dataesr/scientific-paragraphs-categorization huggingface dataset:
3 to -95. 7, P = 7. 5x10 -4 -2. 0x10 -3 ). Our study has identified new susceptibility variants independently conferring pancreatic cancer risk that merit functional follow-up to identify target genes and explain the underlying biology. risk loci  [16]. To discover additional pancreatic cancer susceptibility loci for individuals of European ancestry, we imputed three GWAS datasets including a total of 5,107 cases and 8,845 controls (PanScan I-III, Stage I)  [12]. For replication of promising signals, we first genotyped an additional 1,912 cases and 3,763 controls (PANDoRA; Replication 1), and then further assessed promising signals in a second set of 4,164 cases and 3,792 controls (PanC4; Replication 2). We identified three new susceptibility signals that achieved genome-wide significance for pancreatic cancer risk.
RESULTS We conducted imputation of three published pancreatic cancer GWAS datasets performed in individuals of European ancestry, PanScan I, II and III  [10] [11] [12]  using the 1000G (Phase 1, version 3) reference dataset  [17]. We included 9,132,527 genotyped or imputed SNPs with an imputation information (INFO) score >0. 5 and minor allele frequency (MAF) >0. 01, and performed a fixed effects meta-analysis to combine association results for a total of 5,107 pancreatic cancer cases and 8,845 control subjects  [10] [11] [12]. Little evidence of systematic inflation due to population stratification was observed (λ = 1. 02 for PanScan I+II and λ = 1. 07 for PanScan III). We attempted replication of promising findings in two stages. In the first replication stage, we genotyped 15 promising variants in 1,912 pancreatic cancer cases and 3,763 control subjects from the PANcreatic Disease ReseArch (PANDoRA) consortium, a case-control consortium including studies from eight European countries  [18].
DISCUSSION In this study, we performed imputation across three pancreatic cancer GWAS datasets, namely PanScan I, II and III  [10] [11] [12] , using 1000G reference data. Through replication of promising variants in individuals from two independent pancreatic cancer case-control consortia, PANDoRA and PanC4, we identified three new GWAS significant risk signals for pancreatic cancer. They are independent signals in previously established pancreatic cancer risk loci on chromosomes 1q32. 1, 5p15. 33 and 8q24. 21, as per conditional analysis, supporting their importance for pancreatic cancer risk. The signal on 1q32. 1 is located in NR5A2, a gene that encodes nuclear receptor subfamily 5 group A member 2 (NR5A2), a transcription factor important for pancreatic development and adult function in the pancreas, liver, intestine and ovary, where it regulates cholesterol synthesis, bile acid homeostasis and steroidogenesis  [19, 20].
This hypothesis needs to be formally investigated by future molecular studies. In conclusion, through imputation of three existing GWAS datasets and replication in two independent casecontrol consortia, we identified three new susceptibility signals for pancreatic cancer in populations of European ancestry. They are located in genomic regions previously reported by GWAS of pancreatic cancer, further supporting their importance for pancreatic cancer risk. Further work is required to identify target genes and explain the underlying biological mechanisms.
Genotyping, imputation and association analysis GWAS genotyping was performed at the Cancer Genomics Research Laboratory (CGR) of the National Cancer Institute (NCI) of the National Institutes of Health (NIH) using the Illumina HumanHap series arrays (Illumina HumanHap550 Infinium II, Human 610-Quad) for PanScan I-II, and the Illumina Omni series arrays (OmniExpress, Omni1M, Omni2. 5 and Omni5M) for PanScan III  [10] [11] [12]. The 1000 Genomes (1000G) Phase 1, Release 3  [17]  reference dataset was used to impute the PanScan I-III GWAS datasets using IMPUTE2  [72]  as previously described  [12, 24]. Due to the large overlap of variants on genotyping arrays for PanScan I and II, these datasets were imputed and analyzed together. The PanScan III data was imputed and analyzed separately. For quality control, variants were excluded based on: 1) completion rate < 90%; 2) MAF < 0.
Heterogeneity between studies was assessed using the Cochran's Q-test. IMPUTE2 information scores were 0. 78 (rs2816938), 0. 96 (rs10094872) and 0. 87 (rs35226131) for the three reported variants. Recombination hotspots for association plots were generated as previously described using SequenceLDhot  (10) (11) (12). The recombination hotspot inference was performed 5 times, each time utilizing a hundred random sampled EUR from the 1000 Genomes (EUR, n = 503). The linkage disequilibrium heatmap was prepared using the 1000 Genomes Phase 3 EUR data (n = 505, CEU+FIN+GBR+IBS+TSI), and snp. plotter R software package  [76].
HaploReg v4. 1 (http://www. broadinstitute. org/mammals/haploreg/haploreg. php) was used to annotate functional and regulatory potential of the most significant germline variants at each locus as well as highly correlated variants (r 2 >0. 7) that mark each of the regions identified  [25]. RegulomeDB (http://regulomedb. org/) was used to assess and score regulatory potential of variants in each locus  [26]. Expression quantitative trait locus (eQTL) effects were assessed using the Genotype-Tissue Expression Project (GTEx) database (http://www. gtexportal. org/home/) for pancreatic tissues (n = 149 samples)  [27].
All tissue samples were obtained from the Mayo Clinic in Rochester, MN. The project was approved by the Institutional Review Boards of the Mayo Clinic and the NIH. Nine pancreatic cancer cell lines (AsPC-1, BxPC-3, Hs766T, SU. 86. 86, SW1990, CFPAC-1, Capan-1, PANC-1, MIA PaCa-2) were purchased from ATCC and cultured as recommended (http://www. ATCC. com). The cell lines were tested for authentication with a panel of short tandem repeats (STR) using the Identifiler kit (Life Technologies) and compared with the ATCC and the DSMZ (German Collection of Microorganisms and Cell Cultures) STR Profile Databases. All cell lines matched the listed profiles.
ACKNOWLEDGMENTS The authors acknowledge the contribution of the staff of the  Cancer Genomics Research Laboratory (CGR)  at the  National Cancer Institute, NIH , for their help throughout the project. This work was supported by the  Intramural Research Program  of the  US National Institutes of Health (NIH) ,  National Cancer Institute. The content of this publication does not necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  U. S. Government. Additional acknowledgements for individual participating studies are listed in the Supplemental Materials.
The risk of relapse determining conversion to clinically definite MS was also significantly reduced following teriflunomide treatment in patients with early MS in the phase 3 TOPIC study  (Miller et al. , 2014). Teriflunomide has shown a consistent, manageable safety and tolerability profile  (O'Connor et al. , 2011; Confavreux et al. , 2014; O'Connor et al. , 2006; Miller et al. , 2014; Confavreux et al. , 2012) , and these results have been supplemented by extensive data from leflunomide clinical studies  (Emery et al. , 2000; Smolen et al. , 1999; Strand et al. , 1999)  and post-marketing surveillance. Here, we present safety results from pooled analyses of the teriflunomide clinical trial database, including long-term extension studies.
Data sets analyzed Analyses of safety outcomes were performed on two data pools (data cut-off date 3 May 2013. Pool 1 included patients from four placebo-controlled, double-blind core studies: phase 2 (NCT01487096); TEMSO (NCT00134563); TOWER (NCT00751881); and TOPIC (NCT00622700). Pool 2 comprised teriflunomide-treated patients from these same core studies, plus those enrolled in longterm extensions of the phase 2 (NCT00228163) and TEMSO (NCT00803049) studies (Tables  1  and 2 ). At the time of manuscript preparation, extension data from the TOWER and TOPIC studies were not available, and thus not included in Pool 2. Pool 2 was formed to include safety data from all patients treated with teriflunomide, regardless whether it was during a placebo controlled period or not. All studies were conducted in accordance with the International Conference on Harmonisation Guidelines for Good Clinical Practice and the Declaration of Helsinki.
Hepatic events Hepatic effects were monitored closely in the teriflunomide clinical program based on post-marketing reports of liver injury in patients with RA treated with leflunomide. In Pool 1, the proportion of patients with hepatic disorders was higher in the teriflunomide groups (14 mg, 215 [21. 5%]; 7 mg, 207 [19. 8%]; placebo, 152 [15. 2%]). The main contributor was transient and reversible ALT increases r 3 Â ULN. Eight patients (14 mg, n ¼2; 7 mg, n ¼1; placebo, n¼ 5) had concurrent laboratory values of ALT 4 3 Â ULN and total bilirubin 4 2 Â ULN (Hy's law range; Appendix); however, there was no suggestion of drug-induced liver injury and an alternative cause was identified in each case. Overall, data from Pool 2 did not suggest an increased risk of clinically relevant hepatic events with longer term exposure.
Acknowledgment Professor Christian Confavreux  ( University Claude Bernard, Lyon, France ), who sadly passed away in 2013, was a member of the Steering Committee and contributed to the design and conduct of the individual studies, as well as to the analysis and interpretation of the data. Studies reported in this manuscript were supported by  Genzyme , a  Sanofi company. This manuscript was reviewed by  Stephanie Jurgensen, MPH , of  Genzyme , a Sanofi company. Assistance in preparing the manuscript was provided by  Catherine Simonson , of  Fishawack Communications , and was funded by  Genzyme.
Similarly, within polynyas there is a narrow opportunity for phytoplankton growth, the timing of which plays an important role in both biogeochemical cycles  (Smith Jr. and Barber, 2007)  and biological production  (Arrigo and van Dijken, 2003; Ainley et al. , 2010). However, while studies have suggested that the timing of sea ice retreat is synchronized with the timing of the phytoplankton bloom, other factors such as wind forcing  (Chiswell, 2011) , thermal convection  (Ferrari et al. , 2014)  and iron availability  (Boyd et al. , 2007, and references therein)  play important roles as well. In this study we use the long-term passive microwave sea ice concentration data record to evaluate variability and trends in the MIZ, pack ice and polynyas from 1979 to 2014. A complication arises, however, as to which sea ice algorithm to use.
g. , southern Weddell and Ross seas). While any pixel with SIC < 0. 8 adjacent to the coastal boundary is flagged as potential polynya when stepping northwards, if a pixel is already flagged as MIZ or consolidated pack ice when stepping southwards, it remains flagged as MIZ or pack ice. After that analysis, a check for pixels with SICs less than 0. 8 is done to flag for broken ice or open water. Thus, during these months (e. g. , December to February or March), the physical interpretation of the different ice classes may be less useful. Using the binary classification scheme, daily gridded fields at each 25 km pixel are obtained. Using this gridded data set, we then obtain regional averages for five different regions as defined previously by  Parkinson and Cavalieri (2012). These regions are shown in Fig.
In this study, the positive trends in circumpolar sea ice extent over the satellite data record are primarily driven by statistically significant trends (p < 0. 05) in expansion of the consolidated pack ice in both sea ice algorithms. However, an exception occurs in the NASA Team sea ice algorithm after the ice pack reaches its seasonal maximum extent when the positive trends in the pack ice are no longer as large nor statistically significant. Instead, positive trends in the MIZ dominate during September and October (p < 0. 10). This is in stark contrast to the Bootstrap algorithm, which shows a declining MIZ area from March through November. The algorithms also give different proportions of how much the total ice cover consists of consolidated ice, MIZ or polynya area. In some regions, such as the Pacific Ocean sector, the NT algorithm suggests the MIZ is the dominant ice category, whereas in the BT algorithm the pack ice is dominant, which is true for all sectors analyzed in the Bootstrap algorithm.
Acknowledgements. This work is funded under  NASA  grant  NNX14AH74G  and  NSF  grant  PLR 1341548. We are grateful to  Sharon Stammerjohn  for her helpful comments on the manuscript. Gridded fields of the different ice classifications from both algorithms are available via ftp by contacting  Julienne C. Stroeve. We thank all the wintering fieldworkers involved in the collection of snow petrel data at  Dumont d'Urville  for more than 50 years, as well as the  Institut Polaire Français Paul-Émile Victor  (program IPEV no. 109  to  H. Weimerskirch ),  Terres Australes et Antarctiques Françaises  and  Zone Atelier Antarctique  ( CNRS-INEE ) for support. Edited by: C. Haas Reviewed by:  H. Flores ,  S. Kern , and one anonymous referee.
Context. Studies measuring the star formation rate density, luminosity function, and properties of star-forming galaxies are numerous. However, it exists a gap at 0. 5 < z < 0. 8 in Hα-based studies. Aims. Our main goal is to study the properties of a sample of faint Hα emitters at z ∼ 0. 62. We focus on their contribution to the faint end of the luminosity function and derived star formation rate density, characterising their morphologies and basic photometric and spectroscopic properties. Methods. We use a narrow-band technique in the near-infrared, with a filter centred at 1. 06 µm. The data come from ultra-deep VLT/HAWK-I observations in the GOODS-S field with a total of 31. 9 h in the narrow-band filter. In addition to our survey, we mainly make use of ancillary data coming from the CANDELS and Rainbow Cosmological Surveys Database, from the 3D-HST for comparison, and also spectra from the literature.
We perform a visual classification of the sample and study their morphologies from structural parameters available in CANDELS. In order to obtain the luminosity function, we apply a traditional V/V max method and perform individual extinction corrections for each object to accurately trace the shape of the function. Results. Our 28 Hα-selected sample of faint star-forming galaxies reveals a robust faint-end slope of the luminosity function α = -1. 46 +0. 16  -0. 08. The derived star formation rate density at z ∼ 0. 62 is ρ SFR = 0. 036 +0. 012 -0. 008 M yr -1 Mpc -3. The sample is mainly composed of disks, but an important contribution of compact galaxies with Sérsic indexes n ∼ 2 display the highest specific star formation rates. Conclusions. The luminosity function at z ∼ 0. 62 from our ultra-deep data points towards a steeper α when an individual extinction correction for each object is applied.
Narrow-band data: the VLT/HAWK-I survey The main data used in this work correspond primarily to very deep NIR imaging observations with the HAWK-I instrument at the VLT. HAWK-I  (Pirard et al. 2004; Casali et al. 2006; Kissler-Patig et al. 2008; Siebenmorgen et al. 2011 ) is a NIR (0. 85-2. 5 µm) wide-field imager installed at the Nasmyth A focus of ESO VLT UT4. The field of view is 7. 5 ×7. 5. It is composed of four HAWAII 2RG detectors of 2048×2048 pixels. The pixel scale is 0. 106 /pix. A narrow-band (NB) filter centred at 1. 061 µm (NB1060) was employed. The data was obtained as part of an ESO large programme between September 2008 and April 2010 (project 181. A-0485(A),  Clément et al. (2012) ), that was devised to detect Lyα emitters (LAEs) at z = 7. 7 in order to constrain the epoch of reionization.
Photometric ancillary data The CANDELS survey  (Grogin et al. 2011; Koekemoer et al. 2011 ) is a powerful imaging survey of the distant universe carried out with the HST. It is designed to study galactic evolution from z = 8 to 1. 5. To accomplish this objective it employs deep imaging of more than 250 000 galaxies with the WFC3/IR and ACS instruments. For the present work we were interested in the advanced data products that the CANDELS project had released in the GOODS-S field. The CANDELS GOODS-S Multiwavelength catalogue  (Guo et al. 2013)  presents the multiwavelength photometry available in this field. It combines the newly obtained data from HST/WFC3 F105W, F125W, and F160W bands with previous ground-based public data. This catalogue is based on source detection in the WFC3 F160W mosaic, that includes the data from the CANDELS deep and wide programmes.
2013)  to characterise the TIR luminosity of the galaxies. A description of the method used to derive consistent mid-to-far IR spectral energy distributions (SEDs) and rest-frame luminosities was presented in  Pérez-González et al. (2008a)  and  Barro et al. (2011b). The mid-and far-IR fluxes and derived TIR luminosities for the sources in the CANDELS catalogue presented in  Guo et al. (2013)  are publicly available through the Rainbow Cosmological Surveys Database  (Pérez-González et al. 2008a; Barro et al. 2011a,b)  1. The Rainbow Database also includes rest-frame nearultraviolet (NUV) and FUV luminosities based on synthetic photometry (see  Pérez-González et al. 2008a; Barro et al. 2011a,b, for details). We used these sources to collect several measurements required for our study: astrometry, photometric redshifts (photoz's), stellar masses, structural parameters, photometry, restframe absolute magnitudes, synthetic TIR luminosity, and synthetic FUV and NUV magnitudes.
We refer to TIR synthetic luminosity as that calculated via integration of SED from 8. 0 µm to 1000 µm. The FUV and NUV synthetic magnitudes are those obtained integrating the SED over the filter profiles. We found counterparts in the CANDELS database for 42 objects out of the 46 candidate emission-line emitters in the original raw sample. They were confirmed as ELGs by the stellarity parameter, photo-z's, and visual inspection of the postage stamps. The four objects with no CANDELS identification are those with reference numbers HAWKI0003148, HAWKI0003151, HAWKI0003361, and HAWKI0003606 in our original catalogue. If we try to visualise them in the CANDELS multicolour Interactive Display 2 , we find that HAWKI0003148 and HAWKI0003151 are located beyond the images edges. HAWKI0003361 is either a star or it is contaminated by a neighbouring object.
Morphologies We generated RGB postage stamps of the objects in our sample from the Rainbow Database. We used three filters of HST/ACS. The colour blue was assigned to the F435W band, green to the Gómez-Guijarro et al. : Properties of galaxies at the faint end of the Hα luminosity function at z ∼ 0. 62 Table  1. Summary of numbers and percentages of Hα emitters and interlopers at other redshifts within the 42 galaxies with CANDELS counterparts.
Line a z Number Percentage Hα ∼ 0. 62 28(12) 67%(19%) [S iii] ∼ 0. 2 1 2% Hβ ∼ 1. 17 8(1) 19%(2%) [O ii] ∼ 1. 8 3 7% (. ) > 2. 5 2 5% Notes. The numbers and percentages of the spectroscopically confirmed objects are in parentheses. -0. 5 0. 0 0. 5 1. 0 1. 5 2. 0 0. 0 0. 5 F606W band, and red to the F850W band. In this configuration, we observe the rest-frame spectral range of NUV, B, and V bands for an object at z ∼ 0. 62. Therefore, we show regions associated with star formation and young populations in blue and green and a more evolved stellar population in red. In addition, we searched in the II/258 Hubble Ultra Deep Field Catalog (UDF)  (Beckwith 2005)  and found 11 results. We used the UDF SkyWalker 6  tool to create images with a greater exposure time and quality than the ones we obtained from the Rainbow Database for these 11 objects.
The filters used in this case are similar: ACS F435W, F606W, F775W, and F850LP. We present the final RGB postage stamps of the 42 objects with CANDELS counterparts in Fig. 3. Thirty-one of them come from the Rainbow Database and 11 from the UDF Skywalker. 1. 0 1. 5 2. 0 2. 5 V-J U-V log (M/M ☉ ) > 9. 5 8. 5 < log (M/M ☉ ) < 9. 5 log (M/M ☉ ) < 8. 5 We were able to develop a visual morphological classification for our objects. We used the z band ACS image in the GOODS-S field. We based it on the CANDELS morphological classification in  Kartaltepe et al. (2015). In this work, the authors classify the objects in five main categories: 'Disk', 'Spheroid', 'Irregular', 'Compact', and 'Unclassifiable'. They add mixed classes, but we decided to maintain only the main categories. The Disk category includes objects with a disk structure.
Stellar masses The stellar masses are another product that we took from CAN-DELS in the GOODS-S field  (Santini et al. 2015). The authors use the same data to compute masses through ten different codes and then they combine the results. We compared the CANDELS stellar masses with those released by 3D-HST  (Skelton et al. 2014 ). We measured an offset median = -0. 057 with a rms = 0. 26 once we eliminated the outliers CANDELS 12039 = 3D-HST 23397 and CANDELS 18444 = 3D-HST 3408. The offset is defined as log (M CANDELS median /M 3D-HST ). Our offset perfectly matches the median = -0. 058 ± 0. 003 measured in  Santini et al. (2015). The authors of the CANDELS stellar mass catalogue argue that, because the comparison is satisfying at a first order, the differences between the two catalogues are due to different sys- tematics in the SED fitting.
It is a completely negligible value compared with the intrinsic extinction. On the other hand, it is known that dust absorbs an amount of the star emission. This dust emits it again in the IR. Therefore, 7 http://guaix. fis. ucm. es/projects/milia a good approach for calculating the intrinsic extinction of the studied galaxy is to study the ratio between dust and FUV fluxes (F dust /F FUV ), the IRX ratio. This value is related to the FUV extinction A(FUV)  (Buat et al. 2005) : A(FUV) = -0. 0333y 3 + 0. 3522y 2 + 1. 1960y + 0. 4967, (6) where y = log F dust F FUV. We obtained F dust from the TIR luminosity synthetically calculated and F FUV from synthetic FUV magnitude. Both of these values were taken from the Rainbow Database, as is indicated in Sect. 2. 4. Then, we obtained the Hα extinction (A(Hα)) with the A(FUV) value applying a Calzetti extinction law  (Calzetti et al.
2000) : A(Hα) = A(FUV) k Hα k FUV , (7) where k Hα = 2. 659 -1. 857 + 1. 040 λ Hα + R V , k FUV = 2. 659 -2. 156 + 1. 509 λ FUV -0. 198 λ 2 FUV + 0. 011 λ 3 FUV + R V and R V = 4. 05. For CANDELS 15282 we got a value A(Hα) < 0, thus, we assumed A(Hα) = 0. In the case of CANDELS 12039 the synthetic TIR luminosity was not available and we calculated the extinction employing FUV and NUV synthetic magnitudes from the Rainbow Database. The FUV -NUV colour is related to the IRX ratio  (Muñoz-Mateos et al. 2009 ): L TIR L FUV = 10 0. 30+1. 15(FUV-NUV) -1. 64. (8) Once we have the IRX ratio from Eq. 8, we can substitute it in Eq. 6 and apply a Calzetti law (Eq. 7) to obtain the extinction for this object. In Fig. 6  we show the distribution of the sample in terms of stellar masses, rest-frame B-band absolute magnitude and B -V colour, which we took from the CANDELS stellar masses catalogue  (Santini et al.
Spectroscopy As was described in Sect. 2. 5, we collected the 12 spectra shown in Fig. 1. In this section, we present the outcome obtained through the spectra analysis. Making use of the software IRAF and the task splot, we measured the emission lines that we identified in each spectrum. We performed ten measurements of each line, taking the average value of them with the standard deviation as the uncertainty. The spectroscopic properties of ELGs are generally characterised using line-ratio diagnostic diagrams  (Baldwin et al. 1981; Veilleux & Osterbrock 1987). We focus the analysis on two diagrams: excitation versus luminosity and restframe EW([O ii]) versus luminosity. These diagrams are common in studies of this kind  (Gallego et al. 1997; Guzmán et al. 1997). In Fig. 7   Along the sequence, the metallicity grows with luminosity (i.