dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge  Prof. Edna Freymuller  and the technicians of the  Electron Microscope Service of UNIFESP  for helping with EM images. Dr. A. Leyva  helped with English editing of the manuscript. Dr. Sarah Gordon  for the proof reading of the revised version. We also acknowledge the help of Ms. Adriely Goes Conceição with plating of colonies and  Ms. Jana Westhues  for A549 cell culture. Funding GMR, CKM and CSCW received grants from  São Paulo Research Foundation (FAPESP)  ( 2012/04913-8, 2013/16018-6  and  2009/14665-9 ).
Colonies were bright grey. Bacterial cells were Gram-positive, non-motile and nonspore-forming with a mean diameter of 0. 55 μm. Strain Marseille-P2746 T is a strictly anaerobic coccus and exhibits oxidase activity but no catalase activity. Using the universal primer pair FD1 and rp2 as previously described  [2] , and a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France), the 16S rRNA gene was sequenced. Strain Marseille-P2746 T exhibited an 89. 28% 16S rRNA gene sequence identity with Murdochiella asaccharolytica strain WAL 1855C T (GenBank Accession number EU483153), the phylogenetically closest species with a validly published name (Fig. 1 ). This value was lower than the 95% 16S rRNA gene sequence threshold proposed by Stackebrandt and Ebers  [3]  to define a new genus without carrying out DNA-DNA hybridization and classifies it as a new genus within the Peptoniphilaceae family (phylum Firmicutes), first created in 2014  [4].
Fission product yields (FY) are fundamental nuclear data for several applications, including decay heat, shielding, dosimetry, burn-up calculations. To be safe and sustainable, modern and future nuclear systems require accurate knowledge on reactor parameters, with reduced margins of uncertainty. Present nuclear data libraries for FY do not provide consistent and complete uncertainty information which are limited, in many cases, to only variances. In the present work we propose a methodology to evaluate covariance matrices for thermal and fast neutron induced fission yields. The semi-empirical models adopted to evaluate the JEFF-3. 1. 1 FY library have been used in the Generalized Least Square Method available in CONRAD (COde for Nuclear Reaction Analysis and Data assimilation) to generate covariance matrices for several fissioning systems such as the thermal fission of U235, Pu239 and Pu241 and the fast fission of U238, Pu239 and Pu240.
Covariance generation The covariance generation methodology effectiveness was already demonstrated in  [6, 7]  and it is summarized in Fig. 1. FY covariance matrices have been evaluated using a e-mail: nicholas. terranova@unibo. it the CONRAD code  [8]. The same independent FY semiempirical models used in JEFF-3. 1. 1 evaluations to fill the gaps existing in the available experimental measurements were employed and implemented in CONRAD. A Bayesian Generalized Least Square Method (GLSM) has been adopted to find the model parameter best estimates which can faithfully represent the JEFF library. JEFF-3. 1. 1 FY values were taken as reference and included in a GLSM procedure as pseudo-experimental data. Actual experimental measurements on miscellaneous fission quantities, such as the total prompt neutron emission probability P(ν) and the total prompt neutron multiplicity < ν >, were embedded in the calculation.
In-situ synchrotron X-ray diffraction experiments were conducted at the ID11 beamline of the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. A beam energy of about 80 keV (k = 0. 15582 A ˚) and a beam size of 300 9300 lm 2 were used. To obtain diffraction data, a 2D CCD detector (FReLoN-fast readout low noise) camera system  [20]  was employed. CeO 2 calibrant was used on each sample to determine sample-detector distance, tilt of the detector, and the beam center. Corrections were applied to omit dark field, flat field, and detector distortion during data analysis. [21] An Instron/NPL electro-thermal mechanical testing (ETMT) machine was used to apply loads up to 3 kN. Thermal cycles were generated by Ohmic heating with a direct current passing through the sample. Temperature control was established by a type-R thermocouple discharge welded to the center of the specimen.
ACKNOWLEDGMENTS The authors would like to acknowledge  ESRF  for beamtime and thank the ID11 beamline team for their assistance, in particular  Dr. Th. Buslaps. Mr A. Navarro-Lopez , and  Dr. J. Hidalgo-Garcia  for their assistance during the experiments and  Professor I. M. Richardson  for reviewing the manuscript. This research was carried out under the project number  F22. 8. 13507  in the framework of the  Partnership Program  of the  Materials innovation institute M2i  (www. m2i. nl) and the  Foundation of Fundamental Research on Matter (FOM)  (www. fom. nl), which is part of the  Netherlands Organisation for Scientific Research  (www. nwo. nl).
Faint CMEs of all widths may be attributed either to a scarcity of material associated with the event or to a significant propagation component away from the observer's plane of the sky (POS) (e. g. ,  Cremades et al. 2015). Coronal jets were first observed in detail by the Soft X-ray Telescope (SXT) on board the Japanese mission Yohkoh. Yohkoh/SXT data revealed the characteristics of large and energetic jets  (Shibata et al. 1992a; Shimojo et al. 1996 Shimojo et al. , 1998;; Shimojo & Shibata 2000). Observations in X-rays and EUV show that they are collimated, beam-like structures, typically anchored in coronal bright points, and best seen inside polar coronal holes against the dark background. Polar coronal jets were studied by  Wang et al. (1998)  using data from LASCO and the Extreme Ultraviolet Imaging Telescope (EIT) on board SOHO.
Observational data sets The observational data used in this study come from several sources. To understand the magnetic complexity of the an Hα filter with a passband of 0. 5 Å. The image size is enlarged by a factor of two using a Barlow lens. The pixel resolution of the images is 1. To understand the thermal and non-thermal nature of the studied flare, we reconstructed X-ray images from the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI;  Lin et al. 2002)  using the CLEAN technique. The evolution of the flare and other associated dynamic phenomena in EUV are studied using data from the Transition Region and Coronal Explorer data (TRACE;  Handy et al. 1999). The pixel resolution of the images is 0. 5 and the temporal cadence can be as low as tens of seconds. The TRACE observed in six EUV/UV bands from Fe IX/X (171 Å) to C I/Fe II/UV continuum (1600 Å) to white-light emission.
for at least 20 EIT pixels in those images. There are many more events originating from the east of the AR if one looks at EIT difference images. The first column in the table indicates the time at which we first detect the jet. These hours have an estimated error of 12 min, EIT 195 Å temporal cadence. Several of the broad jets, such as those studied by  Yan et al. (2012)  and those in this article, were also observed by TRACE with much higher temporal cadence but, since TRACE observed AR 10484 sporadically, we prefer to refer to EIT data for coherence in the table. The second column in Table  1  shows the length of the jets measured in projection on the solar disk from EIT direct images. The lengths of the jets, reported at their maximum extensions, are measured from the brightenings observed at their bases to the brightest feature in their easternmost borders.
The closed blue lines were computed starting integration very close to the corresponding BP on its western side, while the open blue lines are computed from the BP eastern side. It is via magnetic reconnection that the blue field lines can evolve into the red field lines with very close footpoints, but located on the other side of the photospheric trace of each BP separatrix; the blue field lines are line tied at the photosphere by the gravity force acting on the dense plasma at their bases. Several numerical models (see references in Sect. 5. 2. 1) show that magnetic reconnection can occur in configurations similar to the configuration that we describe; see, in particular, the  Cheung et al. (2010)  sketch in Fig. 8  that summarizes the evolution found in their simulation. Our data analysis, combined with modeling and magnetic field topology computation, led us to propose two possible scenarios to explain this particular event.
Acknowledgements. C. H. M. , H. C. , G. D. C. , and F. N. acknowledge financial support from grants  PICT 2012-0973  ( ANPCyT)  and  PIP 2012-01-403  ( CON-ICET ). C. H. M. and G. D. C. recognize grant  UBACyT 20020130100321. H. C. appreciates support from project  UTI4035TC  (UTN). A. K. S. acknowledges the support from  RESPOND-ISRO  ( DOS/PAOGIA2015-16/130/602 ) project. This work was initiated during a one-month stay of R. C. and C. H. M. at  Paris Observatory, Meudon , for which they acknowledge financial support. We thank  Dr. Pascal Démoulin  for his very helpful comments. C. H. M. , H. C. , and G. D. C. are members of the  Carrera del Investigador Cientí fico (CONICET). F. N. is a fellow of  CONICET.
Water temperature was monitored every 15 min during the entire experiment using a RBR TR-1060 temperature recorder. Incident photosynthetically active radiation (PAR, 400-700 nm) was monitored continuously using a LI-1000 data logger combined with a LI-190SA cosine sensor (LICOR) located near the incubator. Incident UVA (SED033 detector/UVA filter/W diffuser) and UVB (SED240 detector/UVB filter/W diffuser) radiation was measured daily (day 5 to 9 only due to instrument dysfunction during the first 4 days) at around midday with an IL1700 radiometer (International Light).
Values of pCO 2 were calculated using CO 2 SYS software; values of proton concentrations were calculated after the pH T measurements. 1L and 1H are the control microcosms. bator was then covered with a white canvas sheet until sunset and bags remained overnight in the incubator without further manipulation. After the initial acidification, the carbonate chemistry was not manipulated and the only changes in pH that occurred during the experiment were due to biological activity. Subsampling began the next day in the morning (T1, 7 August, around 07:00 LT).
Light treatments Once acidified, each set of incubation bags covering the initial pH values between 8. 1 and 7. 2 was assigned to a light treatment (Tables  1  and 2 ): "low light" (LL, 6 bags), and "high light" (HL, 6 bags). The transmittance through the incubation bags of both LL and HL treatments was determined in the laboratory using a Perkin Elmer LAMBDA 850 Spectrophotometer. We based our experimental LL conditions (PAR, UVA, and UVB transmittance of 32. 6, 20. 6, and 0 %, respectively) on a model of the minimal light conditions required to trigger an under-ice bloom  (Palmer et al. , 2014)  and available data from measurements of UV and PAR under ponded ice  (Trodahl and Buckley, 1990; Perovich et al. , 1998; Belzile et al. , 2000; Frey et al. , 2011; Palmer et al. , 2014). As UVB is strongly attenuated by sea ice and thus likely negligible at 5 m depth under the ice  (Frey et al.
Duplicates were analysed for each incubation bag, and pH T was subsequently calculated according to  Dickson et al. (2007,  see their section SOP 6b). Data were then converted to mean incubation temperature using CO 2 SYS and the TA measurements  (Pierrot et al. , 2006). The pCO 2 values corresponding to each pH T measurement were also calculated using CO 2 SYS software. Concentrations in protons for each microcosm were calculated using the pH T measurements. Water for TA and DIC analyses was subsampled early in the morning on days T1, T4, and T9. Water was gently collected from the incubation bags in pre-rinsed 250 mL glass bottles. Samples were analysed on board within 4 h by coulometric and potentiometric titration for DIC and TA, respectively (more details are given in, for example,  Johnson et al.
Ultra-high-purity N 2 was used to extract the volatile DMS from solution at a flow rate of 100 mL min -1. The sparged DMS was adsorbed onto Carbopack-X packed in a stainless steel trap. After trapping was completed, a series of high current pulses heated the trap to 250 • C to desorb the DMS onto the capillary column prior to elution and detection by a pulsed flame photometric detector (OI Analytical, Model 5380). Light emitted during combustion in the PFPD was converted to a voltage (using a PMT -photomultiplier tube) and recorded by the LabView software. Raw data outputs (peak voltages) were processed using MATLAB by calculating the area under each curve and by comparing them to standard curves to give final DMS concentrations. Known DMS concentrations of 0, 3, 6, 9, 12, and 15 nmol L -1 were processed for calibration purposes before each sampling day in order to obtain the standard curves.
Statistical analysis All statistical analyses were run using RStudio (http://www. rstudio. com/). Normality of the data was determined using a Shapiro-Wilk test at the 0. 05 significance level and data were transformed (log or square root) when the normality was rejected (p < 0. 05). To test for differences between pH and light treatments for the variables measured during the experiment (time series of nutrient and Chl a concentrations, phytoplankton, bacteria abundances, and dimethylated compound concentrations), we used a generalized least-square model that corrected for data autocorrelation due to time repetition (gls command in R studio, package nlme; also see the method described by  Paul et al. , 2016 ). In the model, time and pH were taken as two continuous factors; the light was included as a categorical factor (two levels: low light, high light).
Pico-phytoplankton cell abundance was low at the start of the experiment (< 5 × 10 3 cells mL -1 ) and remained at this low level until T3 when it began to increase to reach maximum concentrations (200-300 ×10 3 cells mL -1 ) between T7 and T8, depending on the pH T (Fig. 4e, f ). Exceptions to this trend are the two micro-   4. Regressions were based on the full dataset when no significant difference between the light treatments was detected (see also Table  3  for more details). cosms at the lowest pH T under both light regimes, in which pico-phytoplankton abundance continued to increase linearly until the end of the experiment. Of the two stressors tested (light and pH), only pH T had a weak but significant effect on pico-phytoplankton abundances (Table  3 ). As shown in Fig. 5c , mean pico-phytoplankton abundance increased with increasing proton concentration for both light treatments, although this relation does not appear to be linear (data pooled in Table  4 ).
Acknowledgements. The authors wish to thank commander  Alain Gariépy , the officers, and crew of the Canadian ice-breaker  NGCC Amundsen  for their support during the project. We also want to thank  Glenn Cooper  and  Kyle Simpson  of the  Institute of Ocean Sciences in Sidney, BC, Canada , for providing advice and the material and equipment needed for pH measurements,  Isabelle Courchesne  and  Gabrièle Deslongchamps  for the nutrients analysis,  Marjolaine Blais  for the flow cytometry analyses, and  Jean-Bruno Nadalini  for the taxonomic analyses. This study was funded by the  NSERC   Discovery Grant Program and Northern Research Supplement Program  ( M. Levasseur ,  M. Gosselin ), as well as by the  NETCARE network  (funded under the  NSERC Climate Change  and  Atmospheric Research program ),  ArcticNet  (The  Network of Centres of Excellence of Canada ), and  Fisheries and Oceans Canada.
Pancreas Genotyping, quality control and GWAs statistics Genotyping was performed on Illumina BeadChip arrays (Illumina, San Diego, California, USA). The ACP samples of the screening cohort and NACP samples of replication cohort 1 were genotyped at the Helmholtz Center Munich (Dr P. Lichtner). The study design is summarised in online supplementary figure  S1. All analyses were performed using PLINK V. 1. 9  17 and R (www. R-project. org). Data were filtered to achieve an individual-wise and single-nucleotide polymorphism (SNP)-wise call rate >0. 99. 18 enotyped sex was determined in each sample from gonosomal data. Related individuals were excluded (pi-hat >0. 185). Heterozygosity outliers defined by heterozygosity more extreme than median ±3 IQRs and ethnic outliers according to Price et al  18  with 6 SD criterion in principal component analysis were not used for further analysis.
Hence, the 1959 ACP samples that passed quality control as well as controls (n=6040) derived from different consortia (KORA S3/F3 Illumina Omni data set from Augsburg, Germany; GESGA consortia data set from Mannheim, Germany; INCIPE Illumina Omni data set from Verona, Italy) were included for further analyses. Imputation was performed with SHAPEIT V. 2 and IMPUTE V. 2. 3. 0 applying the 1000Genomes reference phase 1, V. 3  19  and logistic regression was applied with the first three principal components of the SNP data included as covariates to account for possible population stratification resulting in a standardised overall inflation factor of 1. 02. 20.
replication genotyping Details of polymerase chain reactions (PCR) and melting curve assays used for analysis of the CTRB1-CTRB2 locus can be found in the online supplementary file 1. In the first replication cohort of the German NACP patients and controls, chip data were used with similar preprocessing as described (n=584 NACP patients; n=4892 population-based controls from LIFE, the latter were genotyped using Affymetrix AXIOM-CEU (Affymetrix, St. Clara, California, USA) genome-wide SNP array  22  ). Thereby, imputation on 1000Genomes reference phase 1, V. 3 was performed for a 5 Mb region on chromosome 16 between 71 310 697 bp and 76 045 524 bp using 285 SNPs fulfilling quality control in all individuals. Further replication in two independent NACP cohorts from Germany (Greifswald cohort, 520 patients, 760 controls) and France (Brest cohort, 546 patients, 1043 controls) was carried out by genotyping SNPs with the melting curve assay (see online supplementary table  S2 ).
Pancreas Identification of a complex genetic rearrangement in the ctrb1-ctrb2 locus associated with cP The CTRB1-CTRB2 locus harbours complex genomic rearrangement variants which include a 16. 6 kb inversion (see figure  2 ) and a 584 bp deletion in CTRB2 (not shown). 21 First, we excluded the deletion in CTRB2 as an explanation for the association as genotyping of 289 ACP patients revealed only negligible LD (R²=0. 12) with our lead SNP rs8055167. Subsequently, in order to examine whether our lead SNP is in LD with the 16. 6 kb inversion in the CTRB1-CTRB2 locus, we genotyped 227 ACP patients for the inversion and found stronger LD (R 2 =0. 50). By combining our genotype data on the inversion with genetic data from SNP chips, we successfully imputed the inversion in all 1959 ACP and 6040 control samples (info-score of the inversion =0.
Pancreas assess the link between the inversion and expression of CTRB1-CTRB2, we quantified the relative mRNA expression ratio of the two chymotrypsins using cDNA samples obtained from human pancreatic tissue or acinar cells with different inversion genotypes. We found that the major risk allele was associated with higher relative CTRB1 expression while heterozygous carriers with one minor allele expressed higher levels of CTRB2 (figure  4A ). In accordance with our findings, data from the GTExPortal ( gtexportal. org) indicated that the major allele of rs8048956 tagging the inversion was associated with increased CTRB1 and decreased CTRB2 mRNA expression relative to the minor allele. The association of the lead SNP rs8055167 with CTRB1-CTRB2 expression was similar but smaller, supporting a causal role of the inversion in CP (see online supplementary figure  S8 ).
Acknowledgements The authors thank all study participants for providing clinical data and blood samples. They also thank  Vera Sahin-Tóth  (Boston),  Knut Krohn ,  Kathleen Schön  and  Birgit Oelzner  (IZKF core unit DNA technologies, Leipzig) for excellent technical assistance. They are grateful to  Jeff MacDonald  and  Stephen Scherer  for providing details for the data of Pang et al. They thank  Stephan Buch  and  Jochen Hampe  ( Medical Department 1, University Hospital Dresden, TU Dresden, Dresden, Germany ) for initial data collection and fruitful discussions.
Funding This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  grants  RO 3929/1-1 ,  RO 3929/2-1  and  RO3929/5-1  (to JR),  Wi 2036/2-2  and  Wi 2036/2-3  (to HW) and SFB 1052 (to MB, MS, AT, PK), SFB  1052 C01 ,  B01 ,  B03 ;  SPP 1629 TO 718/2-1  (to AT) by a grant of the  Colora Stiftung gGmbH  (to JR), the  Else Kröner-Fresenius-Foundation (EKFS)  (to HW),  NIH  grants  R01DK058088 ,  R01DK082412 ; and  R01DK095753  (to MS-T), a grant from the  National Pancreas Foundation  (to EH), the  Institut National de la Santé et de la Recherche Médicale  (INSERM; to CF), the  Programme Hospitalier de Recherche Clinique  ( PHRC R 08-04 ; to CF), the  French Association des Pancréatites Chroniques Héréditaires  (to CF), the  Council of Scientific and Industrial Research (CSIR)  (to CF), by grants of the  European Regional Development Fund (ERDF)   V-630-F-150-2012/133  and  V630-S-150-2012/132  (to FUW), by grants  310030_138747  and  310030_169196  from the  Swiss National Funds  (to FS) and by grants  RTICC  from  Instituto de Salud Carlos III  ( RD12/0036/0034 ) and SAF 2015-70857 from  Ministerio de Economía y Competitividad (Madrid, Spain)  (co-funded by the  ERDF-EU)  (to FXR) and  Fondo de Investigaciones Sanitarias (FIS) ,  Instituto de Salud Carlos III, Spain  (Grant # PI1501573 ) (to NM).
SPP was supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre. LIFE is funded by means of the  European Union , by the  European Regional Development Fund (ERDF)  and by funds of the  Free State of Saxony  within the framework of the  excellence initiative  (project numbers  713-241202 ,  14505/2470 ,  14575/2470 ). The  KORA study was  initiated.
| Genotyping of the sampled trees with microsatellite markers To genotype sampled trees, total DNA was extracted from 100 mg of fresh leaf tissue by GeneElute Plant Genomic DNA Miniprep Kit (Sigma-Aldrich), following manufacturer's instructions, and then quantified by a Nanodrop spectrophotometer. Genotype identification was performed by analyzing 15 informative nuclear SSR markers  (Baldoni et al. , 2009; El Bakkali et al. , 2013). PCR products were separated using an automatic capillary sequencer (ABI 3130 Genetic Analyzer, Applied Biosystems), and electropherograms were then investigated for allele composition across marker loci using GenMapper 3. 7 software (Applied Biosystems). To verify the genetic representation of the selected sample set, SSR data obtained on the 118 trees (Table  S1 ) were compared to a collection of 342 genotypes: the 309 olive genotypes present in the OWGB collection  (El Bakkali et al.
DNA database reference corresponds to voucher specimen accessible in referenced collections (see Table  S1 ). We performed stigma tests by depositing pollen from one test sample on stigmas of a pair of cross-compatible testers (i. e. , belonging to two different SI groups). Under the hypothesis that O. europaea exhibits DSI, we expected pollen from every sample to be compatible with one of the two tester lines (thereby confirming pollen viability) and incompatible with the other; indeed, cases in which pollen would be compatible with both tester lines would indicate either the presence of a third SI group (different from these represented by the tester recipients) or a nonfunctional SI genotype. Cases in which tested pollen was negative with both reference recipients were likely caused by either low pollen viability or low stigma receptivity.