(A, B) Plasma-adjusted calcium, (C, D) plasma phosphate, and (E, F) plasma PTH concentrations in male and female wild-type (Ap2s1 þ/þ ) and heterozygous mutant (Ap2s1 +/del17 ) mice. Each point represents an individual mouse. Mean values for the respective groups are indicated by horizontal solid lines. Ap2s1 +/+ and Ap2s1 +/del17 mice had similar plasma calcium, phosphate, and PTH concentrations, and these were also similar between the two sexes. regions of the protein known to be involved in interactions with other AP2 complex proteins (Fig. 2 ). Similarly, Ile123 lies within a loop structure close to the binding regions with other AP2 complex proteins, but the mutation is not predicted to disrupt any structures within AP2s or to generate steric hindrance within the structure (Fig. 2 ). Moreover, it seems that there may be a low frequency of Ap2s1 mutations as only five missense mutations and no splice or nonsense mutations have been reported in the ExAc and NHLBI-ESP databases.
(36) Indeed, the observed frequencies of missense and nonsense AP2S1 variants is significantly lower than that expected from the ExAc database  (36)  (observed Ap2s1 variants versus ExAc gene variants:   Expected ratio from heterozygous intercrosses for Mendelian inheritance of wild-type (Ap2s1 þ/þ ): heterozygotes (Ap2s1 þ/del17 ): homozygotes (Ap2s1 del17/del17 ) ¼ 1:2:1. missense ¼ 0. 008% versus 0. 1%; nonsense ¼ 0% versus 0. 01%, chi-square p < 0. 0001). This suggests that mutations that are predicted to disrupt the structure of AP2s, or its functions such as cargo recognition, are not tolerated. Thus, our studies highlight the limitations of bioinformatics and protein prediction software, which may be inaccurate,  (45)  and the importance of acquiring data from multiple sources, including modeling of mutations to predict possible outcomes of missense changes and in vitro functional characterization to assess known roles of the protein.
Acknowledgments This work was supported by the  United Kingdom Medical Research Council (MRC)  program grant ( G1000467 ),  Wellcome Trust   Investigator Award  (RVT), and  National Institute for Health Research Senior Investigator (RVT). AR was a  Wellcome Trust Clinical Training Fellow. Authors' roles: Study design: CMG, MS, LT, SW, and RVT. Study conduct: CMG, AR, MS, AP, TAH, and LT. Data collection: CMG, AR, MS, AP, TAH, and LT. Data analysis and interpretation: CMG, AR, MS, AP, TAH, LT, SW, SDMB,  RDC , and RVT. Manuscript preparation: CMG, AR, MS, AP, TAH, LT, SW, SDMB,  RDC , and RVT.
Identifying gold standard reference set for variant recalibration In addition to QUAL type quality scores, GATK outputs additional metrics such as depth, quality of depth, Fisher strand position, map quality rank sum, and read position rank sum for each variant based on read statistics (See Methods for details of these metrics). These values were used to train linear mixed models, using the GATK Var-iantRecalibrator  [30] , to assign a composite quality score (VQSLOD) to help detect type I and II errors. Training these models required a "gold standard" reference set of known genotypes across multiple individuals. Lacking this resource in the domestic horse, we defined three high confidence, putative "gold standard" datasets with which to train the VariantRecalibrator: 1) SNPs on the legacy SNP50 chip; 2) WGS variants which were seen in four or more (4+) calling groups (Additional file 1: Table  S1 ); and 3) WGS variants that were in the top 1 % of QUAL scores.
Both factors also had substantial coefficients in the model indicating a large magnitude of effect. In the logistic regression, the factor indicating hair root had a strong negative model coefficient of -2. 70 while DNA concentration had a positive coefficient of 1. 71. (see Discussion). After quality control steps for samples, genotypes were called for all 2,001,826 SNPs. Genotypes were assessed for clustering quality using the Metrics. R script provided by Affymetrix (see Methods). In total, 92. 2% of SNPs on the MNEc2M array passed quality control producing a set of 1,846,988 high-quality SNPs (1. 8 M; see Table  1 ) genotyped on the 332 horses remaining in the analysis (see Methods). SNPs exclusive to a single WGS variant calling group were validated (polymorphic), on average, at a rate of 80%.
Yet, validation rates were much higher in SNPs discovered in multiple calling groups (>96%) (See Additional file 3: Table  S3 ). Genotypes produced from the 332 horses with passing genotypes on the MNEc2M array ("SNP" genotypes) were combined with genotypes discovered from the 153 whole genome sequence horses ("WGS" genotypes) to create a dataset containing 1. 8 M genotypes for 485 horses ("WGS + SNP" genotypes). These variants were analyzed to select a subset of SNPs for inclusion on the MNEc670k array, with the intent that this array would be designed for genotype imputation to the full 2M SNP set. To maximize the information content of the MNEc670k array SNPs, multi-marker r 2 statistics were calculated on the 1. 8M high-quality candidate SNPs to identify 'tagging SNPs' that allowed for efficient reconstruction of genomic haplotypes in the 485 horses both within and across breeds.
Tagging SNPs that reconstructed haplotypes across all 485 horses (inter-breed tag SNPs) were identified using the software FastTagger  [31]. In total, 355,903 tagging SNPs were needed to reconstruct haplotypes present across all the horses in the cohort with a minor allele frequency (MAF) > 0. 01 and tag-SNP r 2 > 0. 99. These SNPs were included on the MNEc670k imputation array (see Additional file 4: Table  S4 ; Inclusion criteria: Inter). Haplotype tagging SNPs were also examined at the breed-specific level. Horses were split into 15 tagging breed groups based on the minimal sample size necessary to perform SNP tagging (see Additional file 5: Table  S5 ). Tagging SNPs were identified separately in each tagging breed group using FastTagger, then a subset of population specific tag SNPs were identified using the software Multi-Pop-TagSelect  [32].
Alternate allele frequency Frequency of alternate (non-reference) SNP alleles were calculated on both arrays using the full 485 sample dataset (WGS + SNP), genotypes derived from whole genome sequence only (WGS Only), and from samples genotyped on the 2M test array described above (SNP Only; see Additional file 5: Table  S5 ). Kernel density estimations (KDE) of the alternate allele frequency of SNPs on the MNEc2M array showed a mean frequency between 0. 20 and 0. 28 (See Fig. 4  and Table  5 ). In general, regardless of genotyping source, alternative allele frequencies shared similar distributions. Frequency distributions exhibit long tails indicating substantial numbers of samples with non-reference alleles. Alternate allele (ALT) distributions also exhibit a lower median Breed specific imputation accuracy (mean +/-s.
Breeds are ordered (TBLR) in the legend by their r 2 values at 400 kb that clearly out-performed the others (Fig. 2 ). However, using recalibrated VQSLOD scores coupled with a focus on identifying high precision SNPs, did result in a high conversion rate of probes (92. 3%) on the 2M test array. As more WGS data are generated in more horses allowing for further comparisons between SNPs genotyped using different technologies, and SNPs discovered here can be further validated, the ideal SNP training set for variant recalibration will become more formalized. Genotyping success in blood versus hair root DNA DNA samples genotyped on the MNEc2M array were primarily derived from blood, but also came from hair roots. Both DNA sources had failed samples, however, a substantially higher fraction of hair root samples produced poor genotyping rates.
It is important to note that the 96% average imputation accuracy in the Draft horses was mainly due to underperformance (92. 6%) in the Percheron horses. This is not a surprising result as Percherons were underrepresented in the Draft breed group (2 out of 18 individuals) thus removing both horses from the imputation reference population for validation yielded poor results. The inclusion or exclusion of samples in the reference population can significantly impact imputation accuracy. Here, it was critical to maximize the available data during the SNP discovery process, especially for those breed groups that had limited sample representation. While our imputation scenarios focused on imputing to the MNEc2M SNP set, imputation to higher SNP densities (>2M) is feasible. A recent study using whole genome sequencing from 44 Franches-Montagnes and Warmbloods imputed SNPs from the legacy SNP50 genotypes to nearly 13 M variants with 95% accuracy.
Conclusions Here we report, through a community effort, the leveraging of WGS data from 153 individuals with a 13X median coverage to achieve new variant discovery in the domestic horse. The use of WGS enabled us to generate orders of magnitude more data than previous technologies allowed. Empirical SNP properties were used to produce composite scores for each SNP based on machine learning approaches, allowing better detection of false positive variant calls that could not be achieved using generic QUAL scores alone. Thus, from a starting set of over 23 million SNPs, we identified a set of ~5 million SNPs which were considered for array design. With probe design recommendations from Affymetrix, we further filtered this list to 2M SNPs (MNEc2M), which adequately represented the breeds used in variant discovery, were evenly spaced across the genome, and had the highest chance of conversion in the array implementation.
Whole-genome sequencing and mapping pipeline Paired-end Illumina Hi-Seq 100 base pair whole genome sequences were generated for 153 horses representing 24 distinct breeds (Additional file 1: Table  S1 ). Raw reads for each individual were mapped using the PALEOMIX pipeline and aligned to an extended EquCab version 2. 0 genome (see below). Specifically, reads for each individual were processed separately by sequencing lane and filtered for quality control using the program Adapter Removal  [44] , which removed PCR adapters, trimmed low quality base-pairs, and collapsed overlapping paired-end reads into a single high-quality read, and the resulting reads were filtered by length. Passing reads were mapped to each reference file using BWA  [45]. Paired-end reads for which the mate was filtered were mapped in single-end mode.
PALEOMIX vcf pipeline implementation and python source code Programs within PALEOMIX are abstracted as nodes within the program to allow files to be generated within a temporary directory and only to be moved to the final directory upon successful completion. In addition to nodes for the read alignment mapping programs, additional nodes were created to run variant calling programs implemented by GATK  [26]  and SAMtools  [27]. Additional PALEOMIX nodes were created to perform variant recalibration as well as to assess precision versus recall for Morgan and Standardbred breed groups (Figs. 1  and 2 ). Source code for the extended PALEOMIX nodes are available at https://github. com/schae234/pypeline.
e. the variation seen on only the forward or reverse strand. Mapping Quality Rank Sum (MQRankSum) -The rank sum test for mapping qualities. Read Position Rank Sum (ReadPosRankSum) -The rank sum test for the distance of the variant from the end of reads. These parameters were used to train linear mixed models using GATK VariantRecalibrator which produces a composite variant quality metric called a VQSLOD score which can be summarized as using the formula VQSLOD ~DP + QD + FS + MQRankSum + ReadPos-RankSum. We defined three "gold standard" datasets: 1) SNPs previously genotyped on the 54K SNP chip, 2) variants called in four or more calling groups, 3) and variants within the 99th percentile of QUAL scores. Models were trained on chromosomes 2-31 and validated on chromosome 1 in both Morgan and Standardbred breed groups as a subset of each breed group had whole genome sequence data at target depths of 6X (12 Morgans and 6 Standardbreds) and 12X (6 Morgans and 4 Standardbreds) target coverage (actual coverage MOR6X: 5.
Availability of data and materials Whole genome sequences are available in the following NCBI BioProjects: PRJEB14779, PRJNA273402, and PRJEB10098. Additional sequences have are restricted in availability due to pre-existing material transfer agreements and can be requested by contacting the contributing investigator in Additional file 1: Table  S1. Genotypes for horses on the MNec2M array will be released upon publication. Genome positions for all 23 million discovered SNPs have been submitted to dbSNP as well as the European Variation Archive.
Pedigree analysis Performing controlled matings in wild populations is challenging, and hence to study inheritance patterns, we inferred pedigrees in three natural populations monitored over multiple years at three localities. Given the reduced dispersal in Z. borbonicus  [53, 54] , samples from these populations were more likely to include parents and their offspring. Pedigree reconstruction was performed using the Bayesian parentage assignment algorithm implemented in the R package MASTERBAYES v. 2. 50  [55]  and genotype data from an informative panel of 11 neutral microsatellites  [39]. A total of 261 birds of known sex were genotyped and included in this analysis. When known, a birth cohort identifier was specified to minimize the possibility that individuals appear as potential parents of offspring from the same or a previous cohort.
RAD-sequencing using pooled DNA samples To identify loci associated with colour, we used a paired-end RAD-sequencing protocol, using a dataset described previously in which six pools of 18-25 individuals representing the two colour morphs in each of three separate populations (named 'Bois Ozoux', 'Pas de Bellecombe' and 'Tévelave') were sequenced  [40]. We took advantage of the recent sequencing of the Z. lateralis genome  [56]  to map reads back onto this reference with BWA MEM (v. 0. 7. 12)  [57] , instead of creating consensuses directly from data as in  [40]. We aligned contigs and scaffolds from Z. lateralis on the zebra finch genome (version July 2008, assembly WUGSC v. 3. 2. 4) using LASTZ  [58]. We removed PCR duplicates using samtools  [59]. SNPs were called using Popoolation2 (v.