dataesr/scientific-paragraphs-categorization huggingface dataset:
Contributors JG, ADM, JVL, EBC, and SNi were responsible for study conception and design. A study concept sheet was circulated to all authors to comment on the study design. All authors contributed to data acquisition. ADM, EBC, and SNi facilitated document access. Document searches were also conducted independently by ADM, EBC, and SNi to further corroborate search findings. ADM and EBC categorised the outcome criteria. ADM, JG, GJD, EBC, and JVL contributed to the drafting of the report.
Acknowledgments We thank the following people for their assistance with retrieval and interpretation of documentation:  Havard Midgard  ( Department of Infectious Diseases, Akershus University Hospital, Norway; Institute for Clinical Medicine, University of Oslo, Norway; Department of Gastroenterology, Oslo University Hospital, Norway ),  Ecaterina Filep ,  Gerard Estivill Mercade ,  Marcel Schulz ,  Rainer Puhr  (The  Kirby Institute, UNSW, Sydney, NSW, Australia ),  Petros Katsioloudes  ( Ministry of Health, Cyprus ), and  Ioannis Demetriades  ( Grigorios Clinic, Larnaca General Hospital, Cyprus ). We also thank  The All Wales Therapeutics and Toxicology Centre (Penarth, UK) , and the  National Institute for Health Research Health Protection Research Unit in Evaluation of Interventions (University of Bristol, UK ).
On POD 63, the patient got fever resistant to antibiotics. Her chest CT-scan showed right-sided pleural effusion with passive atelectasis and alveolar opacity. Broncho-alveolar lavage (BAL) was performed and its direct examination displayed Aspergillus-like branched hyphae. At the same time, galactomannan (GM) antigen index (Platelia Aspergillus, BioRad) and (1-3)-β-D-glucan (BDG) (Cape Code) in serum, which were previously negative, became positive (GM antigen index > 6 (threshold index: 0. 5), BDG = 234 pg/mL (threshold value: 80 pg/mL)). Aspergillus real-time PCR (qPCR), based on a target of 67-bp DNA fragment specific to the multicopy gene encoding the 28S rRNA of A. fumigatus, was positive in serum (Cq value = 35)  [24]. Eventually, based upon data from the European Organization for Research and Treatment of Cancer/Mycoses Study Group (EORTC/MSG)  [25] , the patient was classified as having a probable IFI.
) after a step of bead beading in MagNA Lyser Instrument (Roche). The rDNA of partial β-tubulin and calmodulin genes were amplified as described by Samson et al. [16]. Sequencing reactions were carried out for both strands. When compared with the partial calmodulin gene sequences available in the NCBI database, the highest identity was obtained with A. pseudodeflectus NRRL 278 strain (Genbank accession number EF652368. 1) and NRRL 6135 strain (Genbank accession number EF652419. 1) [16]  with nucleotide identity rates of 100 and 99%, respectively over a sequence length of 573 bp. For the partial β-tubulin sequences, identity rates of 99% were obtained with both strains, NRRL 278 (Genbank accession number EF652280. 1) and CBS 596. 65 (Genbank accession number EF591732. 1). Phylogenetic trees of these two sequences, alone and combined, were built with the MEGA 6.
26 = 6. 56, P = 0. 011) (Fig. 4c ). Abundance and density of populations. Population abundance was estimated using a N-mixture model with Poisson error, as it showed lower AIC than zero-inflated models (Poisson model: AIC = 555. 74, zero-inflated Poisson: AIC = 557. 74). N-mixture models indicated a high detection probability of individuals (mean ± SE: 0. 504 ± 0. 029). The estimated number of salamanders was highly variable among sites, ranging from five to 103   26, 60. The map was built using the package biomod2  61. Orange circles indicate all sites used to estimate suitability for the species; red squares represent sites surveyed in the present study. individuals per site (Table  1 ); the average of density estimates across all sites was 0. 06 ± 0. 03 individuals/m 2. Salamanders density strongly differed among sites, being significantly higher in areas showing the highest environmental suitability (r = 0.
Suitability modelling. We used correlative species distribution models to assess relationships between salamander distribution and major bioclimatic variables, and to obtain measures of broad-scale ES. We considered four bioclimatic variables: annual mean temperature, temperature seasonality, annual precipitation and precipitation seasonality (period: 1979-2013); variables were extracted from the Chelsa-climate dataset at the 30 arc-seconds resolution (approx. 920 × 700 m within the study area), which provides improved climatic estimates in landscapes with complex topography  51. These variables represent average conditions and their variability across the year, and are major determinants of vertebrate distribution  52, 53. Furthermore, these variables are enough to explain most of the climatic variation  54  , and other important variables (e.
Models were calibrated using a 67% random sample of the presence data and evaluated against the remaining 33% data using the True Skill Statistic (TSS)  56. To improve reliability of models, we restricted the geographical background to a small region potentially accessible to the species  [57] [58] [59]. Background data were limited to within 17 km from the known presence records (Fig. 2 ), which is the maximum distance between H. flavus populations belonging to the same genetic cluster  60. This analysis was repeated five times, thus providing a fivefold cross-validation; models were run using the package biomod2 (version 3. 3-7) 61 in R 3. 3. 3  62. Given that alternative SDM can provide variable outputs, ensemble forecasting of the different SDM  63  was then used to obtain an overall suitability map.
2 ). Surveys were performed from April to October, the period in which Hydromantes salamanders are more active and show the highest abundance and detectability in underground environments  27, 66. During the first survey, we divided each cave in 3-m longitudinal sectors, and measured the maximum height of the ceiling and the maximum width of each sector  49. These measures were then used to estimate the explored cave surface. Subsequently, we measured multiple features of the populations: distribution of salamanders within these caves, stomach content, body condition and density. Some of these population features required sampling during specific months and thus, due to sampling constraints (i. e. , restricted sampling periods, need of speleological equipment), it was impossible to measure all the population features for all the caves, nevertheless for all the features we gathered data covering the whole range of the species (Table  1 ).
To assess if the frequency of empty stomachs differs among caves (i. e. , to test the foraging hypothesis), we run the binomial Generalized Linear Models (GLM) using the stomach condition (empty/full) as dependent variable, while season (autumn/spring) and cave identity were used as independent variables. We also used the Bayesian equal-tailed Jeffreys intervals (package MKmisc;  68  ) to estimate 95% CI of the frequency of empty stomachs across populations, as Jeffreys intervals are a robust approach for the estimation of binomial CI  69. The dataset is published in  50.
Body weight can show seasonal variation. To test the body condition hypothesis, we ran a linear mixed model (package lme4;  73  ) considering BCI of salamanders as dependent variable, ES, month of survey and group (m/f/j) as independent variables, and population identity as random factor. Sample size was not homogeneous among populations, thus residual degrees of freedom were approximated following Satterthwaite  74. Given that we detected significant BCI differences between the tree groups, we used orthogonal contrast to test whether there are differences between juvenile and adults and, within adults, between males and females. Dataset is shown in Supplementary Table  S2. Population abundance and density. In seven caves we performed repeated visual encounter surveys in a short period, to estimate salamander abundance.
We used Empirical Bayes methods to estimate the posterior distribution of the abundance (mean and 95% Bayesian credible intervals, CI)  79. Surveyed surface was strongly variable among caves (33-1229. 61 m 2 ), therefore we calculated salamander density to allow comparison among sites. We calculated population density on the basis of abundance estimates and the surveyed surface of caves, and then assessed the correlation between population density (square-root transformed) and ES. N-mixture models were run using the unmarked package in R  80. Dataset is shown in Supplementary Table  S3. Spatial autocorrelation can influence the outcome of statistical tests, therefore we used Moran's I to assess the occurrence of residual autocorrelation in all regression and correlation analyses  81. For all regression models, residual autocorrelation was weak and not significant (in all tests, Moran's I ≤ 0.
In summary, this GWAS provides insight into the etiologies of CRC and provides a basis for future fine-mapping, functional characterization, and risk modeling research. (36, 37) , and the Lung Cancer and Smoking study  (38). The prostate and PanScan study data sets were accessed with appropriate approval through the dbGaP online resource (http://cgems. cancer. gov/data/) accession numbers phs000207. v1. p1 and phs000206. v3. p2, respectively, and the lung data sets were accessed from the dbGaP website (http://www. ncbi. nlm. nih. gov/gap) through accession number phs000093. v2. p2. Funding for the Lung Cancer and Smoking study was provided by the NIH and the Genes, Environment and Health Initiative (GEI; Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438). For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination  (23) , and the Johns Hopkins University Center for Inherited The funders had no role in the design of the study; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication.
Colorectal Transdisciplinary Study (CORECT): The CORECT Study was supported by the National Cancer Institute/ National Institutes of Health (NCI/NIH), US Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350, P01 CA196569, R01 CA201407), and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678). The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study was supported by US Public Health Service contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Cancer Institute. The Cancer Prevention Study-II Nutrition Cohort is funded by the American Cancer Society. ColoCare: This work was supported by the National Institutes of Health (grant numbers R01 CA189184, U01 CA206110, 2P30CA015704-40 [Gilliland]), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and the EU TRANSCAN initiative.
AZ, CO, MN, NC, NH; and by the Victoria Cancer Registry and Ontario Cancer Registry. ESTHER/VERDI was supported by grants from the Baden-Wu ¨rttemberg Ministry of Science, Research and Arts and the German Cancer Aid. GALicia Estudio Oncol ogico de coloN (GALEON): FIS Intrasalud (PI13/01136). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. Memorial Sloan Kettering Cancer Center (MSKCC): The work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation.
Nutrition Questionnaires plus study (NQplus) : The NQplus study is sponsored by a  ZonMW investment grant  ( 98-10030 ); by  PREVIEW , the project  PREVention of diabetes through lifestyle intervention and population studies in Europe and around the World (PREVIEW)  project, which received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under grant No. 312057 ; by funds from  TI Food and Nutrition  (cardiovascular health theme), a public-private partnership on precompetitive research in food and nutrition, and by  FOODBALL , the  Food Biomarker Alliance , a project from  JPI Healthy Diet for a Healthy Life. Asia Colorectal Cancer Consortium (ACCC) : The work at  Vanderbilt University School of Medicine  was supported partly by  US National Institutes of Health  (grant numbers  R37 CA070867 ,  R01 CA182910 ,  R01 CA148667 ) and  Anne Potter Wilson  funds from  Vanderbilt University School of Medicine.
The  Fukuoka Colorectal Cancer Study  was funded by the  Ministry of Education, Culture, Sports, Science and Technology, Japan. Hispanic Colorectal Cancer GWAS: This work was supported by the  National Institutes of Health  (grant numbers  R01 CA155101 ,  U01 HG004726 ,  R01 CA140561 ,  T32 ES013678 ,  U19 CA148107 ,  P30 CA014089 ). The  US-Japan Colorectal Cancer GWAS  and the  African American Colorectal Cancer GWAS  were funded through the  US National Institutes of Health  (grant numbers  1R01-CA126895 ,  1R01-CA126895-S1 ,  1R01-CA104132 ,  2U24-CA074806 ). The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the National Institutes of Health. Additional funds were provided by the  NCI, NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS.
Donors were enrolled at  Biospecimen Source Sites  funded by  NCI/SAIC-Frederick, Inc. (SAIC-F)  subcontracts to the  National Disease Research Interchange  ( 10XS170 ),  Roswell Park Cancer Institute  ( 10XS171 ), and  Science Care, Inc. ( X10S172 ). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract ( HHSN268201000029C ) to The  Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to  Van Andel Institute  ( 10ST1035 ). Additional data repository and project management were provided by  SAIC-F (HHSN261200800001E ). The  Brain Bank  was supported by a supplement to  University of Miami  (grants  DA006227  and  DA033684 ) and to contract  N01MH000028. Statistical Methods development grants were made to the  University of Geneva  ( MH090941  and  MH101814 ), the  University of Chicago  ( MH090951 ,  MH090937 ,  MH101820 ,  MH101825 ), the  University of North Carolina -Chapel Hill  ( MH090936  and  MH101819 ),  Harvard University  ( MH090948 ),  Stanford University  ( MH101782 ),  Washington University St Louis  ( MH101810 ), and the  University of Pennsylvania  ( MH101822 ).
Funding Colorectal Transdisciplinary Study (CORECT): The CORECT Study was supported by the National Cancer Institute/ National Institutes of Health (NCI/NIH), US Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350, P01 CA196569, R01 CA201407), and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678). The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study was supported by US Public Health Service contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Cancer Institute. The Cancer Prevention Study-II Nutrition Cohort is funded by the American Cancer Society. ColoCare: This work was supported by the National Institutes of Health (grant numbers R01 CA189184, U01 CA206110, 2P30CA015704-40 [Gilliland]), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and the EU TRANSCAN initiative.
Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the Total ARTICLE Cancer Care Initiative, Collaborative Data Services Core, and Tissue Core at the H. Lee Moffitt Cancer Center and Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center (grant number P30 CA076292). Studies of Epidemiology and Risk Factors in Cancer Heredity: Cancer Research UK (C490/A16561). The ). The Colon Spanish study was supported by Instituto de Salud Carlos Cancer Family Registry (CFR) Illumina GWAS was supported III, co-funded by FEDER funds: a way to build Europe (grants by funding from the National Cancer Institute, National PI14-613 and PI09-1286), Catalan Government DURSI (grant Institutes of Health (grant numbers U01 CA122839, R01 2014SGR647), and Junta de Castilla y Le on (grant LE22A10-2).
CA143247). The Colon CFR/CORECT Affymetrix Axiom The Swedish Low-risk Colorectal Cancer Study: The study GWAS and OncoArray GWAS were supported by funding was supported by grants from the Swedish Research from National Cancer Institute, National Institutes of Health Council (K2015-55X-22674-01-4, K2008-55X-20157-03-3, (grant number U19 CA148107 to S. Gruber). The Colon CFR K2006-72X-20157-01-2) and the Stockholm County Council participant recruitment and collection of data and biospeci- (ALF project). Research and contributions in Taiwan and mens used in this study were supported by the National Taipei Medical University were funded by Taiwan Ministry Cancer Institute, National Institutes of Health (grant num- of Health and Wealth (MOHW105). Center for Inherited ber UM1 CA167551), and through cooperative agreements Disease Research (CIDR) genotyping for the Oncoarray with the following Colon CFR centers: Australasian was conducted under contract 268201200008I (to Colorectal Cancer Family Registry (NCI/NIH grant numbers K.
Carboxyl-ester lipase contains ABO blood group determinants Structural assignment was facilitated by all molecular ions from the MS spectra being examined by MS/MS analysis. In Fig. 9 , fragmentation patterns of the selected ions m/z 1781, 1822, and 1794 (from spectra in Fig. 8, A-C , respectively) are shown. Moreover, three peaks observed in the blood group O secretor and nonsecretor samples had identical m/z values (1519, 1607, and 1968; Fig. 8, A  and C ). The fragmentation pattern showed that they corresponded to glycans of the same composition but ). Major peaks are annotated with their proposed carbohydrate structure, according to the symbolic nomenclature adopted by the CFG  (66). Putative structures are based on monosaccharide composition, data from the MS/MS analysis of all molecular ions and knowledge of the biosynthetic pathways for O-linked glycans.
Antibodies Three different anti-CEL antibodies were used: a polyclonal rabbit antiserum (against peptide sequence 299 -427 of the CEL globular domain) for immunostainings (Sigma-Aldrich, cat. HPA052701), a polyclonal rabbit antiserum for immunoblotting (generated against the truncated CEL form pV562X, recently published in Ref. 13 ), and the mouse mAb As20. 1 for immunoprecipitation (raised against the CEL globular domain, a generous gift from Prof. Olle Hernell, Umeå University, Sweden). The antibody mAb16D10 has been described previously  (27) (28) (29). Anti-Tn antibody was a generous gift from Prof. Ulla Mandel (Copenhagen University, Denmark). Mouse monoclonal anti-A (sc-69951) and goat polyclonal anti-GAPDH antibodies (sc-20357) were from Santa Cruz Biotechnology. Secondary antibodies were HRP-donkey anti-mouse (Invitrogen, cat.
322310), starting with the application of the signal enhancer solutions, then washing vigorously between each step and eventually adding 3,3Ј-diaminobenzidine as substrate for HRP. The sections were counterstained with Mayer's hematoxylin for 1 min and then dehydrated in alcohol, cleared in xylene, and eventually mounted with permanent mounting media. Images were acquired at various magnifications using a MC170HD camera attached to a DM2000 LED microscope (Leica) and processed using Leica Application Suite V4. 8 software. When in situ hybridization was combined with immunofluorescence, the RNAscope 2. 0 HD detection system with alkaline phosphatase (Advanced Cell Diagnostics, cat. 310036) was first used in conjugation with the VECTOR Red fluorescent substrate (Vector Laboratories, cat. SK-5100).
RT-qPCR was performed using TaqMan Universal PCR Master Mix (Applied Biosystems, cat. 4440040) following the manufacturer´s instructions. The PCR conditions were 40 cycles of denaturation at 95 °C for 15 s followed by combined annealing/extension at 60 °C for 1 min. Fluorescent intensity was measured, and results were processed with SDS software, version 2. 2 (Applied Biosystems).
MALDI-TOF MS MALDI-TOF MS data on permethylated samples were acquired in the reflectron positive-ion mode using a 4800 MALDI-TOF/TOF mass spectrometer (Applied Biosystems). The instrument was calibrated externally using the Calmix 4700 calibration standard, samples were dissolved in 10 l of methanol, and 1 l was mixed at a 1:1 ratio (v/v) with 20 mg/ml 3,4-diaminobenzophenone (DMBP) in 75% (v/v) MeCN in water as matrix. The samples were then spotted onto a 384-well sample plate and dried at room temperature. Data were acquired using 4000 Series Explorer instrument control software and were processed using the software Data Explorer (Version 4. 9, Applied Biosystems). MS spectra were assigned and annotated with the help of the GlycoWorkbench software  (65). Further MS/MS analyses of peaks observed in the MS spectra were carried out using the same instrument.
Acknowledgments-We thank Dong Li Lu for help in MALDI-TOF-MS and MS/MS data acquisition. We acknowledge the participation of the Protein-Glycan Interaction Resource of the Consortium for Functional Glycomics (Grant R24 GM098791) for the glycan array experiments, Ulla Mandel and Olle Hernell for providing antibodies, and Elisabeth O. Berge for providing the SK-BR-3 cell line.
This work was supported by  Ph. D. fellowship 911831 (to K. E. J. ) and  Research Grant   912057  (to A. M. ) from  Western Norway Regional Health Authority  ( Helse Vest ). The work was also supported by grants from the  Gade Legacy  (to K. E. J. ),  Stiftelsen Kristian Gerhard Jebsen  (to P. R. N. ),  Research Council of NorwayЈs FRIMEDBIO Program  (to A. M. ), and by  Biotechnology and Biological Sciences Research Council  Grant  BB/K016164/1  (to A. D. and S. M. H. ).
MethodS Data were used from 182 of the 202 patients enrolled in the Jain COS; 10 dropped out and did not give permission to use their data and 10 did not fully complete the exercise questionnaire. The questionnaire used in the screening visits (online supplementary information) between 6 November 2012 and 19 March 2015 asked about the type, level and frequency of all physical activity prior to symptom onset. Self-reported age of first symptoms, first wheelchair use and full-time wheelchair use was taken from screening questionnaires. Exercises were classified based on metabolic equivalents (METs) as moderate (MET 3-6) or vigorous (MET >6) (online supplementary table 1). 3  Participants were coded, based on the maximum frequency of activity reported between ages 10 and 18 years, as 0-no physical activity; 1vigorous activity occasionally/monthly, or moderate activity once weekly; 2moderate activity multiple times per week or vigorous activity once weekly; and 3vigorous activity multiple times per week.