dataesr/scientific-paragraphs-categorization huggingface dataset:
4 ). Across all three SNP panels, adjusting for nongenetic effects using PC generated threefold more eQTLs than the unadjusted analysis. For either adjusted or unadjusted expression data, imputing 2. 4M HapMap SNPs gained 6%-7% additional signals, while imputing the ;8M 1000G SNPs further increased the number of signals by 5%-8% (a total increment of 12%-15%). Power gain from increased marker density thus appeared to be largely independent of the beneficial effects of reducing measurement error. The increase of power through imputation and PC adjustment was largest for transcripts with heritability (H 2 ; narrow sense total heritability)  (Dixon et al. 2007 ) in the middle range (Fig. 5 ). The biggest gain in the MRCA data set was observed at 52% of transcripts with H 2 between 0. 6 and 0.
7 mapped in locally or at a distance after PC adjustment and imputation compared with 20% before the improved analysis. For the MRCE data set, the most improved category was for H 2 between 0. 3 and 0. 4, with increments from 15% to 66%. In addition to the gain in power, genotype imputation also produced a much denser and better localized map of eQTLs, as illustrated by the TIMM22 locus from the MRCA data set (Fig. 6 ). In this example, HapMap imputation produced minimal additional information, but the 1000G imputation increased the maximum -log10(P) from nine to 23 and allowed finer localization of the peak effect to the 39 UTR of TIMM22.
Cross-platform comparisons The association analyses above were carried out with individual transcripts, identified by Affymetrix or Illumina probes. In order to facilitate comparison between platforms, we next analyzed the results at the level of individual genes, identified by the Affymetrix or Illumina annotation. We defined that a gene was associated with an eSNP if any probe belonging to the gene was significantly (FDR < 5%) associated with the SNP. We note that Illumina and Affymetrix probesets annotated to the same gene may target different transcripts of that gene with the possibility of different association results  (Veyrieras et al. 2012). Using a common set of markers from 1000 Genomes SNP imputation, the MRCA (Affymetrix) and the MRCE (Illumina) data together identified genome-wide significant (Benjamini and Hochberg FDR < 5%)  (Benjamini and Hochberg 1995)  eQTLs for transcripts from 13,661 autosomal genes.
We found shared eQTLs for 2483 genes in both MRCA and MRCE data set, while 4819 genes were only mapped by the MRCA data and 6358 genes were specific to MRCE data (Supplemental Fig. 5 ). For particular gene-SNP associations, there were 136,466 gene-SNP pairs mapped in both MRCA and MRCE, while 416,952 were specific to MRCA and 492,861 were specific to MRCE. The large fraction of eQTLs specific to each platform is likely due to their particular designs  (Veyrieras et al. 2012). There are 7601 genes queried by at least one probe in both Affymetrix U133 Plus2 and Illumina Human6 V1 chips. Among these, 5625 genes were associated with an eQTL, and 2483 of them were mapped in both MRCA and MRCE (986 genes specific to MRCA and 2156 genes specific to MRCE). These findings are consistent with other studies that suggest that these Affymetrix and Illumina arrays provide complementary information for gene expression  (Pedotti et al.
Discussion In this study we generated two large eQTL databases for LCLs that identify robust local and distant eQTLs for more than 14,000 genes. This is more than six times larger than our previous eQTL database  (Dixon et al. 2007) , which has been used to identify association with expression for SNPs associated with asthma  (Moffatt et al. 2007 ), Crohn's disease  (Libioulle et al. 2007) , and type 1 diabetes  (Plagnol et al. 2009). Even before publication, our two newly developed eQTL databases have been used to interpret findings from large-scale GWAS, including human height (Lango  Allen et al. 2010) , body mass index  (Speliotes et al. 2010) , waisthip ratio  (Heid et al. 2010) , osteoporosis-related traits  (Hsu et al. 2010) , Graves' disease  (Chu et al. 2011) , pancreatic cancer  (Wu et al.
2012) , and pathways associated with basal cell skin carcinoma  (Zhang et al. 2012). Our systematic investigation of the GWAS database presented in Supplemental Table  3  suggests that eQTL analyses may add to the understanding of many other loci mapped for complex genetic diseases. Expression QTL results can be used to prioritize genes in a region of association before undertaking laborious and expensive analysis in animal models and other systems  (Teslovich et al. 2010). Current results suggest that many eQTLs could be shared across tissues  (Ding et al. 2010 ) and that constructing a compre- hensive catalog may increase the chances of capturing relevant transcripts for diseases that primarily affect particular organs and for which the relevant transcripts are only incidentally expressed in blood.
Genotyping and gene expression processing Global gene expression data were measured by two techniques in two independent samples. The first sample (MRCA) contained 405 children of British descent  (Dixon et al. 2007 ). The 405 children are organized into 206 sibships, including 297 sib pairs and 11 half-sib pairs. The families were identified through a proband with childhood asthma, and siblings were included regardless of disease status. Global gene expression in LCLs was measured using Affymetrix HG-U133 Plus 2. 0 chips. LCL cultures were harvested at log phase in the first growth after Epstein-Barr virus (EBV) transformation. Robust multi-array averaging (RMA)  (Bolstad et al. 2003 ; www. genome. org Irizarry et al. 2003 ) was used for background correction and normalization and to compute expression values.
Genotype imputation We performed genotype imputation in the MRCA and MRCE samples independently. We used the ILMN300K genotypes to mimic the data that might be used in a typical GWAS and to impute the polymorphic SNPs in the Phase II HapMap. We imputed genotypes for all polymorphic HapMap SNPs by using a hidden Markov model programmed in MaCH  (Li et al. 2010 ). The method combines genotypes from the study samples with the HapMap CEU sample ( July 2006 phased haplotype release) and identifies the stretches of haplotype shared between the study samples and the HapMap sample. For each individual, the genotype at the untyped SNP can be summarized by taking (1) the most likely genotype according to the posterior probability of the three possible genotypes and (2) allele dosage, the expected number of copies of the reference allele (a fractional value between 0 and 2).
Adjusting nongenetic effects in gene expression We estimated nongenetic contributions in gene expression measures using PC analysis  (Leek and Storey 2007; Stegle et al. 2010). PCs were estimated from the gene expression values, ignoring the family relationship in the data. Top PCs were included in the eQTL regression model as covariates. In order to determine the optimal number of PCs to use, we used genotyped SNPs and, starting with the first PC, included other PCs one at a time until the number of transcripts mapped locally no longer increased. Finally, the top 69 PCs were used in the Affymetrix expression data set (MRCA), and the top 61 PCs were used for the Illumina expression data set (MRCE).
Data access The database of our results is available to the public through our website at http://www. hsph. harvard. edu/faculty/liming-liang/ software/eqtl/. The gene expression data for the MRCA and MRCE cohorts are available through the European Bioinformatics Institute (EBI) (http://www. ebi. ac. uk) under accession number E-MTAB-1425 for MRCA, and accession number E-MTAB-1428 for MRCE. The genotyping data for the subjects is accessible through the European Genome-Phenome Archive (EGA) (https://www. ebi. ac. uk/ega/), which is also hosted by the European Bioinformatics Institute (accession number EGAS00000000137 for both MRCA and MRCE samples).
Searches The search aims to capture as comprehensive as possible a sample of literature published in peer-reviewed journals as well as other relevant literature. The following literature databases will be searched: Biofuels abstracts database by CABI, Web of Science, LAN TEEAL (Agriculture and natural resource management), and Directory of Open Access Journals. In addition, the following internet search engines will be used in order to maximize coverage: Google, Google Scholar, and Scirus [12-14]. Literature will also be searched in the websites of relevant organizations such as the Food and Agriculture Organization (FAO), the International Finance Corporation (IFC), the United Nations Framework Convention on Climate Change (UNFCCC), the World Resources Institute (WRI), the International Institute for Environment and Development (IIED), the International Union for Conservation of Nature (IUCN), the Rights and Resources Initiative, and the World Wildlife Fund (WWF).
Data extraction strategy and synthesis For the analysis we will categorize the data using the following five categories: mammals, birds, amphibians and reptiles, invertebrates, and plants. If we find enough studies on plants they will be classified according to plant functional groups. The estimates of species richness and abundance and their means and standard deviations will be extracted to a spreadsheet. Also, species similarity indices will be extracted. If information about the type of species that cause dissimilarities in the species composition, e. g. forest, edge, or light-demanding species, is available, it will be extracted and categorized accordingly. In regard to ecosystem functions the change and its direction (negative or positive changes) will be recorded. Finally, information about the potential sources of heterogeneity will be included in the spreadsheet and if enough data for any of the categories will be found it will be taken into account in subsequent data analyses.
Where insufficient data is provided for extraction, we will contact authors to acquire additional data. If there is insufficient amount of data to perform meaningful statistical analyses on biodiversity indicators or ecosystem functions, we will summarize the data narratively. At a minimum we will present narrative synthesis tables that will list all included studies together with key characteristics, including critical appraisal of methodology. The data will be categorized according to the subject, comparator and outcome. We will also summarize the requirements to mitigate impacts related to biodiversity in different standards.
Expression of CAZy genes in T. reesei and comparison with A. niger The degradation of plant cell wall carbohydrates is mediated by enzymes of four different classes: the carbohydrate esterases (CEs), the polysaccharide lyases (PLs), the glycoside hydrolases (GHs) and the auxiliary activities (AAs). These enzymes are classed, based on their primary amino acid sequence and related activity, into families in the Carbohydrate Active Enzyme database (CAZy) (www. cazy. org) [24]. Analysis of the T. reesei QM6a genome identified 22 CE-encoding genes representing 8 families, 5 PL-encoding genes representing 3 families, 195 GH-encoding genes, representing 49 families and 6 AAencoding genes, representing 1 family  [18]. The A. niger ATCC 1015 genome contains 25 CEs representing 9 families, 8 PLs representing 2 families, 239 predicted GHs representing 50 families and 7 AAs representing 1 family  [25].
niger hydrophobin-encoding genes which were also highly induced in the same conditions  [3]. The other three highly induced cell wall protein-encoding genes are likely to also have a role in mediating interactions of the fungus with a solid substrate. The QI74 cell wall proteinencoding gene has been shown to be induced in Trichoderma harzianium when replacing glucose medium with chitin, simulating mycoparasitic conditions  [44]. NCBI BLASTP analysis of the protein encoded by the gene with transcript ID 104277 (Table  2 ) revealed the presence of putative conserved domain which is also found in the HsbA (hydrophobic surface binding protein A) superfamily; an enzyme family which differs in structure and mechanism of surface interaction, when compared to hydrophobins  [43]. In A. oryzae, HsbA also recruits the cutinase CutL to PBSA to promote its degradation  [43].
RNA-seq and subsequent data analysis Ribosomal RNA was degraded in 10 μg of total RNA using the Ribominus Eukaryotic kit (Invitrogen). SOLiD whole transcriptome libraries were made according to the SOLiD Whole transcriptome kit protocol (Applied Biosystems) and the library concentrations were measured with the Quant-it HS dsDNA assay kit (Invitrogen). Libraries were pooled to equimolar amounts (Invitrogen) and gel purified using 2% size-select E-gels to 200-300 bp (Invitrogen). Emulsion PCR (0. 5 M final concentration of pooled libraries) and bead-based enrichment was done according to the SOLiD 4 Templated bead preparation guide containing library. Sequencing was carried out on a SOLiD 4 ABi sequencer platform according to manufacturer's instructions to generate single fragment 50 bp colour space reads.
Using the BioScope version 1. 3 Whole Transcriptome Pipeline (LifeTechnologies), reads from each SOLiD 4 libraries were initially filtered against sequencing library adaptors and other sequencing artefacts. Reads were then mapped independently to the entire unmasked and masked versions of the annotated genome assembly of T. reesei, and to defined gene transcript sequences (JGI T. reesei assembly version 2, annotation filtered models version 2. 0). For mapping against the genome assemblies, it was also possible to align reads against a library of exon junctions derived from the exon coordinates detailed in the genome annotation. This allowed reads spanning exon boundaries to be included in alignment results and be recorded in BAM format. Subsequently, the reads corresponding to rRNA encoding gene regions were removed from the BAM records before further downstream analysis.
The percentage of reads mapped uniquely to transcripts sequences, as a proportion of total transcript mapped reads, was 38. 49%, 54. 93% and 33. 83% for the conditions 48 h glucose, 24 h straw and 5 h glucose respectively (Additional file 5). For gene expression analysis the mapped read counts per gene calculated against the unmasked genome. Counts were determined for both sense strand only and to both strands (unstranded), as described previously. Antisense read counts per gene were calculated by subtracting the sense counts from the other. Read counts were then normalized to RPKM (Reads Per Kilobase per Million mapped reads) expression values for each gene  [3]  and visualised with the Integrative Genome Viewer (IGV 1. 5) programme  [3]. Differential expression values were determined using DEGseq  [23]  using sense read counts per gene for each experimental condition.
Acknowledgments We are grateful to  Dr. Christophe Paupy  from  IRD  and  CIRMF  and  Dr. Francois Rouet  from  CIRMF  for reviewing the manuscript and providing a great deal of useful advice. Special thanks to  Heidi Lanc  ¸on who revised the article. Obituary. Philippe BARBAZAN, Ph. D. (1950-2009) was a medical entomologist working for the  French Institute of Research for Development (IRD). For more than a decade he worked as a researcher, and was a mentor for the students, at  Mahidol University, Center  for  Vaccine Development and Center for Vectors and Vector-borne Diseases (Thailand). He dedicated his career to epidemiology of arthropod-borne viral diseases, and spatial and environmental factors affecting their transmission among several developing countries around the world. Sadly, Philippe passed away in June 2009 after months of courage fighting against cancer.
Acknowledgements The authors would like to thank  Pavel Khil  for helpful discussions. This research was supported by the  NIH  grant  1R01GM084104-01A1  from  NIGMS  (G. V. P. ); grants  FS71HU ,  R071HU  and  CS71HU  from  USUHS  (G. V. P. ) and by the  NIDDK Intramural Research Program (RDCO).
E-cadherin immunostaining showed an increased expression level and illustrated the increased cell-cell interactions in the DU145-LN4 cells (Figure  2A , lower panels). The intermediate cell lines, DU145-LN1, LN2 and LN3 also displayed progressively more epithelial-like characteristics (Supplementary Figure  1B ). We investigated the gene expression profile of these cells using an Illumina cDNA expression array. Using BRB Array Tools software, data were transformed and normalized as described in Methods. To identify differentially expressed genes, we applied analysis of variance tools typically used for time-course analysis. We selected for linear (positive or negative) gene expression changes, to correspond with the repeated rounds of in vivo cycles of lymph node metastasis in our model. We found that many of the genes associated with an epithelial phenotype were dramatically increased.
The most significantly decreased miRNA was miR-424 (Figure  4 ). MiR-200c was the most changed miRNA in the DU145-LN4 microarray data, with 5. 5 fold increase in expression relative to parental DU145, miR-141 was the second most changed (4. 6 fold), followed by miR-200b (3. 2 fold) and miR-200a (2. 6 fold). MiR-200 family expression was investigated using real time RT-PCR. MiR-200a, miR-200b, miR-200c and miR-141 were amplified from three independent RNA collections of the parental DU145, DU145-LN2 and DU145-LN4 cells. Data were normalized to the small housekeeping RNA, SNORD38B. Figure  5A  demonstrates that miR-200a, miR-200b, miR-200c and miR-141 expression were progressively increased in DU145-LN2 and DU145-LN4 cells, compared to DU145. MiR-200c and miR-141 showed a greater fold increase in DU145-LN4 relative to DU145 (.
Expressing clones were selected with puromycin. Additional stable miR-424 overexpressing DU145-LN4 cells were created by FuGENE 6 (Promega, Madison, WI) transfection with pCMV-MIR424 plasmid (Origene Technologies, Rockville, MD). Vector controls were transfected with empty pCMV vector, and stable cells were selected using neomycin. Phase contrast and fluorescent microscopy images were captured using a Nikon Eclipse TE200 microscope with an RT SPOT camera and SPOT Advanced v4. 0. 9. software (Diagnostic Instruments, Inc. , Sterling Heights, MI). Western blot analysis. Whole cell lysates were collected in modified RIPA buffer with EGTA and EDTA (Boston Bioproducts, Ashland, MA) with protease inhibitor cocktail (P8340, Sigma-Aldrich). Protein concentration was measured using a BCA protein assay kit (Pierce/Thermo Scientific).
MiRNA labeling, hybridization, scanning and expression profiling was performed using miRCURY LNA microarray service (Exiqon). PCR. Total RNA for PCR was reverse transcribed using iScript cDNA synthesis kit (BioRad). cDNA from DU145, DU145-LN and B16-F10 cell lines was amplified using mouse and human GAPDH primers with iQ SYBR Green Supermix (Biorad) using a DNA Engine Thermal Cycler (Biorad) machine. DNA was separated on a 2% agarose gel containing ethidium bromide. B16-F10 mouse melanoma cell cDNA was included as a positive control for mouse DNA. Real time PCR was performed using a DNA Engine Opticon 2 machine (MJ Research) and analyzed using Opticon 3. 1 software (Biorad Laboratories). cDNA was amplified using primers to mouse GAPDH, human GAPDH, CDH1 and ZEB1 (Real Time Primers, Elkins Park, PA), and ZEB2, FWD 59CAAGAGGCGCAAACAAGC and REV 59GGTTGGCAATACCGTCATCC  57.
Acknowledgments Research reported in this publication was supported by grants from the  Prostate Cancer Foundation  (BRZ), and by the  National Institutes of Health  under award numbers,  NIH   R01 CA37393  (BRZ),  NIH   R21 CA155728  (DRB) and  NIH K01 CA118732  (DRB). This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank  Courtney Barrows ,  Milind Chalishazar  and  Andrea Henricks  for technical assistance,  Melissa Anderson  for administrative assistance, and  Kristin Johnson  for graphical preparation of figures. We acknowledge  Ricardo Sanchez , HTL ( Ricasan Histology Consultants ) for tissue sections.
Observations In the following section, we describe new photometry and spectroscopy of these Upper Scorpius sources, as well as our data reduction. Observations with the Herschel Space Observatory's PACS instrument  (Poglitsch et al. 2010)  were carried out as part of the Herschel open time key project GASPS (P. I. Dent). Additional observations were carried out using the Receiver A and HARP instruments on the James Clerk Maxwell Telescope (JCMT). In Table  A. 1, we report the observation identification numbers (obsid) and settings for Herschel observations, as well as the date, instrument, and integration times for JCMT observations.
PACS photometry We carried out a full survey of our sample using the Herschel PACS instrument's photometric scan map mode (PacsPhoto). Scans were carried out at the medium scan speed of 20 ′′ /s, with 10 legs of 3 ′ length and separations of 4 ′′. Targets were observed in both the "blue" passband centered at 70 µm, and the "green" passband centered at 100 µm, with each simultaneously observing in the "red" passband at 160 µm. All but 4 early observations were carried out in a pair of 276 second scan-map observations at 70 • and 110 • , so as to provide more even coverage of the field of view and reduce smearing of the point spread function in the scan direction. The data were reduced with HIPE 9. 0 (Ott 2010) using the standard reduction pipeline for photometric data. For most objects, there are two scans each in the 70 and 100 µm bands, with a total of 4 accompanying scans in the 160 µm band.
While the measured effective temperatures could be used with stellar isochrones to select a stellar radius, the uncertain ages and large variations between isochrones make that approach far more uncertain. Though many sources appear to the eye to have photospheric K band emission, we omit this band from the fitting procedure as it could be potentially contaminated at small levels by dust at the sublimation radius. Using effective temperatures (T e f f ) from the literature, with typical uncertainties of 100 K, we select a closest match photospheric model  (Kurucz 1993). We also use the R=3. 1 reddening law of  Fitzpatrick (1999)  to deredden photometry. Then, we carry out a χ 2 minimization of the stellar radius and extinction using the Levenburg-Marquardt algorithm, as implemented in the IDL code MPFIT  (Markwardt 2009).
Gas-to-dust ratio To attempt to place a limit on the gas to dust ratio of the typical Upper Scorpius disk, we use the thermochemical modeling code ProDiMo  (Woitke et al. 2009; Kamp et al. 2010)  to predict [OI], [CII], and CO line emission for the median SED disk for gas-to-dust ratios of 100, 10, and 1. These values represent ratios ranging from that of ISM material and the presumed ratio in the youngest disks, down to the regime where disks may be considered to be transitioning to debris disks. We assume the disks have well mixed gas and dust, and adopt the disk geometry found by SED fitting (Sect. 6. 1). High energy UV photons play an important role in heating gas in the disk atmosphere, but the ultraviolet environment around these stars remains unknown. To compensate, we explored two UV emission scenarios, with a fractional luminosity of 0.
Acknowledgements. This work is supported by the  EU A-ERC  grant  291141  CHEMPLAN. C. Pinte acknowledges funding from the  European Commission 's 7 th Framework Program (contract  PERG06-GA-2009-256513 ) and from  Agence Nationale pour la Recherche (ANR) of France  under contract  ANR-2010-JCJC-0504-01. We acknowledge the  Service Commun de Calcul Intensif de l'Observatoire de Grenoble (SCCI)  for computations on the super-computer funded by  ANR  (contracts  ANR-07-BLAN-0221 ,  ANR-2010-JCJC-0504-01  and  ANR-2010-JCJC-0501-01 ). Financial support was also provided by the  Milenium Nucleus   P10-022-F  from the  Chilean Ministry of Economy  and from the  European Community 7th Framework programme  (Grant Agreement  284405 ).