dataesr/scientific-paragraphs-categorization huggingface dataset:
VI's Canopy phenological variables are derived using MODIS as well as in-situ VI's as well as NEE time-series data. TIMESAT v. 3. 1 software has been selected for VI time-series processing. TIMESAT is available at the following URL: http://www. nateko. lu. se/TIMESAT/timesat. asp  (Jonsson and Eklundh 2002; 2004). With respect to TIMESAT options for use, the adaptive Savitzky-Golay method for time-series smoothing and the double sigmoid method to extract seasonally dependent variables from a timeseries have been selected for application. By definition, the adaptive Savitzky-Golay method smooths a time-series with a total of N points i, which comply with: (t i ,V i ) with taking values of i = 1, 2,. , N. Each point i is fitted with a quadratic polynomial function as defined by eq. 3: (eq. 3) For all 2k + 1 points within a time window ranging from n = ik to m = i + k, a linear combination of nearby values is solved according to eq.
Evaluation of NEE phenology For the nine sites listed in EVI has been derived from the 8-day 500 m surface reflectance MODIS product (MOD09G1, collection 5, being the same dataset used for the main analysis in this study) and is calculated according to eq. 7: (eq. 7) where  PAR is band 1 (620-670 nm),  NIR is band 2 (841-876 nm) and  blue is band 3 (459-479 nm). Furthermore, the same QA/QC flags applied for the VI's calculated for MODIS (see section 2. 3) have been used to check the QA/QC of the EVI time-series.
The performance of SGS MODIS and SGS in-situ estimates derived from different MODIS and in-situ VI's intended to predict SGS NEE, has been investigated similarly to the procedures described earlier in this chapter. However, the response variable selected for this case is SGS NEE. To confirm the hypothesis that differences between SGS NEE and respectively the SGS values derived from MODIS and in-situ VI's (i. e. SGS NEE -SGS MODIS and SGS NEE -SGS in-situ , respectively) are related to VI seasonality, VI type and PFT properties, we applied a general linear mixed effects model (GLMM). In this respect seasonality is represented by the amplitude of a VI time-series evaluated with the TIMESAT software. Using the GLMM, a boundary condition is that FLUXNET sites and measurement years are considered as random variables.
For many PFT's, SGS NEE shows a higher correlation coefficient value with SGS MODIS than with SGS in-situ , both SGS data derived from the NDVI (see Table  6 ). Woody savanna (WSA) elicits a very good correlation between SGS NEE and SGS MODIS-NDVI and SGS in-situ-NDVI (see Table  6 ). Nevertheless, only one site has been used for this PFT. Note however, that SGS MODIS-NDVI based estimates for deciduous forest (Table  6 : DBF; R 2 = 0. 74) elicits a high correlation coefficient. Moreover, the SGS MODIS-NDVI date is only only 8-9 days earlier than SGS NEE. [Figure  2 ] [Table  5 ] Note as well that SGS MODIS-NDVI dates correspond well with SGS NEE for grassland (GRA) and cropland (CRO). However, SGS in-situ-NDVI shows a higher R 2 and lower RMSE than SGS MODIS- NDVI for cropland. For cropland the MODIS WRDVI, EVI2 and GEMI show slighly higher scores than the NDVI (see Table  S3  and S4 in the Supplementary Data).
GLMM analysis To test the hypothesis that the prediction of the residuals of the SGS NEE date from MODIS and in-situ VI's (i. e. SGS NEE -SGS MODIS and SGS NEE -SGS in-situ , respectively) are related to VI seasonality, VI, PFT type and the variable amplitude (the difference between the maximum and minimum value of a VI), a GLMM analysis was performed. The results of the GLMM analysis of the residuals of the regression line between SGS NEE and SGS MODIS reveals significant two way interactions between each of the three explanatory variables (see Table  7 ). For in-situ data only the interaction effect PFT x VI significantly affects the residuals of the SGS NEE vs. SGS in-situ relationship. The other interctions were not significant and therefore they were not taken into account in the final model (amp*VI: F 5,285 = 0.
Acknowledgements This work used radiometric and eddy covariance measurements acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program  ( DE-FG02-04ER63917  and  DE-FG02-04ER63911 )),  AfriFlux ,  AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FLUXNET -Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS -Siberia , and  USCCC. We acknowledge the support to data harmonization provided by  CarboEuropeIP, FAO-GTOS-TCO , iLEAPS (the  Integrated Land Ecosystem-Atmosphere Processes Study , a core project of IGBP),  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, Université Laval  and  Environment Canada and US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California -Berkeley , and  University of Virginia.
2PLM setup and procedure The 2PLM setup is as described in  Lecoq et al. (2011), and Parpaleix et al. (2013). Briefly, the output from a Ti:sapphire laser (Mira, Coherent, Santa Clara, California; l = 850 nm, 120 fs pulse width, 76 mHz) is gated by an acousto-optic modulator (AOM, AA Optoelectronic, Orsay, France; MT110-B50-A1. 5-IR-Hk). This allows for repetitive cycles of alternating 'on' and 'off' periods to be generated, that correspond to periods of excitation and recording from the PtP-C343 probe in the sample. The excitation period was 24 ms and the recording period was 225 ms, for a total cycle period of 250 ms and a repetition rate of 4 kHz. The scanning of the excitation light by the galvanometric mirrors (Cambridge Technology, Bedford, Massachusetts) is synchronized with the gating of the laser output by the AOM, using custom-built electronics and LabVIEW software (National Instruments, Austin, Texas).
Phosphorescence decays detected during the 'off' period were averaged over a number of cycles (~5000-50,000 decays) and the lifetime of the phosphorescence determined by fitting a single exponential curve to the data. The first 6-7 bins (~5 ms) after the end of the on-phase were discarded. This lifetime measurement is then converted to a value of Po 2 using a calibration curve. We used the nonlinear least-squares method and the Marquardt-Levenberg algorithm to obtain the decay lifetimes and associated standard errors. We used a Stern-Volmer-like calibration curve to convert the phosphorescence lifetimes into the corresponding Po 2 values. This analysis was performed using custom build software (see below) developed using LabView (National Instruments).
The EAT-extraction custom-built software is now available online at the following address: https://github. com/charpak-lab/EAT-detection. Hemoglobin saturation (So 2 ) was estimated from Po 2 RBC using the Hill equation, with a Hill coefficient (2. 59) and P 50 (40. 2 mm Hg), which are accurate for C57BL/6 mice  (Uchida et al. , 1998)  and have previously been used to make estimations of mouse cerebral So 2 from Po 2 data  (Sakadz ˇic´et al. , 2014). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements We would like to particularly thank  Sergey Vinogradov  for providing PtP-C343. We also thank  Emmanuelle Chaigneau ,  Mathieu Ducros ,  Yannick Goulam-Houssen  and  Sergei Sasnouski  for software support, along with  David Attwell ,  Ravi Rungta  and  Etienne Audinat  for their critical comments.
, Valencia CA, USA) and sequencing libraries were prepared by the Nextera kit (Illumina Inc, San Diego, CA, USA), according to the manufacturers' protocols. Illumina paired-end sequencing was performed in three separate lanes on the Illumina HiSeq2000 platform. Aluca4 was sequenced at the Human and Molecular Genetics Center, Medical College of Wisconsin (Milwaukee, WI, USA). Auca15 and Aluca39 were sequenced at BGI (Shenzhen, Guangdong, China). The raw sequencing data are available from the NCBI SRA database via NCBI BioProject accession PRJNA280114 and PRJNA301923.
Whole Genome Shotgun Sequencing Data Assembly We used Platanus (v1. 2. 1)  (Kajitani et al. 2014 ) to generate a de novo assembly for A. lucayanum with trimmed WGS reads from the Aluca4 and Aluca15 libraries, which were from the same animal. We chose Platanus because it was designed to handle highly heterozygous genomes. The maximum difference for bubble crush (-u) was set as 0. 2 following the suggestion of the Platanus user manual for highly heterozygous genomes. The statistical summary for the assembly result was calculated by NGSQCToolkit (v2. 3)  (Patel and Jain 2012). This genome assembly has been deposited at DDBJ/ENA/GenBank under the accession LZCU00000000. The version described in this article is the version LZCU01000000.
Whole Genome Shotgun Reads Mapping For each animal sample, the software package Stampy (v1. 0. 23)  (Lunter and Goodson 2011)  with "divergence" set to 10% was used to map trimmed reads to the B. floridae reference genome (v2. 0)  (Putnam et al. 2008). The mapping alignments were further processed with three programs 1) SAMtools (v0. 1. 19) (Li et al. 2009) , 2) Picard-Tools (v1. 106) (http://broadinstitute. github. io/picard/, last accessed July 20, 2016), and 3) GATK (v2. 8-1)  (McKenna et al. 2010). Depth of coverage across the reference genome was calculated by GATK with a mapping quality cutoff of 20.
RNA-Seq Sequencing and Assembly The sequencing data from four RNA-Seq libraries used for this study included the following: 1) pooled A. lucayanum adults (denoted as asymAD), 2) pooled A. lucayanum 20h-larvae (phylotypic stage) (denoted as asym20h), 3) pooled B. floridae 20h-larvae (phylotypic stage) (denoted as bf20h), and 4) pooled B. floridae adults (denoted as bfAD). Details of reads processing and transcriptome assembly of the two A. lucayanum RNA-Seq libraries were previously described  (Yue et al. 2014). The raw reads of these two A. lucayanum RNA-Seq libraries have been deposited in NCBI SRA database via NCBI BioProject accession PRJNA235900. The raw asymAD transcriptome assembly has been deposited at DDBJ/EMBL/ GenBank under the accession GESY00000000 and version number GESY01000000.
The raw asym20h transcriptome assembly has been deposited at DDBJ/EMBL/GenBank under the accession GETC00000000 and version number GETC01000000. The B. floridae pooled 20h-larvae library was prepared by the RNA-Seq kit (NuGen Inc. , San Carlos, CA, USA) and sequenced by the Illumina GAII platform (paired-end 100 bp at the BioGem facility at University of California, San Diego, La Jolla, CA, USA). About 204 million raw reads were obtained for the bf20h library. Raw sequences are in the NCBI SRA database via NCBI BioProject accession PRJNA280115. The B. floridae pooled adults RNA-Seq library was constructed by as described by  Fidler et al. (2014). The raw reads were deposited in NCBI SRA database (accession number: PRJNA215261). We processed the bf20h and bfAD RNA-Seq data following the same protocol as for the two Asymmetron libraries: raw reads were processed by Trimmomatic (v0.
32)  (Bolger et al. 2014), prinseq (v0. 20. 4 )  (Schmieder and Edwards 2011b), and Deconseq (v0. 4. 3)  (Schmieder and Edwards 2011a)  sequentially to trim the sequence and remove potential contamination; Trinity (r20131110)  (Grabherr et al. 2011)   transcriptome assembly has been deposited at DDBJ/EMBL/ GenBank under the accession GESZ00000000 and version number GESZ01000000. The raw bfAD transcriptome assembly has been deposited at DDBJ/EMBL/GenBank under the accession GETA00000000 and version number GETA01000000.
Repeat and Coding Sequence Masking for the B. floridae Reference Genome To facilitate CNE identification, we created a copy of a repeatand-coding-masked B. floridae genome as follows: we first performed repeat-masking for the reference genome using the software package RepeatMasker (open-v4. 0. 3) (http:// www. repeatmasker. org, last accessed July 20, 2016) together with the B. floridae's repeats library (retrieved from http:// genome. jgi. doe. gov/Brafl1/Brafl1. home. html, last accessed July 20, 2016). Then we masked all the B. floridae CDS regions based on a previously corrected copy of B. floridae gene annotation file  (Yue et al. 2014). To exclude potential CDS regions as cleanly as possible, we used Exonerate (v2. 2. 0)  (Slater and Birney 2005)  to align the inferred CDSs from our A.
floridae reference genome formed our preliminary set of WGA-based CNEs. The second method is cross-species-reads-mapping-based (CSRM-based). In this method, we took the A. lucayanum-to-B. floridae reads mapping alignment and masked all the regions covered by < 5 reads (mapping quality cutoff = 20) along the B. floridae reference genome. To form our preliminary set of CSRM-based CNEs, the unmasked regions after such mapping-depth masking were used to identify the intersections with the repeat-andcoding-masked B. floridae reference genome. We annotated the noncoding RNAs (ncRNAs) contained in our preliminary CNE sets with a combination of BLASTN and an Infernal (v1. 0. 2)  (Nawrocki et al. 2009 ) search using the wrapper script rfam_scan. pl provided by Rfam, against the Rfam database (v11. 0) (Gardner et al.
Neighboring CNEs that are <10 bp away were rejoined together as a single CNE. The raw sequences of WGA-based CNEs for A. lucayanum-B. floridae comparison were deposited at GitHub (https://github. com/ yjx1217/SupplementaryData_for_Asymmetron_CNE_paper_ 2016. git, last accessed July 20, 2016). In parallel, we also identified WGA-based CNEs based on the comparison between two Branchiostoma species (B. floridae and B. belcheri) for downstream analysis. The genome assembly and gene annotation information of B. belcheri used for this analysis were retrieved from http://mosas. sysu. edu. cn/ genome/download_data. php, last accessed July 20, 2016 (v18h27. r3) (Huang et al. 2014). The raw sequences of WGA-based CNEs for B. floridae-B. belcheri comparison were also deposited at GitHub (https://github.
Results Sequencing, Assembly, and Mapping of the Asymmetron Genome Illumina sequencing of the three genomic libraries of A. lucayanum yielded about 351 million paired-end 100-bp reads and 294 million paired-end 90-bp reads from the Aluca4 and Aluca15 library, respectively, and another 285 million paired-end 100-bp from the Aluca39 library. The completeness of the sequencing was evaluated by matching our previous A. lucayanum RNA-Seq assemblies to the A. lucayanum WGS reads. Of the two RNA-Seq libraries, about 93. 37% and 98. 43% of the transcriptome contigs had significant hits (BLASTN, e-value < 1E-6) with the WGS reads, indicating that our WGS represented most of the A. lucayanum genome. Using the WGS reads from the Aluca4 and Aluca15 libraries, which are from the same individual animal, we attempted de novo whole genome assembly using the software package Platanus  (Kajitani et al.
One Hox4-CNE, one Gbx-CNE, one Msx-CNE, two Tbx2/ 3-CNEs, and three Znf503/703-CNEs were highly conserved with our sampled vertebrates (table 4 and supplementary figs S4-S11). We examined these eight CNEs using Ensembl (http://www. ensembl. org/, last accessed July 20, 2016) and the UCSC genome browser (http://genome. ucsc. edu/index. html, last accessed July 20, 2016) based on the well-annotated human genome (GRCh37/hg19). We found that the Hox4-CNE (Hox4-CNE-1) should be a 5'-UTR, whereas the other CNEs evidently have bona fide cis-regulatory functions, as suggested by several epigenomic signatures annotated by The Encode Project (The ENCODE Project Consortium 2007). Furthermore, the Msx-CNE (Msx-CNE-1), 5' of the coding sequence, and one ZNF503/703-CNE (ZNF503/703-CNE-1) have been experimentally verified by previous studies  (Holland et al.
Acknowledgments We thank  Dr Michael Hiller  for sharing the script for alignment entropy calculation,  Dr Michael H. Kohn  for helpful suggestions during manuscript writing,  Dr Robert A. Cornell ,  Dr Peter W. H. Holland ,  Dr Manuel Irimia ,  Dr Ignacio Maeso ,  Dr Matthew Blow ,  Dr Len A. Pennacchio , and  Dr Hiroshi Wada  and for providing detailed information about the amphioxus regulatory regions that they identified in their previous studies,  Dr. Nicholas D. Holland  for collecting the specimens of A. lucayanum,  Dr Koichi Kawakami  and  Dr Jose Bessa  for the reagents used in zebrafish transgenic experiment, and the anonymous reviewers for their valuable comments that helped us to improve the manuscript. J. X. Y. was supported by a doctoral fellowship from  Rice University  when conducting this study; he is currently supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  (n PDF20150602803).
Whole genome capture and sequencing B. microti genomic DNA was enriched from mixed DNA templates of tick and human samples using a custom hybridization capture array following the NimbleGen SeqCap method (Madison, USA)  [20]. Hybrid capture techniques overcome the need of propagation of B. microti in rodents or whole genome amplification, methods used to enrich pathogens prior to genome sequencing and known to introduce biases in strain representation and propagation of sequencing errors, respectively  [41, 42]. We used a dual-genome pathogen capture array targeting B. microti and B. burgdorferi genomes. Custom DNA probes were designed in silico to tile 99. 9 % of the B. microti R1 reference genome (GenBank: GCF_000691945. 1; genome size 6. 4 Mb, 4 chromosomes and 1 mitochodrion; GenBank: LK028575 apicoplast complete genome, 28.
Samples were then washed with a series of stringent buffers to remove non-specifically bound DNA fragments. The captured fragments were PCR amplified and purified with AMPure XP beads. Sample concentrations were normalized to 2 nM and loaded onto two lanes of Illumina version 3 flow cells at a concentration that yields 170-200 million passing filter clusters per lane. Samples were sequenced using 75 bp paired end sequencing on an Illumina HiSeq 2500 according to Illumina protocols. Sample de-multiplexing was performed using Illumina' s CASAVA 1. 8. 2 software. We used univariate logistic regression to detect correlates of B. microti capture efficiency and genome coverage. We used a quasi-poisson model to account for overdispersion. Statistical analyses were implemented in R version 3. 1. 1 (R Core.
Population structure Discriminant Analysis of Principal Components (DAPC), a non model-based method implemented in the adegent R package  [48, 49]  was used to describe genetic clusters using synthetic linear combination of variables (in this case SNP genotypes) that best differentiate between two or more groups of individuals. First, we ran sequential K-means clustering algorithm for K = 2 to K = 22 on the SNP data transformed by principal component analysis and subsequently clusters were identified using discriminant analysis (DA). We examined and compared the clustering solutions using Bayesian Information Criterion (BIC). Based on this analysis, K = 4 represented the optimal number of clusters to describe our B. microti SNP data set (Additional file 1: Figure  S2 ). To minimize overfitting of the data we retained two principal components, representing 58.
6 % of the total variance, following optimization procedure as recommended by the package developers  [50]  (Additional file 1: Figure  S4 ). We used ADMIXTURE  [22]  to estimate the optimum number of ancestral clusters in our dataset and to assess the ancestry of the 25 B. microti samples using all biallelic SNPs and a subset of SNPs thinned to remove those likely in LD  [22]. Considering only fully called sites, F ST was calculated in vcftools  [51]  using corrections for small sample size  [52, 53].
Phylogenetic analysis and divergence time estimation The 25 B. microti apicoplast genome sequences generated in this study were aligned using ClustalW  [61]  with default parameters, and the alignment was manually adjusted to maintain reading frame integrity in the protein coding genes. The resulting alignment contained 28,620 sites. The genealogical relationships among the apicoplast genomes were investigated using several approaches. First, we estimated phylogenetic networks using the statistical parsimony procedure implemented in the software TCS 1. 2  [24]  using the 95 % limit for a parsimonious connection. Phylogenetic analyses were conducted using maximum likelihood (ML) inference as implemented in MEGA v5. 2. 2  [62]  and Bayesian coalescent analysis using the BEAST package v1. 7. 1  [63].
The program jModelTest was used to determine the most appropriate substitution models for both ML and Bayesian analyses (TIM + G + I was selected using the Bayesian information criterion). We conducted BEAST coalescent analysis to estimate the genetic divergence of our apicoplast genome sequences and determine whether our data reflect changes in B. microti population sizes. We used the known substitution rate for the most closely related apicoplexan parasite available to calibrate our phylogeny and date internal nodes. The used substitution rate derives from a mitochondrial substitution rate for Plasmodium of 1. 2 × 10 -8 sub. /site/ year (1. 2 % per million years;  [23] ); a previous analysis of mitochondrial and apicoplast evolutionary rates suggested that they are broadly similar  [64].
Availability of data and materials Metadata and sequence data of each sample in this study were submitted to NCBI Short Read Archive (SRA; http://www. ncbi. nlm. nih. gov/sra/), under SRA accession: SRP058536. Sequence alignment and phylogenetic tree used to generate Fig. 2  were submitted to TreeBase (Accession number is 20043 and Study Accession URL: http://purl. org/phylo/treebase/phylows/study/ TB2:S20044).
The RENEB network was established with the aim to significantly increase individual dose reconstruction capacities in case of large-scale radiological scenarios by pooling resources and servicing needs. However, the value of RENEB to support topics also outside emergency preparedness is evident. With the established strategies to guarantee consistent performance between the partner laboratories, the network has the ability and capacity to contribute to large-scale research projects with the analysis of exposure biomarkers. Moreover, RENEBin combination with an open access database like STORE (http://storedb. org)maintains infrastructure for online-training activities such as image-based scoring of aberrations. Additionally, in case of radiological or nuclear emergencies, it offers secure storing and processing of large quantities of data for quality assured dose estimation in radiological incidents.
Acknowledgements This work was supported by the  European Commission  ( FP7 ,  GA 295513 ). We explicitly thank  Susan Fischer  for excellent assistance and administrative support. We express our gratitude for the valuable support and encouragement from the Advisory Board members - Elena Buglova ,  Zhanat Carr ,  Eduardo Herrera-Reyes ,  David Lloyd ,  Istvan Turai  and  Philippe Voisin. Last , not least, the enthusiasm and passion of all partners and candidates for this project cannot be emphasized enough, this is what made RENEB possible.
Copy number aberrations (CNAs) in known breast cancer driver genes  (Curtis et al. , 2012)  present in the PDTXs included gains/amplifications of MYC (78%), CCNE1 (34%), ZNF703 (25%), CCND1 (31%), MDM2 (25%), and ERBB2 (9%) and deletions of PTEN (41%), PPP2R2A (72%), CDKN2A (47%), and CDKN2B (47%). This CNA frequency distribution is different from that seen in a breast cancer clinical population (METABRIC dataset) and reflects both the origin of the PDTXs (around 45% were from metastatic biopsies) and the disproportionate engraftment of triple-negative basal-like cancers (IntClust10) and more-aggressive subtypes of ER+ tumors (IntClust1 and IntClust9; Figure  1B ). In contrast, we observed lower engraftment of ER+ tumors from better-outcome subtypes (IntClust3, IntClust4, IntClust7, and IntClust8; Figure  1B ; Table  S1 ).