dataesr/scientific-paragraphs-categorization huggingface dataset:
6. The mean and standard deviation of the HF fractional cross section of the PbPb data are indicated on the plots. Qualitatively, a similar particle-species dependence of v 2 is observed. However, the mass ordering effect is found to be less evident in PbPb data than in pPb data for all multiplicity ranges. In hydrodynamic models, this may indicate a stronger radial flow is developed in the pPb system as its energy density is higher than that of a PbPb system due to having a smaller size system at the same multiplicity. Moreover, the n q -scaled v 2 data in PbPb at similar multiplicities suggest a stronger violation of constituent quark number scaling, up to 25%, than is observed in pPb, especially for higher KE T /n q values. This is also observed in peripheral AuAu collisions at RHIC, while the scaling applies more closely for central AuAu collisions  [56].
Summary Measurements of two-particle correlations with an identified K 0 S or Λ/Λ trigger particle have been presented over a broad transverse momentum and pseudorapidity range in pPb collisions at √ s NN = 5. 02 TeV and PbPb collisions at √ s NN = 2. 76 TeV. With the implementation of a high- multiplicity trigger during the LHC 2013 pPb run, the identified particle correlation data in pPb collisions are explored over a broad particle multiplicity range, comparable to that covered by 50-100% centrality PbPb collisions. The long-range (|∆η| > 2) correlations are quantified in terms of azimuthal anisotropy Fourier harmonics (v n ) motivated by hydrodynamic models. In low-multiplicity pPb and PbPb events, similar v 2 values of K 0 S and Λ/Λ particles are observed, which likely originate from back-to-back jet correlations.
Introduction West Nile virus (WNV) is an African flavivirus originally maintained in sylvatic cycles mostly between mosquitoes and birds. Since the 1950s, there has been evidence of circulation outside its original ecological niches  [1]. Relatively large outbreaks of West Nile neuroinvasive disease (WNND) have been recorded in humans and/or horses in an increasing number of areas. WNV infections have been identified in many European and Mediterranean countries (Figure  1 )  [1, 2]. The increasing incidence of WNND, the appearance of new foci, and the endemic virus circulation in temperate areas have promoted research and innovation efforts financially supported by the European Commission (EC) under the 7th Framework Programme (FP7). However, knowledge gaps remain on several important aspects of virus ecology, biology, and pathogenicity.
In recent years, the availability of whole WNV genome sequences of European origin, necessary for diagnostic and molecular epidemiology and for better refining WNV taxonomic classification, has improved. A WNV strain bank and associated large genomic database has been built by the EuroWestNile consortium to facilitate this availability. The biobank and database are now available for the scientific community (www. eurowestnile. org). Analyses of full genome sequence data have shown that the virus, notwithstanding its high genetic stability, can adapt to new ecological niches through mutation and selection events  [21]. In addition to this plasticity, the presence of other flaviviruses infecting mosquitoes, birds and humans, may have important consequences not only for WNV ecology, epidemiology and pathogenicity, but also for diagnostics, surveillance and control strategies  [22].
Knowledge gaps on the impact of the presence of other flaviviruses on WNV epidemiology Another important knowledge gap is the impact that other co-circulating flaviviruses (and perhaps arboviruses) may have on WNV epidemiology in Europe. Cross-immunity between closely related flaviviruses might lead to some degree of cross-protection in the animal hosts, or, even enhance infection in certain cases. Further assessment of the consequences of viral interference on the immune response both in hosts and vectors in Europe is needed. Realistic WNV models should also incorporate these effects. The diversity of flaviviruses circulating in Europe, including mosquito-only flaviviruses as well as newly emerging flavivirus strains, represent another challenge for WNV research. Europe's preparedness to face emerging flavivirus threats depends largely on the availability of large datasets integrating genomic characterisation and analysis of all the strains isolated in Europe and neighbouring areas, including Africa.
Acknowledgments This paper is dedicated to the memory of our wonderful colleague and friend,  Dr Paolo Cordioli , who recently passed away. We thank the  European Centre for Disease Prevention and Control (ECDC)  for supporting this initiative and for promoting a stronger integration between public health and research in Europe. We also thank  Dr Jolanta Kolodziejek  of the  Institute of Virology, University of Veterinary Medicine Vienna  for establishing the phylogenetic trees and  Matteo Marcantonio  of the GIS/RS platform at the  Edmund Mach Foundation  for preparing the map in Figure 1. This paper is the product of a cooperative action promoted by the Coordinators and delegates of the  EU  funded projects on West Nile virus under the 7th European Programme, in particular the  EuroWestNile  project (www.
eurowestnile. org,GA n. 261391), the  EDENext  project (www. edenext. eu, GA n. 261504) the  Wings  project (www. west-nile-shield-project. eu , GA n. 261426), and the  Vectorie  project (www. vectorie. eu , GA n. 261466). This study was partially funded by  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext 142 (http://www. edenext. eu). AR was partially funded by the  Autonomous Province of Trento  grant LExEM (http://www. lexem. eu). The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission.
1 , dark purple and orange, respectively)  [35]. For comparative purposes we also used a clonal line for the NPCs containing the Tsix transcription stop. We improved the allele-specific mapping of sequence tags used previously  [22]  by applying a new procedure based on the GSNAP (Genomic Short-read Nucleotide Fig. 1  Overview of the setup to study dynamics of gene silencing on the Xi during XCI. Female ES_Tsix-stop ESCs  [39]  display non-random XCI due to a transcriptional stop in the coding region of Tsix, allowing allele-specific quantification of transcripts originating from the (future) Xi by RNA-seq (pink background). To investigate stable XCI from the same female ES_Tsix-stop ESCs, we performed RNA-seq on a clonal NPC line derived from the ES_Tsix-stop ESCs (*NPC_129-Xi, red background)  [35].
Only Xist is expressed higher from the Xi compared with the Xa, while four other genes (5530601H04Rik, Ogt, Kdm6a (Utx) and 2610029G23Rik) show roughly equal expression from the Xi and the Xa in all three NPC lines (Fig. 5b ). The remaining genes show (much) lower or no contribution of expression from the Xi (Fig. 5a, b ). In total 38, 34 and 18 genes escape XCI in the *NPC_129-Xi, NPC_Cast-Xi and NPC_129-Xi lines, respectively (Fig. 5a ; Table  1 ). Besides six genes that had too little or no coverage over polymorphic sites in our dataset, almost all escapers previously identified in mouse by Yang et al. [37]  (in embryonic kidney-derived Patski cells), Splinter et al. [35]  (in NPCs) and Li et al. [33]  (in neural stem cells) are escaping XCI in at least one NPC line. Only Shroom4 and Car5 are stably inactivated in the NPCs used for the current study, while they escape XCI in the Patski cells as reported by Yang et al.
5d  and 6a-c ). Interestingly the 10 kb promoter region of Ndufb11, positioned outside but in close proximity to escape region 3, is located within the TAD associated with region 3 (Fig. 6c ). This might explain the escape we observe for Ndufb11. To determine the TADs over the three escape regions on the 129-derived X chromosome (that is being inactivated during differentiation of the ES_Tsix-stop ESCs), we performed allele-specific calling of TADs. In line with the non-discriminative 129/Cast Hi-C analysis, the allele-specific Hi-C shows the presence of the domains overlaying the regions of escape on the 129-derived X chromosome (Fig. 6a-c ). For validation of the overlap between the escape regions and the TADs, we analyzed allele-specific RNA-seq data from very similar 129/Cast hybrid female NPCs generated by Gendrel et al.
Calling of allele-specific gene expression Within the individual samples, we used the mapped tags to determine the sequence tag coverage per allele for each of the 20,785,351 polymorphic sites using GSNAP tally. A total of 4,888,065 polymorphic sites were covered at least once in any of the samples used for this study. Per single polymorphic nucleotide, the pile-ups were subsequently assigned to either the 129 or the Cast allele using custom Perl-based scripts (the allelic counts over individual polymorphic sites for each of the Tsix-stop profiles are present within GEO GSE60738). To avoid including counts from positions which were reported to be polymorphic in the Sanger sequencing project and/or the NIEHS/Perlegen resequencing projects, but which were not present in the genotypes used for the current study, we selected polymorphic sites that were covered at least twice from both the 129 and the Cast allele.
Hi-C (data) analysis Collection of the cells for Hi-C and the Hi-C sample preparation procedure was performed as previously described  [79] , with the slight modification that DpnII was used as restriction enzyme during initial digestion. Paired-end libraries were prepared according to Lieberman-Aiden et al. [79]  and sequenced on the NextSeq 500 platform using 2 × 75 bp sequencing (Additional file 2: Table  S1 ). Reads were mapped to the reference mouse genome (mm9) using BWA MEM  [80]  with default parameters. Reads were filtered based on mapping quality score (mapQ ≥10) and PCR duplicates were removed. (Normalized) interaction matrices at a resolution of 40 kb and the corresponding two-dimensional heat maps were generated as previously described  [79]  using optimized LGF normalization (normLGF) from the R package HiTC  [81].
The Hi-C Domain Caller package was used to calculate the directionality index from the normalized interaction matrix and to determine domains and boundaries using default parameters  [51]. For allele-specific domain calling, we first filtered the 129-derived sequence tags using intersection and assignment based on the polymorphic sites between Cast and 129 (20,785,351 polymorphic sites as reported above). Allele-specific domain calling was identical to the procedure for the total data set.
Other datasets used RNA-seq of the ESC line E14 (male; 2i and serum) was obtained from Marks et al. [44]. RNA-seq of EpiSC lines was obtained from Veillard et al. [67]  and includes one newly generated profile from a female EpiSC that was obtained by nuclear transfer (NT)  [66]. Three-dimensional genome organization by Hi-C of male serum J1 ESCs grown on feeders was obtained from Dixon et al. [51]. For human, the Hi-C was generated from the female fibroblast line IMR90  [51]. Additional data of escape genes in NPCs was obtained from Gendrel et al. [52]. Escape genes from human were obtained from Carrel and Willard  [29] , and were profiled in hybrid lines generated from human female fibroblasts and mouse cells. Genes were considered as escapers in case of a ratio >5/9 in the hybrid lines.
Acknowledgements We thank  Eva Janssen-Megens  and  Yan Tan  for technical support with sequencing, and  Kees-Jan François  for bioinformatic support. Allele-specific mappings were carried out on the Dutch national e-infrastructure with the support of  SURF Foundation. The research leading to these results has received funding from the  European Union  grant  PluriSys  ( FP7/2009: 223485 ) and  ERC-2013-ADG-339431  "SysStemCell". Research in the group of HM is supported by a grant from the  Netherlands Organization for Scientific Research  ( NWO-VIDI 864. 12. 007 ).
5 mg/kg estimated weight), that was remotely administered using 1. 5 cc darts (Pneu dart) and a CO 2 powered tranquiliser gun (Dan Inject JM Standard)  [34]. Anaesthesia was maintained using isoflurane delivered via a portable gas vaporizer (Stinger, Advanced Anaesthesia Specialists, Gladesville, NSW, Australia). Six adult females suckling a pup were also captured using a modified hoop-net (Fuhrman Diversified, Flamingo, TX, USA), manually restrained and anaesthetised using isoflurane as described above. Each seal was instrumented with a GPS data logger programmed to collect location data every 10 min (males: MK10AF, Wildlife Computers, Redmond, WA, USA; females: FastLoc, Sirtrack, Havelock North, NZ) in order to assess the degree of overlap in foraging areas between sexes, and a small VHF transmitter (Sirtrack) to assist with recapture.
Comparison of the spatial and isotopic niche The 95% and 50% Utilisation Distribution (UD) probabilities were calculated for the 35 day period when male and female foraging trips overlapped. The Utilisation Distribution Overlap Index (UDOI)  [38]  was then used to quantify the degree to which males and females shared foraging areas. Location data were filtered using a maximum swim speed of 8 m. s -1 to remove erroneous locations  [39]  and linearly interpolated every ten minutes. Smoothing parameters for the UD were calculated using the ad hoc method  [40]  and bathymetry data were used as an habitat grid to avoid unrealistic UD probabilities across land. The R packages trip and adehabi-tatHR  [41]  were used to perform these analyses. Stable isotopes are increasingly used to studying the foraging ecology of wild animals  [42, 43].
Short-term sexual segregation was examined by comparing male and female plasma and red blood cells isotopic values using t-tests after checking the normality of the data and the equality of variances. Long-term niche differentiation was investigated by examining the isotopic signature of seals' whisker. Otariid whiskers show sexual and inter-individual variation in growth rate  [10, 49]  so that whiskers of similar length might not integrate a similar period of time. In order to compare groups and individuals over a similar time frame, specific whisker growth rates were calculated for each individual. Isotopic signature of otariid whiskers often present regular annual cycles along their length  [10, 13, 36, 49, 50]  which allows for growth rate to be estimated for each whisker. The periodicity of δ 13 C and δ 15 N values was assessed, using the wavelet analysis following Kernaléguen et al.
The degree of individual specialisation was calculated on the most recent 2. 5 years, a period of time depicted by all whiskers, so that all individuals contributed an equal weight to the analysis. The R package RInSp  [56]  was used to calculate individual specialisation indices. Significance of WIC/TNW was assessed using a nonparametric Monte Carlo technique to generate 10,000 replicate datasets under the null hypothesis that all individuals are generalists, from which P-values were calculated  [54]. All results are presented as mean ± standard deviation and results were considered significant at the P < 0. 05 level. All statistics were performed using R 3. 0. 3.
Acknowledgments The authors thank  Rachael Orben  and  Lucy Quayle  for their help in the field,  Gaël Guillou  for running stable isotope analysis and  Bernard Cazelles  for his precious help in the wavelet analysis. The logistical support of  Parks Victoria , in particular the Rangers from the Foster and Tidal River offices was crucial for the success of the study as was the skill and experience of the boat charter operators (Geoff Boyd).
Additional internet searches using Google and Google Scholar were also conducted. Grey literature was also searched for, following experts' inputs. The search strategy consisted of the following free search terms: 'cell therapy', 'ATMPs', 'ATMPs prepared on a non-routine basis', 'cell preparations', 'hospital exemption', 'pricing', 'price', 'reimbursement', 'funding', 'market access', and 'France'. The search was conducted in the French and English languages. There was no limitation in terms of publication dates. The cut-off date for search was May 2015. This research was structured following five main steps: 1) First, we identified the French regulatory framework for cell therapies, including regulatory categories and marketing authorisation processes. 2) We subsequently identified the health products categorised as cell therapies in France.
Isolation of total RNA We used RNeasy protocol for isolation of total RNA from CD34 + cells and granulocytes according to the manufacturer's instructions (Qiagen GmbH, Hilden, Germany). Concentration and integrity of total RNA was assessed using NanoDrop spectrophotometer (Thermo Fisher Scientific Inc. , Wilmington, Delaware, USA) and Agilent 2100 Bioanalyzer Software (Agilent Technologies, Waldbronn, Germany) comparing the ratio of 28S and 18S RNA peaks to ensure that there was a minimal degradation of the RNA sample.
DNA sequencing Genomic DNA was extracted from peripheral blood granulocytes of MPN subjects by using the proteinase K and phenol-chloroform technique. Single nucleotide mutation JAK2V617F was characterized by DNA sequencing after PCR amplification. PCR amplification was performed with wild-type JAK-2-specific forward primer 5 0 -tggcagagagaattttctgaact -3 0 and reverse primer 5 0 -ttcattgctttcctttttcaca-3 0 , confirmed by electrophoresis on an ethidium bromide-impregnated 1% agarose gel. PCR amplified samples were analyzed by sequencing on an automated ABI PRISM 3130 Genetic Analyzer (Applied Biosystems; Life Technologies, Carlsbad, CA, USA) with AB DNA Sequencing Analysis Software (v5. 2) by using the Big Dye Terminator v3. 1 Ready Reaction Cycle Sequencing Kit.
Database search and data analysis The Mascot Daemon software (version 2. 3. 0, Matrix Science, London, UK) was used to perform database search in batch mode with all the sample raw files. The following parameters were set for creation of the peak lists: parent ions in the mass range 400-4500, no grouping of MS/MS scans, and threshold at 1000. A peak list was created for each sample analysed, and individual Mascot searches were performed. Data were examined alongside homo sapiens entries in Uniprot protein database (http://www. uniprot. org/). Protein hits were automatically validated if they satisfy one of the following criteria: identification with at least one top ranking peptide with a Mascot score of more than 39 (p-value < 0. 001) or at least two top ranking peptides each with a Mascot score of more than 22 (p-value< 0.
05). When several proteins matched exactly the same set of peptides, only one member of the protein group was reported in the final list. To evaluate the false positive rate in these experiments, all the initial database searches were performed using the "decoy" option of Mascot. For quantification of peptide, the intensity of reporter ions (m/z: 113; 114; 115; 116; 118; 119 and 121) was divided by the intensity of the reporter ion (m/z: 117) for each measured compound. All ratios were normalized against the median intensity of reporters. Only proteins quantified with at least two peptides ratio for each condition were validated. Ratio were transformed into natural logarithms and plotted against the number of peptides subjected to MS/MS analysis. Gaussian curves were fitted on the smoothed histograms.
Microarray analysis The human oligo probe set was purchased from Operon Human Genome Array-Ready Oligo Set Version 4. 0 (Eurofins MWG Operon, Huntsville, AL, USA) which contained 35,035 oligonucleotide probes, representing approximately 25,100 unique genes. The human version 4. 0 was constructed based on the Ensembl human database build (NCBI-35c), with a full coverage on NCBI human Refseq dataset. The arrays were produced in house at the Center for Biologics Evaluation and Research, U. S. Food and Drug Administration by spotting oligonucleotides on poly-L-lysine coated glass slides by Gene Machines robotics (Omnigrid, San Carlos, CA). The MIAME (minimum information about a microarray experiment) guideline was adopted for the data presentation. Total human universal RNA (HuURNA, BD Biosciences, Palo Alto, CA) served as a universal reference control in the competitive hybridization.
CD34 + cells yielded lower amounts of total RNA, insufficient for microarray analysis. Therefore, we amplified total RNA using the Amino Allyl MessageAmp II amplified RNA (aRNA) Amplification kit (Life Technologies Corp. , Carlsbad, CA, US). This amplification protocol was performed both in CD34 + cells and granulocytes of MPN subjects, for parallel studies, according to the manufacturer's instructions and using 300 ng of total RNA for amplification. For microarray analyses we used 3 μg of amplified RNA. Labeled cDNA probes were produced as described  [22]. The prepared hybridization mixture of cDNA probe and aRNA was added on the array in slide and placed in MAUI hybridization chamber (BioMicro Systems, Inc. , Salt Lake City, UT, USA) and incubated overnight at 42°C. Data Filtration, normalization, and analysis were performed as already described  [22].
The microarray data were available from Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo; accession no. GSE55976). Within each group the average correlation was high: PV-0. 91; ET-0. 87; PMF-0. 75; Mut0-0. 78 (S1 Table ). The correlation was also very high for examined technical replicates of CD34 + cells-above 0. 92. Also, the average correlation coefficient was high among MPN samples in granulocytes: PV-0. 96; ET-0. 98; PMF-0. 74; Mut0-0. 96 (not shown).
Data treatment and statistical analysis Statistical analyses were performed using STATA 13 (StataCorp LP, College Station, TX, USA). Concentrations of H 2 O 2 , MDA, 8-OHdG and reducing capacity were log-transformed to normalize their distribution. The evolution of log(reducing capacity), log(H 2 O 2 ), log(MDA), log(8-OHdG) was analyzed using a linear mixed model with the subject considered as a random effect and considering within-day (between time points) and between-day (control and exposure day) differences as main independent effects. We applied a linear mixed model to assess associations with exposure variables for each volunteer (particle number concentration, gravimetric mass, geometric mean diameter, and acellular ROS production potential). To assess exposure variable interactions with biomarker concentrations more precisely, we divided volunteers in two exposure groups based on median exposure values to ROS production potential (low and high ROS); PNC exposure (low and high PNC); and particle GMD (smaller and larger GMD).
Acknowledgments The authors thank colleagues at the  Institute for Work and Health, Switzerland :  Nicolas Lozano-Concha  and  Sébastien Linder  for assistance in study data collection and characterization,  Dessislava Savova-Bianchi  for assistance in ELISA assay analysis and  Simon Deslarzes  for MDA analysis in EBC. The authors also extend their gratitude to  Francoise Secretan  and colleagues at the  Center for Clinical Research of the University Hospital of Lausanne  for assistance with volunteer intake and biological sampling.
The general problem considered here is the computation of the Voronoi polytope for a point set being Z n. With minimal modifications, we could treat a point set of the form ∪ m i=1 (c i + Z n ) with c i ∈ [0, 1[ n , i. e. crystallographic structures. In Section 2 we define the Voronoi polytopes used in this work and explain their geometry. In Section 3, the enumeration algorithms are developed in details. Those relies on the zsolve program for integer enumeration  [24] , the cdd polyhedral program  [12]  and the implementation is available from  [7]. In Section 4, the implementation is applied on the case of the root lattices A n and D n for the L 1 and L ∞ norms. Our algorithm relies in a key way on the hypothesis of rationality of the set L. It is possible that one could dispense from this hypothesis and build efficient and general algorithms.